diff --git a/1.1.4/Baldin_V/1.1.4.pdf b/1.1.4/Baldin_V/1.1.4.pdf new file mode 100644 index 00000000..82b026b8 Binary files /dev/null and b/1.1.4/Baldin_V/1.1.4.pdf differ diff --git a/1.1.4/Baldin_V/1.1.4.tex b/1.1.4/Baldin_V/1.1.4.tex new file mode 100644 index 00000000..cc7ceac2 --- /dev/null +++ b/1.1.4/Baldin_V/1.1.4.tex @@ -0,0 +1,209 @@ +\documentclass[a4paper, 12pt]{article} + +\usepackage{cmap} % поиск в PDF l + +\usepackage[utf8]{inputenc} % кодировка исходного текста +\usepackage[T2A]{fontenc} +\usepackage[english,russian]{babel} % локализация и переносы +\usepackage{amsmath, amsfonts, amssymb, amsthm,mathtools, float} +\usepackage{ stmaryrd } + +% Рисунки +\usepackage{graphicx} +\usepackage{wrapfig} + +\usepackage[left=2cm,right=2cm, top=2cm,bottom=2cm,bindingoffset=0cm]{geometry} + +\usepackage{longtable} +\graphicspath{pictures} + +\author{Балдин Виктор Б01-303} +\title{Работа 1.1.4 \\ Измерение интенсивности радиационного фона} + +\begin{document} + + \maketitle + \section{Аннотация} + \textbf{Цель работы:} применение методов обработки экспериментальных данных + для изучения статистических закономерностей + при измерении интенсивности радиационного фона. + \bigskip\\ + \textbf{Оборудование:} счетчик Гейгера-Мюллера (CTC-6), блок питания, компьютер + с интерфейсом связи со счетчиком. + + \section{Теоретические сведения} + В данной работе измеряется число частиц, проходящих через счетчик за 10 секунд, с помощью которого мы можем найти и количество за 40 секунд. Такие времена выбраны для того, чтобы показать, что при большем времени лучше выполняется нормальное распределение измеряемых величин и гистограмма более симметрична, чем при малых временах, когда при обработке лучше воспользоваться законом Пуассона. + + Если случайные события, такие как регистрация частицы счётчиком, однородны во времени и являются независимыми, то результаты их измерений подчиняются распределению Пуассона. Теория вероятности утверждает, что в таком случае: + \begin{equation} + \sigma = \sqrt{n_0} + \end{equation} + + + Для рассмотренной выборки из $n$ измерений относительная ошибка отдельного измерения равна: + \begin{equation} + \varepsilon_{\mbox{\tiny{отд}}} \approx \frac{1}{\sqrt{n_i}} + \end{equation} + При проведении многочисленных опытов за $n_0$ принимается среднее арифметическое всех результатов + $\langle n \rangle$, а стандартное отклонение $\langle n \rangle$ от $n_0$ может быть + вычислено по формуле: + \[ \sigma_{\langle n \rangle} = \frac{1}{N} \sqrt{\sum_{i=1}^N(n_i - \langle n \rangle)^2}, \] где $N$ - количество измерений, $n_i$ - результат $i$-того измерения. Относительная же погрешность составит: \[ \varepsilon_{\langle n \rangle} = \frac{1}{\sqrt{\langle n \rangle N}}. \] + Таким образом, можно по результатам измерений можно построить гистограмму $\omega_n = f(n)$, где $\omega_n + $ -- доля случаев, для которых число срабатывания счетчика за 10 с равно $n$. + + \section{Методика измерений} + \begin{figure}[H] + \centering + \includegraphics[scale = 0.5]{pictures/scheme.png} + \caption{Схема включения датчика} + \end{figure} + + Космические лучи обнаруживают с помощью ионизации, которую они производят, используя счетчик Гейгера-Мюллера. Схема его подключения приведена на рисунке 1. Счетчик представляет собой наполненный газом сосуд с двумя электродами. Частицы космических лчей ионизируют газ, выбивают электроны из стенок сосуда. Те, сталкиваясь с молекулами газа, выбивают из них электроны. Таким образом, получается лавина электронов, вследствие которой через счетчик резко увеличивается сила тока. + + \section{Используемое оборудование} + Счетчик Гейгера-Мюллера (СТС-6), блок питания, компьютер. + + \section{Результаты измерений и обработка данных} + + \begin{enumerate} + \item Включим установку и убедимся в ее работоспособности, проведя демонстрационный + эксперимент. + \item Проведем основной эксперимент. Данные, полученные для количества частиц, + прошедших за 20 с: + \begin{table}[H] + \centering + \caption{Количество срабатываний за 20 с} + \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|} + \hline + № опыта & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline + 0 & 31 & 25 & 24 & 19 & 24 & 19 & 27 & 27 & 22 & 25 \\ \hline + 10 & 23 & 37 & 36 & 25 & 25 & 22 & 28 & 24 & 23 & 29 \\ \hline + 20 & 24 & 21 & 28 & 28 & 24 & 24 & 28 & 25 & 27 & 30 \\ \hline + 30 & 23 & 29 & 26 & 36 & 21 & 35 & 23 & 20 & 34 & 24 \\ \hline + 40 & 22 & 19 & 34 & 22 & 30 & 30 & 20 & 32 & 33 & 20 \\ \hline + 50 & 28 & 28 & 21 & 25 & 35 & 18 & 26 & 22 & 24 & 26 \\ \hline + 60 & 16 & 27 & 26 & 24 & 27 & 19 & 31 & 15 & 28 & 38 \\ \hline + 70 & 24 & 33 & 24 & 29 & 31 & 21 & 15 & 20 & 26 & 19 \\ \hline + 80 & 20 & 18 & 41 & 25 & 22 & 24 & 22 & 21 & 30 & 25 \\ \hline + 90 & 20 & 31 & 31 & 24 & 14 & 19 & 29 & 17 & 14 & 28 \\ \hline + 100 & 25 & 31 & 29 & 29 & 18 & 25 & 18 & 24 & 24 & 35 \\ \hline + 110 & 31 & 21 & 31 & 32 & 34 & 13 & 29 & 30 & 21 & 20 \\ \hline + 120 & 30 & 22 & 20 & 32 & 32 & 29 & 33 & 20 & 31 & 29 \\ \hline + 130 & 26 & 18 & 22 & 29 & 33 & 20 & 33 & 20 & 31 & 29 \\ \hline + 140 & 22 & 25 & 27 & 23 & 18 & 28 & 21 & 23 & 24 & 27 \\ \hline + 150 & 27 & 25 & 30 & 21 & 24 & 23 & 22 & 33 & 21 & 32 \\ \hline + 160 & 28 & 22 & 26 & 20 & 31 & 29 & 30 & 27 & 23 & 26 \\ \hline + 170 & 22 & 24 & 20 & 31 & 38 & 18 & 24 & 28 & 26 & 24 \\ \hline + 180 & 41 & 28 & 27 & 29 & 25 & 23 & 21 & 24 & 26 & 19 \\ \hline + 190 & 27 & 24 & 25 & 34 & 28 & 28 & 19 & 28 & 26 & 21 \\ \hline + \end{tabular} + \end{table} + + \item Переносим данные для $\tau = 10$ с: + \begin{table}[H] + \centering + \caption{Данные для гистограммы для $\tau = 10$ с} + \begin{tabular}{|c|c|c|} + \hline + Число импульсов $n$ & Число случаев & Доля случаев $\omega_n$ \\ \hline + 4 & 4 & 0.0100 \\ \hline + 5 & 1 & 0.0025 \\ \hline + 6 & 9 & 0.0225 \\ \hline + 7 & 20 & 0.0500 \\ \hline + 8 & 19 & 0.0475 \\ \hline + 9 & 25 & 0.0625 \\ \hline + 10 & 39 & 0.0975 \\ \hline + 11 & 33 & 0.0825 \\ \hline + 12 & 41 & 0.1025 \\ \hline + 13 & 43 & 0.1075 \\ \hline + 14 & 47 & 0.1175 \\ \hline + 15 & 36 & 0.0900 \\ \hline + 16 & 23 & 0.0575 \\ \hline + 17 & 11 & 0.0275 \\ \hline + 18 & 27 & 0.0675 \\ \hline + 19 & 6 & 0.0150 \\ \hline + 20 & 6 & 0.0150 \\ \hline + 21 & 3 & 0.0075 \\ \hline + 22 & 2 & 0.0050 \\ \hline + 23 & 4 & 0.0100 \\ \hline + 27 & 1 & 0.0025 \\ \hline + \end{tabular} + \end{table} + + \item Построим гистограмму по таблице 2: + \begin{figure}[H] + \centering + \caption{Гистограмма для $\tau = 10$ с} + \includegraphics[scale = 0.4]{data/hist_10.png} + \end{figure} + + Среднее значение для этой выборки $\overline{n}_1 = 14.1$, стандартное отклонение + $\sigma_{\overline{n}_1} = 6.5$, относительная ошибка $\varepsilon_1 = 0.014$. + + \item Аналогичным образом обработаем данные для $\tau = 40$ с. + \begin{table}[H] + \centering + \caption{Гистограмма для $\tau = 40$ с} + \begin{tabular}{|c|c|c|} + \hline + Число импульсов $n$ & Число случаев & Доля случаев $\omega_n$ \\ \hline + 26 & 1 & 0.0100 \\ \hline + 32 & 0 & 0.0025 \\ \hline + 34 & 2 & 0.0225 \\ \hline + 36 & 5 & 0.0500 \\ \hline + 38 & 5 & 0.0475 \\ \hline + 40 & 6 & 0.0625 \\ \hline + 42 & 10 & 0.0975 \\ \hline + 44 & 8 & 0.0825 \\ \hline + 46 & 10 & 0.0975 \\ \hline + 48 & 11 & 0.1125 \\ \hline + 50 & 12 & 0.1175 \\ \hline + 52 & 9 & 0.0900 \\ \hline + 54 & 6 & 0.0575 \\ \hline + 56 & 7 & 0.0675 \\ \hline + 58 & 1 & 0.0125 \\ \hline + 60 & 2 & 0.0150 \\ \hline + 62 & 2 & 0.0150 \\ \hline + 64 & 1 & 0.0075 \\ \hline + 66 & 2 & 0.0225 \\ \hline + 76 & 1 & 0.0050 \\ \hline + 82 & 1 & 0.0050 \\ \hline + \end{tabular} + \end{table} + + \item Гистограмма по таблице 3: + \begin{figure}[H] + \centering + \caption{Гистограмма для $\tau = 40$ с} + \includegraphics[scale = 0.4]{data/hist_20.png} + \end{figure} + + Среднее значение $\overline{n}_2 = 50.7$, отклонение $\sigma_{\overline{n}_2} = 14.5$. + Относительная ошибка $\varepsilon_2 = 0.015$. + \end{enumerate} + + \section{Обсуждение результатов} + Согласно теории, стандартное отклонение для распределения Пуассона можно + оценить по формуле (1) как $\sigma = \sqrt{\overline{n}}$. Можно составить таблицу для + этих опытов для сравнения результатов с теоретической оценкой: + \begin{table}[H] + \centering + \caption{Стандартное отклонение для 2-х выборок} + \begin{tabular}{|c|c|c|} + \hline + Выборка & $\sigma_{\text{эксп}}$ & $\sigma_{\text{теор}}$ \\ \hline + 10 с & 6.5 & 3.8 \\ \hline + 40 с & 14.5 & 7.1 \\ \hline + \end{tabular} + \end{table} + + Нетрудно видеть, что порядок сходится с теоретической оценкой. Так же видна + тенденция к уменьшению отклонения при увеличении числа измерений, что согласуется + с теорией. Причиной существенного несовпадения значений с теорией могут быть + некоторые радиационные аномалии, а также системная погрешность аппаратуры. + + \section{Вывод} + В результате эксперимента удалось подтвердить характер распределения Пуассона, а также + измерить средние значения. + +\end{document} diff --git a/1.1.4/Baldin_V/data/data.csv b/1.1.4/Baldin_V/data/data.csv new file mode 100644 index 00000000..697bc4f3 --- /dev/null +++ b/1.1.4/Baldin_V/data/data.csv @@ -0,0 +1,21 @@ +№ опыта,1,2,3,4,5,6,7,8,9,10 +0,31,25,24,19,24,19,27,27,22,25 +10,23,37,36,25,25,22,28,24,23,29 +20,24,21,28,28,24,24,28,25,27,30 +30,23,29,26,36,21,35,23,20,34,24 +40,22,19,34,22,30,30,20,32,33,20 +50,28,28,21,25,35,18,26,22,24,26 +60,16,27,26,24,27,19,31,15,28,38 +70,24,33,24,29,31,21,15,20,26,19 +80,20,18,41,25,22,24,22,21,30,25 +90,20,31,31,24,14,19,29,17,14,28 +100,25,31,29,29,18,25,18,24,24,35 +110,31,21,31,32,34,13,29,30,21,20 +120,30,22,20,32,32,29,33,20,31,29 +130,26,18,22,29,33,20,33,20,31,29 +140,22,25,27,23,18,28,21,23,24,27 +150,27,25,30,21,24,23,22,33,21,32 +160,28,22,26,20,31,29,30,27,23,26 +170,22,24,20,31,38,18,24,28,26,24 +180,41,28,27,29,25,23,21,24,26,19 +190,27,24,25,34,28,28,19,28,26,21 diff --git a/1.1.4/Baldin_V/data/hist_10.png b/1.1.4/Baldin_V/data/hist_10.png new file mode 100644 index 00000000..13cc7da1 Binary files /dev/null and b/1.1.4/Baldin_V/data/hist_10.png differ diff --git a/1.1.4/Baldin_V/data/hist_20.png b/1.1.4/Baldin_V/data/hist_20.png new file mode 100644 index 00000000..6d42c738 Binary files /dev/null and b/1.1.4/Baldin_V/data/hist_20.png differ diff --git a/1.1.4/Baldin_V/data/hist_data_10.csv b/1.1.4/Baldin_V/data/hist_data_10.csv new file mode 100644 index 00000000..6e6c492e --- /dev/null +++ b/1.1.4/Baldin_V/data/hist_data_10.csv @@ -0,0 +1,25 @@ +Число импульсов $n$,Число случаев,Доля случаев $\omega_n$ +4,4,0.0100 +5,1,0.0025 +6,9,0.0225 +7,20,0.0500 +8,19,0.0475 +9,25,0.0625 +10,39,0.0975 +11,33,0.0825 +12,41,0.1025 +13,43,0.1075 +14,47,0.1175 +15,36,0.0900 +16,23,0.0575 +17,11,0.0275 +18,27,0.0675 +19,6,0.0150 +20,6,0.0150 +21,3,0.0075 +22,2,0.0050 +23,4,0.0100 +27,1,0.0025 +,, +mean(n),14.1428571428571, +stdev(n),6.47522751944451, diff --git a/1.1.4/Baldin_V/data/hist_data_10.ods b/1.1.4/Baldin_V/data/hist_data_10.ods new file mode 100644 index 00000000..c0e5c73a Binary files /dev/null and b/1.1.4/Baldin_V/data/hist_data_10.ods differ diff --git a/1.1.4/Baldin_V/data/hist_data_20.csv b/1.1.4/Baldin_V/data/hist_data_20.csv new file mode 100644 index 00000000..5816e8fe --- /dev/null +++ b/1.1.4/Baldin_V/data/hist_data_20.csv @@ -0,0 +1,28 @@ +Число импульсов $n$,Число случаев,Доля случаев $\omega_n$ +26,1,0.0100 +32,0,0.0025 +34,2,0.0225 +36,5,0.0500 +38,5,0.0475 +40,6,0.0625 +42,10,0.0975 +44,8,0.0825 +46,10,0.0975 +48,11,0.1125 +50,12,0.1175 +52,9,0.0900 +54,6,0.0575 +56,7,0.0675 +58,1,0.0125 +60,2,0.0150 +62,2,0.0150 +64,1,0.0075 +66,2,0.0225 +76,1,0.0050 +82,1,0.0050 +,, +,, +,, +,, +stdev(n),, +14.5255112195914,, diff --git a/1.1.4/Baldin_V/data/hist_data_20.ods b/1.1.4/Baldin_V/data/hist_data_20.ods new file mode 100644 index 00000000..2f5242e8 Binary files /dev/null and b/1.1.4/Baldin_V/data/hist_data_20.ods differ diff --git a/1.1.4/Baldin_V/pictures/scheme.png b/1.1.4/Baldin_V/pictures/scheme.png new file mode 100644 index 00000000..7d481fa6 Binary files /dev/null and b/1.1.4/Baldin_V/pictures/scheme.png differ diff --git a/1.1.4/pdf/Baldin_V.pdf b/1.1.4/pdf/Baldin_V.pdf new file mode 100644 index 00000000..82b026b8 Binary files /dev/null and b/1.1.4/pdf/Baldin_V.pdf differ diff --git a/1.2.3/Baldin_V/1.2.3 graph.png b/1.2.3/Baldin_V/1.2.3 graph.png new file mode 100644 index 00000000..e569f5c2 Binary files /dev/null and b/1.2.3/Baldin_V/1.2.3 graph.png differ diff --git a/1.2.3/Baldin_V/1.2.3 ustan.png.jpg b/1.2.3/Baldin_V/1.2.3 ustan.png.jpg new file mode 100644 index 00000000..6650e09c Binary files /dev/null and b/1.2.3/Baldin_V/1.2.3 ustan.png.jpg differ diff --git a/1.2.3/Baldin_V/1.2.3.pdf b/1.2.3/Baldin_V/1.2.3.pdf new file mode 100644 index 00000000..ef2132e7 Binary files /dev/null and b/1.2.3/Baldin_V/1.2.3.pdf differ diff --git a/1.2.3/Baldin_V/1.2.3.tex b/1.2.3/Baldin_V/1.2.3.tex new file mode 100644 index 00000000..7d683ecf --- /dev/null +++ b/1.2.3/Baldin_V/1.2.3.tex @@ -0,0 +1,284 @@ +\documentclass[a4, 12pt]{article} +\usepackage[a4paper,top=1.3cm,bottom=2cm,left=1.5cm,right=1.5cm,marginparwidth=0.75cm]{geometry} +\usepackage{setspace} +\usepackage{cmap} +\usepackage{mathtext} +\usepackage[utf8]{inputenc} +\usepackage[english,russian]{babel} +\usepackage[T2A]{fontenc} +\usepackage{multirow} +\usepackage{graphicx} +\usepackage{wrapfig} +\usepackage{tabularx} +\usepackage{float} +\usepackage{longtable} +\usepackage{hyperref} +\hypersetup{colorlinks=true,urlcolor=blue} +\usepackage[rgb]{xcolor} +\usepackage{amsmath,amsfonts,amssymb,amsthm,mathtools} +\usepackage{icomma} +\mathtoolsset{showonlyrefs=true} +\usepackage{euscript} +\usepackage{mathrsfs} + +\DeclareMathOperator{\sgn}{\mathop{sgn}} +\newcommand*{\hm}[1]{#1\nobreak\discretionary{} + {\hbox{$\mathsurround=0pt #1$}}{}} + + +\title{\textbf{Определение моментов инерции твердых тел с помощью трифилярного подвеса. (1.2.3)}} +\author{Балдин Виктор Б01-303} +\date{30 октября 2023} + + +\begin{document} + + \maketitle + + \section{Введение} + + \textbf{Цели работы:} измерение момента инерции тел и сравнение результатов с расчетами по теоретическим формулам; проверка аддитивности моментов инерции и справедливости формулы Гюйгенса-Штейнера.\\ + \textbf{Оборудование:} трифилярный подвес, секундомер, счетчик числа колебаний, набор тел, момент инерции которых надлежит измерить (диск, стержень, полный цилиндр и другие). + \section{Теоретические сведения} + + \par Инерционность при вращении тела относительно оси определяется моментом инерции тела относительно этой оси. Момент инерции твердого тела относительно неподвижной оси вращения вычисляется по формуле: + + \begin{equation} + I = \int r^2 dm + \end{equation} + + Здесь $r$ -- расстояние элемента массы тела $dm$ от оси вращения. Интегрирование проводится по всей массе тела $m$. + + Если пренебречь потерями энергии на трение о воздух и крепление нитей, то уравнение сохранения энергии при колебаниях можно записать следующим образом: + + \begin{equation}\label{moment} + \frac{I \dot{\varphi^2}}{2} + mg(z_0-z) = E + \end{equation} + + Здесь $I$ -- момент инерции платформы вместе с исследуемым телом, $m$ -- масса платформы с телом, $\varphi$ -- угол поворота платформы от положения равновесия системы, $z_0$ -- координата по вертикали центра нижней платформы $O'$ при равновесии ($\varphi = 0$), $z$ -- координата той же точки при некотором угле поворота $\varphi$. Правый член в левой части уравнения -- кинетическая энергия вращения, второй член -- потенциальная энергия в поле тяжести, $E$ -- полная энергия системы (платформы с телом). + + Воспользуемся системой координат $x, y, z$, связанной с верхней платформой, как показано на Рис. \ref{risunok}. Координаты верхнего конца одной из нитей подвеса точки $C$ в этой системе -- $(r, 0, 0)$. Нижний конец данной нити $C'$, находящийся на нижней платформе, при равновесии имеет координаты $(R, 0, z_0)$, а при повороте платформы на угол $\varphi$ эта точка переходит в $C''$ с координатами $(Rcos\varphi, Rsin\varphi, z)$. расстояние между точками $C$ и $C''$ равно длине нити, поэтому, после некоторых преобразований, получаем: + + \begin{center} + \begin{spacing}{1.6} + $ (R\cos\phi - r)^2 + R^2\sin^2\phi + z^2 = L^2 $ + + $ z^2 = L^2 - R^2 - r^2 + 2Rr\cos\phi \approx z^2_{0} - 2Rr(1 - \cos\phi) \approx z^2_{0} - Rr\phi^2 $ + + $ z = \sqrt{z^2_{0} - Rr\phi^2} \approx z_{0} - \frac{Rr\phi^2}{2z_{0}} $ + \end{spacing} + \end{center} + + Подставляя $z$ в уравнение \eqref{moment}, получаем: + + \begin{equation} + \frac{1}{2}I\dot{\varphi^2} + mg \frac{Rr}{2z_0}\varphi^2 = E + \end{equation} + + Дифференцируя по времени и сокращая на $\dot\varphi$, находим уравнение крутильных колебаний системы: + + \begin{equation} + I\ddot\varphi^2 + mg\frac{Rr}{2z_0}\varphi^2 = 0 + \end{equation} + + Производная по времени от $E$ равна нулю, так как потерями на трение, как уже было сказано выше, пренебрегаем. + + Решение этого уравнения имеет вид: + + \begin{equation} + \varphi = \varphi_0 \sin \left(\sqrt{\frac{mgRr}{Iz_0}}t + \theta\right) + \end{equation} + + Здесь амплитуда $\varphi_0$ и фаза $\theta$ колебаний определяются начальными условиями. Период крутильных колебаний нашей системы равен: + + \begin{equation} + T = 2\pi \sqrt{\frac{Iz_0}{mgRr}} + \end{equation} + + Из формулы для периода получаем: + + \begin{equation}\label{momin} + I = \frac{mgRrT^2}{4 \pi^2z_0} = kmT^2 + \end{equation} + + \section {Методика измерений} + + \begin{wrapfigure}{l}{7cm} + \includegraphics[width=0.95\linewidth]{1.2.3 ustan.png} + \caption{Физический маятник}\label{risunok} + \end{wrapfigure} + + Для наших целей удобно использовать устройство, показанное на Рис. \ref{risunok} и называемое трифилярным подвесом. Оно состоит из укрепленной на некоторой высоте неподвижной платформы $P$ и подвешенной к ней на трех симметрично расположенных нитях $AA'$, $BB'$ и $CC'$, вращающейся платформы $P'$. + + Чтобы не вызывать дополнительных раскачиваний, лучше поворачивать верхнюю платформу, укрепленную на неподвижной оси. После поворота верхняя платформа остается неподвижной в течение всего процесса колебаний. После того, как нижняя платформа $P'$ оказывается повернутой на угол $\varphi$ относительно верхней платформы $P$ возникает момент сил, стремящийся вернуть нижнюю платформу в положение равновесия, при котором относительный поворот платформ отсутствует. В результате платформа совершает крутильные колебания. + + \noindent где $k = \frac{gRr}{4\pi^2z_0}$ -- величина, постоянная для данной установки. + + \section{Оборудование} + Трифилярный подвес, секундомер, счетчик числа колебаний, набор тел, момент инерции которых надлежит измерить (диск, стержень, полный цилиндр и другие). + + \section{Результаты измерений и обработка данных} + \begin{enumerate} + \item Проверим исправность установки. + \item Измерим параметры установки: + $$ z_0 = (213.6\pm0.5) \;\text{см} $$ + $$ R = (114.6\pm0.5) \;\text{мм} $$ + $$ r = (30.2\pm0.3) \;\text{мм} $$ + $$ m = (1066.8\pm0.5) \;\text{г} $$ + \item Вычислим $k$: + $$ k = \frac{gRr}{4\pi^2z_0} = 4.43\cdot10^{-4} \;\text{кг}\cdot\text{м}$$ + $$ \varepsilon_k = \varepsilon_R + \varepsilon_r + \varepsilon_{z_0} = 0.02 $$ + $$ k = (4.43\pm0.09)\cdot10^{-4} \;\text{кг}\cdot\text{м}$$ + \item Вычислим момент инерции пустой платформы. + $$ I = \frac{mR^2}{2} = 7.264\cdot10^{-3} \;\text{кг}\cdot\text{м} $$ + $$ \varepsilon_I = 2\varepsilon_R + \varepsilon_m = 0.01 $$ + $$ I = (7.264\pm0.073)\cdot10^{-3}\;\text{кг}\cdot\text{м} $$ + \item Перейдем к измерению моментов инерции данных нам тел. + Для кольца получим: + \begin{table}[H] + \centering + \begin{tabular}{|c|c|c|c|} + \hline + $t$, c & $T$, c & $I_t+I$, $10^{-3}$ кг$\cdot$м$^2$ & $I_t$, $10^{-3}$, кг$\cdot$м$^2$ \\ \hline + 126.08 & 4.203 & 12.97 & 5.15 \\ \hline + 125.94 & 4.198 & 12.94 & 5.12 \\ \hline + 126.55 & 4.218 & 13.06 & 5.25 \\ \hline + \end{tabular} + \end{table} + По итогу получим $I_\text{кол} = (5.17\pm0.08)\cdot10^{-3}$ кг$\cdot$м$^2$. + \item + Сделаем все то же самое для диска с параметрами + \begin{align*} + m_{диск} &= (580.6 \pm 0.5) \;\text{г} \\ + r_{диск} &= (5.75 \pm 0.01) \;\text{см} + \end{align*} + + \begin{table}[h!] + \begin{center} + \begin{tabular}{|l|r|r|r|} + \hline + No & N & t, с & T, с \\ + \hline + 1 & 10 & 39.254 & 3.9254 \\ + 2 & 10 & 39.221 & 3.9221 \\ + 3 & 10 & 39.203 & 3.9203 \\ + 4 & 10 & 39.189 & 3.9189 \\ + \hline + \end{tabular} + \end{center} + \end{table} + Из этих данных получаем + + + + Теоретически получаем + \[I^{теор}_{диск} = m_{диск}r_{диск}^2 = (1.920 \pm 0.007)\cdot10^{-3}\;\text{кг}\cdot\text{м}^2\] + Как видим в пределах погрешности теория соответствует эксперименту. + + \paragraph{} + Когда оба тела на платформе. + + \begin{table}[h!] + \begin{center} + \begin{tabular}{|l|r|r|r|} + \hline + No & N & t, с & T, с \\ + \hline + 1 & 10 & 39.750 & 3.9750 \\ + 2 & 10 & 39.873 & 3.9873 \\ + 3 & 10 & 39.964 & 3.9964 \\ + 4 & 10 & 39.773 & 3.9773 \\ + \hline + \end{tabular} + \end{center} + \end{table} + + \begin{align*} + T &= (3.984 \pm 0.006)\;\text{c} \\ + I_\text{пф+общ}&=(16.4 \pm 0.3)\cdot10^{-3}\;\text{кг}\cdot\text{м}^2 \\ + I_\text{общ}&=(8.7 \pm 0.4)\cdot10^{-3}\;\text{кг}\cdot\text{м}^2 \\ + I_\text{диск} + I_\text{кол} = (8.8 \pm 0.6)\cdot10^{-3}\;\text{кг}\cdot\text{м}^2 + \end{align*} + \item Теперь исследуем зависимость момента инерции двух полукругов от расстояния между ними: + \begin{figure}[H] + \includegraphics[width=0.25\textwidth]{position} + \caption{Схема расположения грузов на платформе трифилярного подвеса.} + \label{ris:position} + \end{figure} + + Перейдем к определению зависимости момента инерции системы двух тел от их взаимного расположения. Для этого, располагая грузы как показано на рис. \ref{ris:position}, получим зависимость периода от расстояния. Затем, определим зависимость $ I(h^{2}) $ + + Полученные результаты измерений занесем в таблицы \ref{tab:period},\ref{tab:moment} соответсвенно. Основывыаясь на результатах таблицы \ref{tab:moment}, построим график зависимости $ I(h^{2}) $. (Рис. \ref{ris:grafik}) + + + \begin{table}[H] + \begin{center} + \begin{tabular}{| l | l | l || l | l | l |} + \hline + № изм. & T, с & h, сm & № изм. & T, с & h, сm \\ \hline + 1 & 3,122 & 0 & 8 & 3,399 & 3,5 \\ \hline + 2 & 3,127 & 0,5 & 9 & 3,472 & 4,0 \\ \hline + 3 & 3,146 & 1,0 & 10 & 3,568 & 4,5 \\ \hline + 4 & 3,167 & 1,5 & 11 & 3,662 & 5,0 \\ \hline + 5 & 3,207 & 2,0 & 12 & 3,753 & 5,5 \\ \hline + 6 & 3,255 & 2,5 & 13 & 3,886 & 6,0 \\ \hline + 7 & 3,325 & 3,0 & 14 & 4,002 & 6,5 \\ \hline + \end{tabular} + \caption{Зависимость Периода колебаний от расстояния между дисками.} + \label{tab:period} + \end{center} + \end{table} + + \begin{table}[H] + \begin{center} + \begin{tabular}{| l | l | l || l | l | l |} + \hline + № изм. & I, $kgm^2 * 10^{-3}$ & h, cm & № изм. & I, $kgm^2 * 10^{-3}$ & h, сm \\ \hline + 1 & 1,678 & 0 & 8 & 1,827 & 3,5 \\ \hline + 2 & 1,681 & 0,5 & 9 & 1,866 & 4,0 \\ \hline + 3 & 1,691 & 1,0 & 10 & 1,918 & 4,5 \\ \hline + 4 & 1,702 & 1,5 & 11 & 1,968 & 5,0 \\ \hline + 5 & 1,724 & 2,0 & 12 & 2,017 & 5,5 \\ \hline + 6 & 1,750 & 2,5 & 13 & 2,089 & 6,0 \\ \hline + 7 & 1,787 & 3,0 & 14 & 2,151 & 6,5 \\ \hline + \end{tabular} + \caption{Зависимость Момента инерции от расстояния между дисками.} + \label{tab:moment} + \end{center} + \end{table} + + \begin{figure}[H] + + + \begin{center} + \includegraphics[width=0.9\textwidth]{1.2.3 graph.png} + \caption{График зависимости $ I(h^2) $} + \label{ris:grafik} + \end{center} + \end{figure} + + Видно, что данная зависимость весьма хорошо аппроксимируется прямой, что согласуется + с теоретическими данными. + + \end{enumerate} + + \section{Обсуждение результатов} + \begin{enumerate} + \item В результате работы были измерены моменты инерции предоставленных тел. + \item Были посчитаны теоретические моменты инерции и проведено сравнение их + с экспериментальными результатами. Обнаружено совпадение в пределах погрешности. + Таким образом, доказана состоятельность метода трифилярного подвеса для + экспериментального определения моментов инерции различных тел. + \item Получена экспериментальная зависимость момента инерции системы из двух полукругов + от расстояния между их центрами инерции. Линеаризованная зависимость $I(h^2)$ действительно + неплохо аппроксимируется прямой, что очень хорошо согласуется с теорией. + \item Проверена аддитивность момента инерции системы нескольких тел. + \end{enumerate} + \section{Вывод} + Их проделанной работы можно заключить, что установка на основе трифилярного подвеса хорошо + подходит для определения моментов инерции тел. Зачастую аналитическое вычисление моментов + инерции может представлять определенные трудности, особенно в случае сложной и неправильной + формы тела. В таких случаях приходится прибегать к экспериментальным способам, а значит, + и считаться с их погрешностями. Как показала практика, опробованный в данной работе метод + имеет неплохую точность, следовательно, его можно рекомендовать для использования в этих целях. +\end{document} diff --git a/1.2.3/Baldin_V/Grafik.jpg b/1.2.3/Baldin_V/Grafik.jpg new file mode 100644 index 00000000..0f75b644 Binary files /dev/null and b/1.2.3/Baldin_V/Grafik.jpg differ diff --git a/1.2.3/Baldin_V/data/data.ods b/1.2.3/Baldin_V/data/data.ods new file mode 100644 index 00000000..ca42f5c4 Binary files /dev/null and b/1.2.3/Baldin_V/data/data.ods differ diff --git a/1.2.3/Baldin_V/data/disk.csv b/1.2.3/Baldin_V/data/disk.csv new file mode 100644 index 00000000..d6f2dac4 --- /dev/null +++ b/1.2.3/Baldin_V/data/disk.csv @@ -0,0 +1,4 @@ +"$t$, c","$T$, c","$I_t+I$, $10^{-3}$ кг$\cdot$м$^2$","$I_t$, $10^{-3}$, кг$\cdot$м$^2$" +126.08,4.203,12.97,5.15 +125.94,4.198,12.94,5.12 +126.55,4.218,13.06,5.25 diff --git a/1.2.3/Baldin_V/data/ring.csv b/1.2.3/Baldin_V/data/ring.csv new file mode 100644 index 00000000..70d2ffbd --- /dev/null +++ b/1.2.3/Baldin_V/data/ring.csv @@ -0,0 +1,14 @@ +"$t$, c","$T$, c","$I+I_t$, $10^{-3}$, кг$\cdot$м$^2$","$I_t$, $10^{-3}$ кг$\cdot$м$^2$" +Кольцо,,, +4.203,126.08,11.7366695253333,4.47266952533333 +4.198,125.94,11.710619058,4.446619058 +4.218,126.55,11.8243363458333,4.56033634583333 +Цилиндр,,, +,,, +,,, +,,, +Брусок,,, +,,, +,,, +,,, +Полукруги,,, diff --git a/1.2.3/Baldin_V/position.jpg b/1.2.3/Baldin_V/position.jpg new file mode 100644 index 00000000..6584d928 Binary files /dev/null and b/1.2.3/Baldin_V/position.jpg differ diff --git a/1.2.3/Varlamov_A/Report_lab_work_1.2.3.pdf b/1.2.3/Varlamov_A/Report_lab_work_1.2.3.pdf new file mode 100644 index 00000000..adad134e Binary files /dev/null and b/1.2.3/Varlamov_A/Report_lab_work_1.2.3.pdf differ diff --git a/1.2.3/pdf/Baldin_V.pdf b/1.2.3/pdf/Baldin_V.pdf new file mode 100644 index 00000000..ef2132e7 Binary files /dev/null and b/1.2.3/pdf/Baldin_V.pdf differ diff --git a/1.2.4/Baldin_V/1.2.4/1_2_4.pdf b/1.2.4/Baldin_V/1.2.4/1_2_4.pdf new file mode 100644 index 00000000..dda932ca Binary files /dev/null and b/1.2.4/Baldin_V/1.2.4/1_2_4.pdf differ diff --git a/1.2.4/Baldin_V/1.2.4/1_2_4.tex b/1.2.4/Baldin_V/1.2.4/1_2_4.tex new file mode 100644 index 00000000..5490333f --- /dev/null +++ b/1.2.4/Baldin_V/1.2.4/1_2_4.tex @@ -0,0 +1,256 @@ +\documentclass[a4paper, 12pt]{article} + +\usepackage{cmap} % поиск в PDF l +\usepackage[T2A]{fontenc} % кодировка + +\usepackage[utf8]{inputenc} % кодировка исходного текста +\usepackage[english,russian]{babel} % локализация и переносы +\usepackage{amsmath, amsfonts, amssymb, amsthm, mathtools, float} +\usepackage{ stmaryrd } + +% Рисунки +\usepackage{graphicx} +\usepackage{wrapfig} + +\usepackage[left=2cm,right=2cm, top=2cm,bottom=2cm,bindingoffset=0cm]{geometry} + +\usepackage{longtable} + +\graphicspath{pictures} + +\author{Балдин Виктор} +\title{Работа 1.2.4 \\ Определение главных моментов инерции твердых тел с помощью крутильных колебаний} + +\begin{document} + + \maketitle + \section{Аннотация} + \textbf{Цель работы:} измерить периоды крутильных колебаний рамки при различных положениях закрепленного + в ней тела, проверить теоретическую зависимость между периодами крутильных колебаний тела + относительно различных осей, определить моменты инерции относительно нескольких осей для каждого тела, + по ним найти главные моменты инерции тела и построить эллипсоид инерции. + \bigskip\\ + \textbf{Оборудование:} установка для получения крутильных колебаний, набор исследуемых твердых тел, секундомер. + + \section{Теоретические сведения} + Инерционные свойства твердого тела при вращении определяется + пространственным распределением. Оно характеризуется тензором инерции тела. Тензор инерции твердого тела + является симметричным тензором 2-ого ранга $J\in T_{2}^{0}(V)$ и имеет 6 независимых компонент, + которые в прямоугольной декартовой системе координат выражаются как: + \begin{equation} + I_{ij}=\int (\delta _{ij}r^{2}-r_{i}r_{j}) \ dm =I_{ji}, + \end{equation} + где $r$ — расстояния от точек до центра, относительно которого вычисляется тензор инерции, + а $r_{i}$ — координатные компоненты соответствующих отрезков, $i$ и $j$ — номера координат (от 1 до 3).\\ + Если для какой либо системы координат все 6 компонент известны, то момент инерции тела относительно + произвольной оси $l$, проходящей через начало координат может быть вычислен по формуле: + \begin{equation} + I_{l}=n^{j}n^{i}I_{ij}=\vec{n}^{T} I \vec{n} + \end{equation} + где $\vec{n}$ - единичный вектор-столбец который задает направление оси, $I$ - тензор инерции.\\ + А момент импульса $\vec {L}$ и вращательная энергия тела $E_{\text{вращ}}$ тогда будут выражаться как: + \begin{equation} + E_{\text{вращ}}={1 \over 2}\ {\vec {\omega }}^{\,T} I {\vec {\omega }} ={1 \over 2}\sum _{{ij}}\omega^{i}J_{{ij}}\omega^{j} + \end{equation} + \begin{equation} + {\vec {L}}=I {\vec {\omega }}, \ \ \ \ L_{i}=\sum _{j}I_{{ij}}\omega^{j} + \end{equation} + Отложим вдоль оси $l$ из начала координат радиус-вектор $r$ + равный по длине $1/\sqrt{I_{l}}$. Проведем множество таких отрезков, соответствующих различным направлениям оси $l$. + Геометрическое место концов указанных отрезков, является поверхность второго порядка - эллипсоид. Этот эллипсоид принято называть + эллипсоидом инерции. Он жестко связан с телом для которого он построен. Знание эллипсоида инерции позволяет найти момент инерции тела + относительно любой оси, проходящей через центр эллипсоида. Длина отрезка $r$ будет определять момент инерции тела относительно оси $l$: + \begin{equation} + I_{l} = \frac{1}{r^2} + \end{equation} + \begin{figure}[H] + \centering + \includegraphics[scale = 0.5]{pictures/ellipsoid.png} + \caption{Эллипсоиды вращения для разных тел} + \end{figure} + + Как и всякий симметричный тензор второго ранга может быть диагонализован некоторой заменой координат. + Пусть система координат, в которой он диагонализован имеет оси $Ox,Oy,Oz$, тогда эти оси совпадают с главными осями тела. + Полученные диагональные элементы $I_{x}, I_{y}, I_{z}$ называются главными моментами инерции тела, а уравнение эллипсоида + инерции в этих координатах примет вид: + \begin{equation} + I_{x}r^{2}_{x}+I_{y}r^{2}_{y}+I_{z}r^{2}_{z} = 1 + \end{equation} + Крутильные колебания рамки с телом описываются уравнением: + \begin{equation} + (I + I_p)\ddot{\varphi} + f \varphi = 0 + \end{equation} + Здесь $I$ и $I_{p}$ - моменты инерции тела и рамки относительно + оси вращения, $\varphi$ - угол поворота рамки, меняющийся со + временем $t$, $f$ - модуль кручения проволоки. Отсюда период этих колебаний: + \begin{equation} + T = 2\pi\sqrt{\frac{I+I_{p}}{f}} + \end{equation} + На рисунке показано, как проходят оси вращения в параллелепипеде. + Оси $AA'$, $BB'$ и $CC'$ являются главными. Моменты инерции относительно + этих осей обозначим соответственно $I_{x}, I_{y}, I_{z}$.\\ + + \begin{figure}[H] + \begin{center} + \includegraphics[scale=0.5]{pictures/kyb.png} + \caption{Оси вращения прямоугольного параллелепипеда} + \label{graphic1} + \end{center} + \end{figure} + + Момент инерции $I_{D}$ при вращении относительно диагонали $DD'$ выражается + через главные моменты с помощью формулы: + \begin{equation} + I_{d}=I_{x}\frac{a^2}{d^2}+I_{y}\frac{b^2}{d^2}+I_{z}\frac{c^2}{d^2} + \end{equation} + Используя связь момента инерции с периодом крутильных колебаний + получаем соотношение между периодами колебаний относительно осей $DD'$, $EE'$, + $MM'$ и $PP'$ с периодами крутильных колебаний относительно главных осей. + \begin{equation} + \begin{cases} + (b^2+c^2)T^2_{E}=b^2 T^2_{y}+c^2 T^2_{z} \\ + (a^2+c^2)T^2_{P}=a^2 T^2_{x}+c^2 T^2_{z} \\ + (a^2+b^2)T^2_{M}=a^2 T^2_{x}+b^2 T^2_{y} + \end{cases} + \end{equation} + + Эти соотношения также необходимо проверить экспериментально. + + \section{Методика измерений} + В данной работе используется установка для измерения крутильных колебаний, приведенная + на рисунке 3. Рамка 1 жестко соединена с проволокой 2, закрепленной вертикально в специальных + зажимах 3, позволяющих сообщить начальное закручивание для возбуждения крутильных колебаний + вокруг вертикальной оси. + \begin{figure}[H] + \centering + \includegraphics[scale = 0.5]{pictures/stand.png} + \caption{Схема установки} + \end{figure} + +% +% + \section{Используемое оборудование} + Установка для получения крутильных колебаний, набор исследуемых твердых тел, секундомер. + +% + \section{Результаты измерений и обработка данных} + \begin{enumerate} + \item Измерим период сначала для ненагруженной рамки (здесь и далее + период измеряем $N = 15$ раз, погрешность секундомера считаем приблизительно + равной $\sigma_t^{\text{ сист }} = 0.20$ с): + \begin{table}[H] + \centering + \caption{Колебания рамки} + \begin{tabular}{|c|c|c|c|c|c|} + \hline + $t$, c & 38.4 & 38.6 & 38.6 & 38.6 & 38.5 \\ \hline + $T$, c & 2.56 & 2.57 & 2.57 & 2.57 & 2.57 \\ \hline + \end{tabular} + \end{table} + + Отсюда средний период + $$ \overline {T}_{p} = \frac{1}{N}\sum_{i} T_i = 2.57\; \text{с}, $$ + Среднеквадратичное отклонение + $$ \sigma_T^{\text{ случ }} = \sqrt{\frac{ \sum_{i} (T_i - \overline {T})^2}{N} } = 0.01\; \text{с} $$ + % + Общая погрешность таким образом равна: + $$ \sigma_T = \frac{\sigma_t^{\text{ сист }}}{N} + \sigma_T^{\text{ случ }} = 0.03\; \text{с}$$ + + \item Измерим размеры и массу цилиндра: + \begin{table}[H] + \centering + \caption{Измерения размеров цилиндра} + \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|} + \hline + № & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & Среднее & $\sigma$ \\ \hline + $h$, мм & 49.1 & 49.5 & 49.3 & 49.3 & 49.2 & 49.2 & 49.2 & 49.4 & 49.2 & 49.2 & 49.3 & 0.2 \\ \hline + $d$, мм & 88.3 & 88.1 & 88.1 & 88.1 & 88.1 & 88.1 & 88.1 & 88.1 & 88.1 & 88.0 & 88.1 & 0.2 \\ \hline + \end{tabular} + \end{table} + + Масса $m_{\text{цил}} = 2.264\pm0.001$ кг. + Отсюда момент инерции + $$I_{\text{цил}} = \frac{1}{8}md^2 = 2197\; \text{кг} \cdot \text{мм} ^2, $$ + $$ \sigma_I = I\sqrt{\left( \frac{\sigma_m}{m} \right) ^ 2 + 2\left( \frac{\sigma_d} + {\overline{d}} \right) ^ 2} = 8\; \text{кг} \cdot \text{мм} ^2 $$ + + \item Измерим период колебаний цилиндра + \begin{table}[H] + \centering + \caption{Колебания цилиндра} + \begin{tabular}{|cccccc|} + \hline + \multicolumn{6}{|c|}{Главная ось} \\ \hline + \multicolumn{1}{|c|}{$t$, c} & \multicolumn{1}{c|}{47.9} & \multicolumn{1}{c|}{47.6} & \multicolumn{1}{c|}{47.2} & \multicolumn{1}{c|}{47.4} & 47.6 \\ \hline + \multicolumn{1}{|c|}{$T$, c} & \multicolumn{1}{c|}{3.19} & \multicolumn{1}{c|}{3.17} & \multicolumn{1}{c|}{3.15} & \multicolumn{1}{c|}{3.16} & 3.17 \\ \hline + \multicolumn{6}{|c|}{Боковая ось} \\ \hline + \multicolumn{1}{|c|}{$t$, c} & \multicolumn{1}{c|}{45.1} & \multicolumn{1}{c|}{45.0} & \multicolumn{1}{c|}{45.3} & \multicolumn{1}{c|}{45.0} & 44.9 \\ \hline + \multicolumn{1}{|c|}{$T$, c} & \multicolumn{1}{c|}{3.01} & \multicolumn{1}{c|}{3.00} & \multicolumn{1}{c|}{3.02} & \multicolumn{1}{c|}{3.00} & 2.99 \\ \hline + \end{tabular} + \end{table} + + $$ \overline{T}_{z}^{\text{цил}} = 3.17 \; \text{с}, \\\ \overline{T}_{x}^{\text{цил}} = 3.00\; \text{с} $$ + + \item Найдем момент инерции рамки через формулу (8): + $$ \frac{T_p}{T_{z}^{\text{цил}}} = \sqrt{\frac{I_p}{I_p + I_{\text{цил}}}}, $$ + $$ I_p = I_{\text{цил}} \frac{T_p^2}{(T_z^{\text{цил}})^2 - T_p^2} = 4213\; \text{кг} \cdot \text{мм} ^2 $$ + $$ \varepsilon_{I_p} = \frac{\sigma_I}{I} + 4\varepsilon_T = 0.06 $$ + + \item Вычислим величины $1/\sqrt{T^2 - T_p^2}$ для осей $x$ и $z$. + $$ \frac{1}{\sqrt{T_x^2 - T_p^2}} = 0.64 \;\text{c} ^{-2} $$ + $$ \frac{2}{\sqrt{T_z^2 - T_p^2}} = 0.54 \;\text{c} ^{-2}$$ + + \item Нарисуем сечения эллипсоида инерции плоскостью $XOZ$ для цилиндра: + \begin{figure} + \centering + \caption{Сечение плоскостью $AA'M$} + \includegraphics[scale = 0.8]{pictures/graph.png} + \end{figure} + + + \item Повторим все те же действия теперь для куба: + \begin{table}[H] + \centering + \caption{Измерения для куба} + \begin{tabular}{|ccccccccccccc|} + \hline + \multicolumn{13}{|c|}{Измерения длины} \\ \hline + \multicolumn{1}{|c|}{№} & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{3} & \multicolumn{1}{c|}{4} & \multicolumn{1}{c|}{5} & \multicolumn{1}{c|}{6} & \multicolumn{1}{c|}{7} & \multicolumn{1}{c|}{8} & \multicolumn{1}{c|}{9} & \multicolumn{1}{c|}{10} & \multicolumn{1}{c|}{$\overline{a}$} & $\sigma_{a}$ \\ \hline + \multicolumn{1}{|c|}{$a$, мм} & \multicolumn{1}{c|}{92.7} & \multicolumn{1}{c|}{92.8} & \multicolumn{1}{c|}{92.8} & \multicolumn{1}{c|}{92.6} & \multicolumn{1}{c|}{92.6} & \multicolumn{1}{c|}{92.6} & \multicolumn{1}{c|}{92.6} & \multicolumn{1}{c|}{92.6} & \multicolumn{1}{c|}{92.7} & \multicolumn{1}{c|}{92.8} & \multicolumn{1}{c|}{92.7} & 0.1 \\ \hline + \multicolumn{13}{|c|}{Ось $AA’$} \\ \hline + \multicolumn{1}{|c|}{№} & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{3} & \multicolumn{1}{c|}{4} & \multicolumn{1}{c|}{5} & \multicolumn{1}{c|}{Среднее} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \\ \hline + \multicolumn{1}{|c|}{$t$, с} & \multicolumn{1}{c|}{45.4} & \multicolumn{1}{c|}{45.4} & \multicolumn{1}{c|}{45.3} & \multicolumn{1}{c|}{45.3} & \multicolumn{1}{c|}{45.2} & \multicolumn{1}{c|}{45.3} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \\ \hline + \multicolumn{1}{|c|}{$T$, c} & \multicolumn{1}{c|}{3.03} & \multicolumn{1}{c|}{3.03} & \multicolumn{1}{c|}{3.02} & \multicolumn{1}{c|}{3.02} & \multicolumn{1}{c|}{3.01} & \multicolumn{1}{c|}{3.02} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \\ \hline + \multicolumn{13}{|c|}{Ось $MM’$} \\ \hline + \multicolumn{1}{|c|}{№} & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{3} & \multicolumn{1}{c|}{4} & \multicolumn{1}{c|}{5} & \multicolumn{1}{c|}{Среднее} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \\ \hline + \multicolumn{1}{|c|}{$t$, c} & \multicolumn{1}{c|}{45.6} & \multicolumn{1}{c|}{45.6} & \multicolumn{1}{c|}{45.5} & \multicolumn{1}{c|}{45.3} & \multicolumn{1}{c|}{45.5} & \multicolumn{1}{c|}{45.5} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \\ \hline + \multicolumn{1}{|c|}{$T$, c} & \multicolumn{1}{c|}{3.04} & \multicolumn{1}{c|}{3.04} & \multicolumn{1}{c|}{3.03} & \multicolumn{1}{c|}{3.02} & \multicolumn{1}{c|}{3.03} & \multicolumn{1}{c|}{3.03} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \\ \hline + \multicolumn{13}{|c|}{Ось $DD’$} \\ \hline + \multicolumn{1}{|c|}{№} & \multicolumn{1}{c|}{1} & \multicolumn{1}{c|}{2} & \multicolumn{1}{c|}{3} & \multicolumn{1}{c|}{4} & \multicolumn{1}{c|}{5} & \multicolumn{1}{c|}{Среднее} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \\ \hline + \multicolumn{1}{|c|}{$t$, c} & \multicolumn{1}{c|}{45.5} & \multicolumn{1}{c|}{45.7} & \multicolumn{1}{c|}{45.4} & \multicolumn{1}{c|}{45.5} & \multicolumn{1}{c|}{45.5} & \multicolumn{1}{c|}{45.5} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \\ \hline + \multicolumn{1}{|c|}{$T$, c} & \multicolumn{1}{c|}{3.03} & \multicolumn{1}{c|}{3.05} & \multicolumn{1}{c|}{3.03} & \multicolumn{1}{c|}{3.03} & \multicolumn{1}{c|}{3.03} & \multicolumn{1}{c|}{3.03} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \\ \hline + \end{tabular} + \end{table} + + Вычислим моменты инерции: + $$ I_{AA'} = I_p \frac{T_{AA'}^2 - T_p^2}{T_p^2} = 1605\; \text{кг} \cdot \text{мм} ^2, $$ + $$ I_{MM'} = I_p \frac{T_{MM'}^2 - T_p^2}{T_p^2} = 1643\; \text{кг} \cdot \text{мм} ^2, $$ + $$ I_{DD'} = I_p \frac{T_{DD'}^2 - T_p^2}{T_p^2} = 1643\; \text{кг} \cdot \text{мм} ^2 $$ + Так как они примерно одинаковые, можно рассчитать погрешность лишь для одного случая: + $$ \varepsilon_I = \varepsilon_{I_p} + 4\varepsilon_{T} = 0.12 $$ + + + \end{enumerate} + + + \section{Обсуждение результатов} + В результате этой работы мы убедились в выполнении теоретических + соотношений. Построили эллипсоид инерции для цилиндра, а также убедились в + равенстве главных моментов инерции для куба, то есть его эллипсоидом инерции + действительно является сфера. + \section{Вывод} + Использование установки дает некоторую погрешность, но ее + точности вполне достаточно для получения результатов. По виду эллипсоида инерции были вычислены соотношения между моментами + инерции тела относительно главных осей, + которые совпадают с теоретическим расчетом в пределах погрешности. +\end{document} diff --git a/1.2.4/Baldin_V/1.2.4/data/cylinder.csv b/1.2.4/Baldin_V/1.2.4/data/cylinder.csv new file mode 100644 index 00000000..00ff8406 --- /dev/null +++ b/1.2.4/Baldin_V/1.2.4/data/cylinder.csv @@ -0,0 +1,6 @@ +Главная ось,,,,,,mean +"$t$, c",47.9,47.6,47.2,47.4,47.6, +"$T$, c",3.19,3.17,3.15,3.16,3.17,3.17 +Боковая ось,,,,,, +"$t$, c",45.1,45.0,45.3,45.0,44.9, +"$T$, c",3.01,3.00,3.02,3.00,2.99,3.00 diff --git a/1.2.4/Baldin_V/1.2.4/data/data.ods b/1.2.4/Baldin_V/1.2.4/data/data.ods new file mode 100644 index 00000000..5fd0154e Binary files /dev/null and b/1.2.4/Baldin_V/1.2.4/data/data.ods differ diff --git a/1.2.4/Baldin_V/1.2.4/data/kyb.csv b/1.2.4/Baldin_V/1.2.4/data/kyb.csv new file mode 100644 index 00000000..cf113214 --- /dev/null +++ b/1.2.4/Baldin_V/1.2.4/data/kyb.csv @@ -0,0 +1,15 @@ +Измерения длины,,,,,,,,,,,, +№,1,2,3,4,5,6,7,8,9,10,$\overline{a}$,$\sigma_{a}$ +"$a$, мм",92.7,92.8,92.8,92.6,92.6,92.6,92.6,92.6,92.7,92.8,92.7,0.1 +Ось $AA’$,,,,,,,,,,,, +№,1,2,3,4,5,Среднее,,,,,, +"$t$, с",45.4,45.4,45.3,45.3,45.2,45.3,,,,,, +"$T$, c",3.03,3.03,3.02,3.02,3.01,3.02,,,,,, +Ось $MM’$,,,,,,,,,,,, +№,1,2,3,4,5,Среднее,,,,,, +"$t$, c",45.6,45.6,45.5,45.3,45.5,45.5,,,,,, +"$T$, c",3.04,3.04,3.03,3.02,3.03,3.03,,,,,, +Ось $DD’$,,,,,,,,,,,, +№,1,2,3,4,5,Среднее,,,,,, +"$t$, c",45.5,45.7,45.4,45.5,45.5,45.5,,,,,, +"$T$, c",3.03,3.05,3.03,3.03,3.03,3.03,,,,,, diff --git a/1.2.4/Baldin_V/1.2.4/data/kyb.csv.ods b/1.2.4/Baldin_V/1.2.4/data/kyb.csv.ods new file mode 100644 index 00000000..520635ff Binary files /dev/null and b/1.2.4/Baldin_V/1.2.4/data/kyb.csv.ods differ diff --git a/1.2.4/Baldin_V/1.2.4/graph1.py b/1.2.4/Baldin_V/1.2.4/graph1.py new file mode 100644 index 00000000..0fbf80c2 --- /dev/null +++ b/1.2.4/Baldin_V/1.2.4/graph1.py @@ -0,0 +1,17 @@ +import numpy as np +from matplotlib import pyplot as plt +from math import pi + +u=0 #x-position of the center +v=0 #y-position of the center +a=0.64 #radius on the x-axis +b=0.54 #radius on the y-axis + +t = np.linspace(0, 2*pi, 200) +plt.plot( u+a*np.cos(t) , v+b*np.sin(t) , label=r'$Апроксимация$') +plt.xlabel(r'$OX$', fontsize=10) +plt.ylabel(r'$OZ$', fontsize=10) +plt.legend(loc='upper right', fontsize=7) +plt.grid(color='lightgray',linestyle='--', linewidth = 0.5) +plt.plot([0.64, -0.64, 0, 0], [0, 0, -0.54, 0.54], 'ro') +plt.show() diff --git a/1.2.4/Baldin_V/1.2.4/pictures/ellipsoid.png b/1.2.4/Baldin_V/1.2.4/pictures/ellipsoid.png new file mode 100644 index 00000000..c5f4584a Binary files /dev/null and b/1.2.4/Baldin_V/1.2.4/pictures/ellipsoid.png differ diff --git a/1.2.4/Baldin_V/1.2.4/pictures/graph.png b/1.2.4/Baldin_V/1.2.4/pictures/graph.png new file mode 100644 index 00000000..5809c60a Binary files /dev/null and b/1.2.4/Baldin_V/1.2.4/pictures/graph.png differ diff --git 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b/1.2.5/Baldin_V/1.2.5.tex new file mode 100644 index 00000000..ef421f09 --- /dev/null +++ b/1.2.5/Baldin_V/1.2.5.tex @@ -0,0 +1,221 @@ +\documentclass[a4paper,12pt]{article} +\usepackage[a4paper,top=1.3cm,bottom=2cm,left=1.5cm,right=1.5cm,marginparwidth=0.75cm]{geometry} +\usepackage{setspace} +\usepackage{cmap} +\usepackage{mathtext} +\usepackage[T2A]{fontenc} +\usepackage[utf8]{inputenc} +\usepackage[english,russian]{babel} +\usepackage{multirow} +\usepackage{graphicx} +\usepackage{wrapfig} +\usepackage{tabularx} +\usepackage{float} +\usepackage{longtable} +\usepackage{hyperref} +\hypersetup{colorlinks=true,urlcolor=blue} +\usepackage[rgb]{xcolor} +\usepackage{amsmath,amsfonts,amssymb,amsthm,mathtools} +\usepackage{icomma} +\mathtoolsset{showonlyrefs=true} +\usepackage{euscript} +\usepackage{mathrsfs} + +\DeclareMathOperator{\sgn}{\mathop{sgn}} +\newcommand*{\hm}[1]{#1\nobreak\discretionary{} + {\hbox{$\mathsurround=0pt #1$}}{}} + +\title{\textbf{Исследование прецессии уравновешенного гироскопа (1.2.5)}} +\author{Балдин Виктор} +\date{20 ноября 2023} + + +\begin{document} + + \maketitle + + \section{Аннотация} + + \textbf{Цель работы:} исследовать вынужденную прецессию гироскопа, установить зависимость скорости вынужденной прецессии от величины момента сил, действующий на ось гироскопа и сравнить ее со скоростью, рассчитанной по скорости прецессии.\\ + \textbf{Оборудование:} гироскоп в кардановом подвесе, секундомер, набор грузов, отдельный ротор гироскопа, цилиндр известной массы, крутильный маятник, штангенсциркуль, линейка. + + \section{Теоретические сведения} + + В этой работе исследуется зависимость скорости прецессии гироскопа от момента силы, приложенной к его оси. Для этого к оси гироскопа подвешиваются грузы. Скорость прецессии определяется по числу оборотов рычага вокруг вертикальной оси и времни, которое на это ушло, определяемоу секундомером. В процессе измерений рычаг не только поворачивается в результате прецессии гироскопа, но и опускается. Поэтому его в начале опыта следует преподнять на 5-6 градусов. Опять надо закончить, когда рычаг опустится на такой же угол.\\ + \begin{center}$ + \begin{array}{cc} + \includegraphics[width=0.40\textwidth]{img1.png}& + \includegraphics[width=0.40\textwidth]{img2.png}\\ + \end{array}$ + \end{center} + + Измерение скорости прецессии гироскопа позволяет вычислить угловую скорость вращения его ротора. Расчет производится по формуле: + + \begin{equation} + \Omega = \frac{mgl}{I_z\omega_0}, + \end{equation} + + где $m$ -- масса груза, $l$ -- расстояние от центра карданова подвеса до точки крепления груза на оси гироскопа, $I_z$ -- момент инерции гироскопа по его главной оси вращения. $\omega_0$ -- частота его вращения относительно главной оси, $\Omega$ -- частота прецессии.\\ + Момент инерции ротора относительно оси симметрии $I_0$ измеряется по крутильным колебаниям точной копии ротора, подвешиваемой вдоль оси симметрии на десткой проволоке. Период крутильных колебаний $T_0$ зависит от момента инерции $I_0$ и модуля кручения проволоки $f$: + + \begin{equation} + T_0 = 2\pi\sqrt{\frac{I_0}{f}}. + \end{equation} + + Чтобы исключить модуль кручения проволоки, вместо ротора гироскопа к той же проволоке подвешивают цилиндр правильной формы с известными размерами и массой, для которого легко можно вычислить момент инерции $I_\text{ц}$. Для определения момента инерции ротора гироскопа имеем: + + \begin{equation} + I_0 = I_\text{ц}\frac{T_0^2}{T_\text{ц}^2}, + \label{moment} + \end{equation} + Здесь $T_\text{ц}$ -- период крутильных колебаний цилиндра.\\ + \section{Методика измерений} + \begin{center} + \includegraphics[width=0.8\textwidth]{img3.png} + \end{center} + + Скорость вращения ротора гироскопа можно определить и не прибегая к исследованию прецессии. У используемых в работе гироскопов статор имеет две обмотки, необходимые для быстрой раскрутки гироскопа. В данной работе одну обмотку искользубт для раскрутки гироскопа, а вторую -- для измерения числа оборотов ротора. Ротор электромотора всегда немного намагничен. Вращаясь, он наводит во второй обмотке переменную ЭДС индукции, частота которой равна частоте врещения ротора. Частоту этой ЭДС можно, в частности, измерить по фигурам Лиссажу, получаемым на экране осциллографа, если на один вход подать исследуемую ЭДС, а на другой -- переменное напряжение с хорошо прокалиброванного генератора. При совпадении частот на экране получаем эллипс. + \section{Используемое оборудование} + Гироскоп в кардановом подвесе, секундомер, набор грузов, отдельный ротор гироскопа, цилиндр известной массы, крутильный маятник, штангенсциркуль, линейка. + + \section{Результаты измерений и обработка измерений} + \subsection{Измерения} + Данные для частоты прецессии и опускания гироскопа: $\Omega=\frac{2\pi N}{t}$ + + \begin{center} + \begin{tabular}{|c|c|c|c|} + \hline + Масса & $T$, с& $N$ & $\Omega$, $\text{с}^{-1}$ \\ + \hline + \multirow{2}{*}{$m = 336$ г}&178,52&6&21,12$\cdot 10^{-2}$ \\ + \cline{2-4} + &179,23 & 6 &21,03$\cdot 10^{-2}$ \\ + \hline + \end{tabular} + \quad + \begin{tabular}{|c|c|c|c|} + \hline + Масса & $T$, с& $N$ & $\Omega$, $\text{с}^{-1}$ \\ + \hline + \multirow{2}{*}{$m = 269$ г}&186,41&5&16,85$\cdot 10^{-2}$ \\ + \cline{2-4} + &187,02 & 5 &16,80$\cdot 10^{-2}$ \\ + \hline + \end{tabular} + \end{center} + + \begin{center} + \begin{tabular}{|c|c|c|c|} + \hline + Масса & $T$, с& $N$ &$\Omega$, $\text{с}^{-1}$ \\ + \hline + \multirow{2}{*}{$m = 215$ г}&188,31&4& 13,35$\cdot 10^{-2}$ \\ + \cline{2-4} + &188,01 & 4 &13,38$\cdot 10^{-2}$ \\ + \hline + \end{tabular} + \quad + \begin{tabular}{|c|c|c|c|} + \hline + Масса & $T$, с& $N$ &$\Omega$, $\text{с}^{-1}$ \\ + \hline + \multirow{2}{*}{$m = 174$ г}&232,43&4&10,81$\cdot 10^{-2}$ \\ + \cline{2-4} + &231,07 & 4 &10,88$\cdot 10^{-2}$ \\ + \hline + \end{tabular} + \end{center} + + \begin{center} + \begin{tabular}{|c|c|c|c|} + \hline + Масса & $T$, с& $N$ &$\Omega$, $\text{с}^{-1}$ \\ + \hline + \multirow{2}{*}{$m = 138$ г}&219,81&3&8,58$\cdot 10^{-2}$ \\ + \cline{2-4} + &218,99 & 3 &8,61$\cdot 10^{-2}$ \\ + \hline + \end{tabular} + \end{center} + + Каждый раз рычаг опускался на $12^\circ$, что равняется $\dfrac{\pi}{15}$. Для каждой массы посчитаем угловую скорость опускания рычага по формуле: $\omega = \dfrac{\pi/15}{T}$, и момент $M = mgl$, где $l = 121$ мм: + \begin{itemize} + \item $m = 336$ г, $\omega = 11,71\cdot 10^{-4}$ $\text{с}^{-1}$, $M = 39,88\cdot10^{-2}$ Н$\cdot$м + \item $m = 269$ г, $\omega = 11,22\cdot 10^{-4}$ $\text{с}^{-1}$, $M = 31,93\cdot10^{-2}$ Н$\cdot$м + \item $m = 215$ г, $\omega = 11,13\cdot 10^{-4}$ $\text{с}^{-1}$, $M = 25,52\cdot10^{-2}$ Н$\cdot$м + \item $m = 174$ г, $\omega = 9,04\cdot 10^{-4}$ $\text{с}^{-1}$, $M = 20,65\cdot10^{-2}$ Н$\cdot$м + \item $m = 138$ г, $\omega = 9,55\cdot 10^{-4}$ $\text{с}^{-1}$, $M = 16,38\cdot10^{-2}$ Н$\cdot$м + \end{itemize} + + Построим график зависимости $\Omega(M)$: + \begin{figure}[h!] + \includegraphics[scale=0.53]{1.2.5 graph} + \caption{Зависимость $ \Omega $ от $ M $} + \label{graph} + \end{figure} + + Далее найдем момент инерции ротора гироскопа по формуле \eqref{moment}, для этого посчитаем момент инерции цилиндра, с известной нам массой и диаметром: $I_\text{ц} = \dfrac{1}{2}mr^2 \approx 1,23\cdot 10^{-3}$ кг$\cdot \text{м}^2$, а периоды: $T_0 = 3,933$ с и $T_\text{ц} = 3,19$ с. Тогда $I_0 \approx 0,8\cdot 10^{-3}$ кг$\cdot \text{м}^2$ + + \begin{equation} + \sigma_\Omega = \sqrt{ \sigma_\text{случ}^2 + \sigma_\text{сист}^2} \;\;\;\;\;\; \sigma_\Omega^\text{сист} = \Omega \varepsilon_T \;\;\;\;\;\; \sigma_\Omega^\text{случ}= \sqrt{\frac{1}{n(n-1)} \sum_{i=1}^{n}(\Omega_i - \overline{\Omega})^2} + \end{equation} + + Каждая частота $\Omega$ с учетом погрешностей: + \begin{itemize} + \item $\Omega = (21,08 \pm 0,03)\cdot10^{-2}\text{ }\text{с}^{-1}$ + \item $\Omega = (16,83 \pm 0,04)\cdot10^{-2}\text{ }\text{с}^{-1}$ + \item $\Omega = (13,37 \pm 0,05)\cdot10^{-2}\text{ }\text{с}^{-1}$ + \item $\Omega = (10,85 \pm 0,05)\cdot10^{-2}\text{ }\text{с}^{-1}$ + \item $\Omega = (8,60 \pm 0,03)\cdot10^{-2}\text{ }\text{с}^{-1}$ + \end{itemize} + + Погрешность $\sigma_{I_0} = I_0\cdot\sqrt{\varepsilon_{I_\text{ц}}^2+ 4\varepsilon_{T_0}^2+ 4\varepsilon_{T_\text{ц}}^2 } \approx 0,03$ кг$\cdot \text{м}^2$, значит $I_0 = (0,80\pm 0,03)$ кг$\cdot \text{м}^2$ + \subsection{Частота вращения ротора} + Определить частоту вращения ротора можно по формуле $\omega_0 = \dfrac{1}{kI_0}$, где $k$ -- коэффицент наклона графика зависимости $\Omega(M)$. + + График построен по МНК, а значит: + \begin{equation} + k=\frac{\langle xy\rangle-\langle x\rangle \langle y\rangle}{\langle x^2\rangle - \langle x\rangle^2}\approx 0,531\frac{1}{\text{Дж}\cdot\text{с}} + \end{equation} + \begin{equation} + \sigma_k^\text{сл}=\frac{1}{\sqrt{N}}\sqrt{\frac{\langle y^2 \rangle - \langle y \rangle^2}{\langle x^2 \rangle - \langle x \rangle^2} - k^2 } \approx 0,002\text{ }\frac{1}{\text{Дж}\cdot\text{с}} + \end{equation} + + Тогда $\omega_0 = 2354,05\text{ }\text{с}^{-1}$, а $\sigma_{\omega_0} = \omega_0\cdot\sqrt{\varepsilon_{I_0}^2+ \varepsilon_k^2} \approx 88,72\text{ }\text{с}^{-1}$ + + Используя полученную угловую скорость можно определить частоту вращения ротора гироскопа: $\nu = \dfrac{\omega_0}{2\pi} \approx 374,7\text{ }\text{Гц}$, а $\sigma_{\nu} = \nu\varepsilon_{\omega_0} \approx 14,1\text{ }\text{Гц}$ + + Таким образом получаем: $\nu = (374,7\pm14,1)\text{ Гц}$, что с учетом сигмы попадает в значение полученное с помощью осциллографа $\nu_0 = 387,2\text{ Гц}$ + + \subsection{Момент силы трения} + + Оценить момент силы трения мы можем по формуле: $M = \omega I_0 \omega_0$, а $\sigma_M = M\cdot\sqrt{\varepsilon_M^2+ \varepsilon_k^2}$. Для каждой массы момент силы трения будет свой: + + \begin{itemize} + \item $m = 336$ г, $\omega = 11,71\cdot 10^{-4}$ $\text{с}^{-1}$, $M = (2,21\pm0,02)\cdot10^{-3}$ Н$\cdot$м + \item $m = 269$ г, $\omega = 11,22\cdot 10^{-4}$ $\text{с}^{-1}$, $M = (2,11\pm0,02)\cdot10^{-3}$ Н$\cdot$м + \item $m = 215$ г, $\omega = 11,13\cdot 10^{-4}$ $\text{с}^{-1}$, $M = (2,09\pm0,02)\cdot10^{-3}$ Н$\cdot$м + \item $m = 174$ г, $\omega = 9,04\cdot 10^{-4}$ $\text{с}^{-1}$, $M = (1,70\pm 0,02)\cdot10^{-3}$ Н$\cdot$м + \item $m = 138$ г, $\omega = 9,55\cdot 10^{-4}$ $\text{с}^{-1}$, $M = (1,80 \pm 0,02)\cdot10^{-3}$ Н$\cdot$м + \end{itemize} + + \section{Обсуждение результатов} + В результате данной работы мы: + \begin{enumerate} + \item Измерили момент инерции ротора гироскопа относительно оси симметрии. + \item Оценили погрешность в определении $I_0$, $\Omega$. + \item Рассчитали частоту вращения ротора гироскопа. + \item По скорости опускания рычага во время прецессии определили момент сил трения. + \item Определили частоту вращения ротора гироскопа по фигурам Лиссажу. + \item Оценили погрешность полученных результатов. Сравнили угловые скорости вращения + ротора гироскопа. + \item Убедились в применимости соотношения (5) в данной работе. + \end{enumerate} + + \section{Вывод} + Полученная частота совпадает со значением частоты, измеренным с помощью осциллографа ($ \nu_\text{осц} = 387.2$) в пределах погрешности.\\ + Также был оценен момент силы трения, действующий на ось гироскопа $ M \approx 10^{-3} \text{ } \text{Н} \cdot \text{м} $. Он оказался достаточно мал по сравнению с моментом силы тяжести груза, подвешенного на ось гироскопа, но достаточным для поворота гироскопа в сторону направления силы тяжести груза. Для его более точной оценки необходима более точная шкала определения отклонения гироскопа от начального уровня, которой, к сожалению, не было. + + + +\end{document} diff --git a/1.2.5/Baldin_V/img1.png b/1.2.5/Baldin_V/img1.png new file mode 100644 index 00000000..5300e0ec Binary files /dev/null and b/1.2.5/Baldin_V/img1.png differ diff --git a/1.2.5/Baldin_V/img2.png b/1.2.5/Baldin_V/img2.png new file mode 100644 index 00000000..f07cb0fa Binary files /dev/null and b/1.2.5/Baldin_V/img2.png differ diff --git a/1.2.5/Baldin_V/img3.png b/1.2.5/Baldin_V/img3.png new file mode 100644 index 00000000..9a772571 Binary files /dev/null and b/1.2.5/Baldin_V/img3.png differ diff --git a/1.2.5/pdf/Baldin_V.pdf b/1.2.5/pdf/Baldin_V.pdf new file mode 100644 index 00000000..7339fb93 Binary files /dev/null and b/1.2.5/pdf/Baldin_V.pdf differ diff --git a/1.4.8/Baldin_V/1.4.8.pdf b/1.4.8/Baldin_V/1.4.8.pdf new file mode 100644 index 00000000..7dba216c Binary files /dev/null and b/1.4.8/Baldin_V/1.4.8.pdf differ diff --git a/1.4.8/Baldin_V/1.4.8.tex b/1.4.8/Baldin_V/1.4.8.tex new file mode 100644 index 00000000..7f2d27ec --- /dev/null +++ b/1.4.8/Baldin_V/1.4.8.tex @@ -0,0 +1,297 @@ +\documentclass[a4, 12pt]{article} +\usepackage[a4paper,top=1.3cm,bottom=2cm,left=1.5cm,right=1.5cm,marginparwidth=0.75cm]{geometry} +\usepackage{setspace} +\usepackage{cmap} +\usepackage{mathtext} +\usepackage[utf8]{inputenc} +\usepackage[english,russian]{babel} +\usepackage[T2A]{fontenc} +\usepackage{multirow} +\usepackage{graphicx} +\usepackage{wrapfig} +\usepackage{tabularx} +\usepackage{float} +\usepackage{longtable} +\usepackage{hyperref} +\hypersetup{colorlinks=true,urlcolor=blue} +\usepackage[rgb]{xcolor} +\usepackage{amsmath,amsfonts,amssymb,amsthm,mathtools} +\usepackage{icomma} +\mathtoolsset{showonlyrefs=true} +\usepackage{euscript} +\usepackage{mathrsfs} + +\DeclareMathOperator{\sgn}{\mathop{sgn}} +\newcommand*{\hm}[1]{#1\nobreak\discretionary{} + {\hbox{$\mathsurround=0pt #1$}}{}} + + +\title{\textbf{Измерение модуля Юнга стержней методом акустического резонанса. (1.4.8)}} +\author{Балдин Виктор Б01-303} +\date{20 ноября 2023} + + +\begin{document} + + \maketitle + + \section{Введение} + \textbf{Цель работы:} исследовать явление акустического резонанса в тонком стержне; из- +мерить скорость распространения продольных звуковых колебаний в тонких стержнях из различных материалов и различных размеров; измерить модули Юнга раз- +личных материалов.\\ +\textbf{В работе используются:} генератор звуковых частот, частотомер, осциллограф, +электромагнитные излучатель и приёмник колебаний, набор стержней из различных материалов. + + \section{Теоретическая часть} + Основной характеристикой упругих свойств твёрдого тела является его +модуль Юнга $E$. Согласно закону Гука, если к элементу среды приложено +некоторое механическое напряжение $\sigma$, действующее вдоль некоторой +оси $x$ (напряжения по другим осям при этом отсутствуют), то в этом эле- +менте возникнет относительная деформацию вдоль этой же оси +$\varepsilon = \Delta x/x_0$ , определяемая соотношением +\begin{equation} + \sigma = \varepsilon E +\end{equation} +Если с помощью кратковременного воздействия в некотором элементе +твёрдого тела создать малую деформацию, она будет далее распростра- +няться в среде в форме волны, которую называют акустической или звуко- +вой. Распространение акустических волн обеспечивается за счёт упругости +и инерции среды. Волны сжатия/растяжения, распространяющиеся вдоль +оси, по которой происходит деформация, называются продольными. Как +будет строго показано далее, скорость $u$ распространения продольной аку- +стической волны в простейшем случае длинного тонкого стержня опреде- +ляется соотношением +\begin{equation} + u = \sqrt{\frac{E}{\rho}} +\end{equation} +где $\rho$ — плотность среды. +Заметим, что размерность модуля Юнга $E$ равна [Н/м$^2$] и совпадает с +размерностью механического напряжения (или давления). Характерные +значения модуля Юнга металлов лежат в диапазоне $E\sim$ 1010 ÷ 1012 Па, так +что при плотности $\rho\sim$ 104 кг/м3 характерные значения скорости звука в +твёрдых телах составляют $u\sim$ 103 - 104 м/с. +В общем случае звуковые волны в твёрдых телах могут быть не только +продольными, но и поперечными — при этом возникает деформация сдвига +перпендикулярно распространению волны. Кроме того, описание распространения волн в неограниченных средах осложняется тем +обстоятельством, что при отличном от нуля коэффициенте Пуассона 1 +напряжение вдоль одной из осей вызывает деформацию не только в про- +дольном, но и в поперечном направлении к этой оси. Таким образом, общее +описание звуковых волн в твёрдых телах — относительно непростая задача. +В данной работе мы ограничимся исследованием наиболее простого случая +упругих волн, распространяющихся в длинных тонких стержнях. +Рассмотрим стержень постоянного круглого сечения, радиус $R$ которого +много меньше его длины $L$. С точки зрения распространения волн стержень +можно считать тонким, если длина $\lambda$ звуковых волн в нём велика по срав- +нению с его радиусом: $\lambda R$. Такая волна может свободно распростра- +няться только вдоль стержня, поэтому можно считать, что стержень испы- +тывает деформации растяжения и сжатия только вдоль своей оси (заметим, +что в обратном пределе коротких волн $\lambda R$ стержень следует рассматри- +вать как безграничную сплошную среду). Если боковые стенки тонкого +стержня свободны (т.е. стержень не сжат с боков), то его деформации опи- +сывается законом Гука в форме (1), и, следовательно, его упругие свойства +определяются исключительно модулем Юнга среды. +Акустическая волна, распространяющаяся в стержне конечной длины $L$, +испытает отражение от торцов стержня. Если при этом на длине стержня +укладывается целое число полуволн, то отражённые волны будут склады- +ваться в фазе с падающими, что приведёт к резкому усилению амплитуды +их колебаний и возникновению акустического резонанса в стержне. Изме- +ряя соответствующие резонансные частоты, можно определить скорость +звуковой волны в стержне и, таким образом, измерить модуль Юнга мате- +риала стержня. Акустический метод является одним из наиболее точных +методов определения упругих характеристик твёрдых тел. +Получим дифференциальное уравнение, описывающее распростране- +ние упругих волн в тонком стержне. +Направим ось $x$ вдоль геометрической оси стержня (рис. 1). Разобьём +исходно недеформированный стержень на тонкие слои толщиной $\Delta x$. При +продольной деформации среды границы слоёв сместятся в некоторые но- +вые положения. Пусть плоскость среды, находящаяся исходно в точке $x$ +\begin{figure}[H] + \centering + \includegraphics[scale = 1.5]{scheme.png} + \caption{} +\end{figure} +сместилась к моменту $t$ на расстояние $\xi(x, t)$. Тогда слой, занимавший исходно +отрезок $[x, x+\Delta x]$, изменил свой продольный размер на величину $$\Delta \xi = +\frac{\partial \xi}{\partial x}\Delta x$$ + +\section{Методика измерений} +\begin{figure}[H] + \centering + \includegraphics[scale = 1.5]{stand.png} +\end{figure} +Схема экспериментальной установки приведена на рис. 3. Исследуемый +стержень 5 размещается на стойке 10. Возбуждение и приём колебаний в +стержне осуществляются электромагнитными преобразователями 4 и 6, +расположенными рядом с торцами стержня. Крепления 9, 11 электро- +магнитов дают возможность регулировать их расположение по высоте, а +также перемещать вправо-влево по столу 12. +\par Электромагнит 4 служит для возбуждения упругих механических про- +дольных колебаний в стержне. На него с генератора звуковой частоты 1 по- +даётся сигнал синусоидальной формы: протекающий в катушке электро- +магнита ток создаёт пропорциональное ему магнитное поле, вызывающее +периодическое воздействие заданной частоты на торец стержня (к торцам +стержней из немагнитных материалов прикреплены тонкие стальные +шайбы). Рядом с другим торцом стержня находится аналогичный электро- +магнитный датчик 6, который служит для преобразования механических +колебаний в электрические. Принцип работы электромагнитных датчиков +описан подробнее ниже. +Сигнал с выхода генератора поступает на частотомер 2 и на вход +канала X осциллографа 3. ЭДС, возбуждаемая в регистрирующем электро- +магните 6, пропорциональная амплитуде колебаний торца стержня, усили- +вается усилителем 7 и подаётся на вход канала Y осциллографа. +Изменяя частоту генератора и наблюдая за амплитудой сигнала с реги- +стрирующего датчика, можно определить частоту акустического резонанса +в стержне. Наблюдения в режиме X–Y позволяют сравнить сигналы гене- +ратора и датчика, а также облегчает поиск резонанса при слабом сигнале. +\par Как следует из формулы (2), модуль Юнга материала $E$ может быть +найден по скорости распространения акустических волн в стержне $u$ и его +плотности $\rho$. Для определения скорости $u$ в данной работе используется +метод акустического резонанса. Это явление состоит в том, что при часто- +тах гармонического возбуждения, совпадающих с собственными частотами +колебаний стержня $f \approx f_\text{рез}/Q$ , резко увеличивается амплитуда колебаний, при +этом в стержне образуется стоячая волна. +Возбуждение продольных колебаний в стержне происходит посред- +ством воздействия на торец стержня периодической силой, направленной +вдоль его оси. Зная номер гармоники $n$ и соответствующую резонансную +частоту $f_n$ , на которой наблюдается усиление амплитуды колебаний, +можно вычислить скорость распространения продольных волн в стержне: +\begin{equation} + u = 2L\frac{f_n}{n} +\end{equation} + +Таким образом, для измерения скорости $u$ необходимо измерить длину +стержня $L$ и получить зависимость резонансной частоты от номера резо- +нанса $n$. Если все теоретические предположения справедливы, эта за- +висимость будет прямой пропорциональностью. +Следует отметить, что в реальном металлическом стержне могут воз- +буждаться не только продольные, но и поперечные (в частности, изгибные) +колебания стержня. При этом каждому типу колебаний соответствует не +одна, а целый спектр частот. Таким образом, стержень «резонирует» не +только на частотах, определяемых формулой (15), но и на множестве дру- +гих частот. Для того чтобы отличить нужные нам резонансные частоты от +«паразитных», следует провести предварительные расчёты и не принимать +во внимание резонансы, не описываемые зависимостью (15). +Скажем также несколько слов о точности измерения резонансной ча- +стоты. В первую очередь отметим, что в идеальном случае резонанс дости- +гался бы при строгом совпадении частот $f = f_n$ (а амплитуда в резонансе +стремилась бы к бесконечности). Однако в реальности возбуждение стоя- +чей волны возможно при относительно малом отклонении частоты от резо- +нансной — амплитуда колебаний как функция частоты $A(f)$ имеет резкий +максимум при $f = f_n$. +\par Именно конечная ширина резонанса $\Delta f$ определяет в основном погреш- +ность измерения частоты в нашем опыте. +Используемые в работе металлические стержни являются весьма высо- +кодобротными системами: их добротность оказывается порядка $Q\sim$ 102 ÷ +÷ 103 . Поэтому ширина резонанса оказывается довольно малой, что приво- +дит к необходимости тонкой настройки частоты генератора (при $f\sim$ + 5 кГц ширина резонанса $\Delta f$ оказывается порядка нескольких герц) +Кроме того, время установления резонансных колебаний, которое можно +оценить как +\begin{equation} + \tau_\text{уст} \sim \frac{1}{\Delta f} \sim \frac{Q}{f}, +\end{equation} +оказывается весьма велико, из-за чего поиск резонанса нужно проводить, меняя частоту +генератора очень медленно. +\section{Оборудование} +Генератор звуковых частот, частотомер, осциллограф, +электромагнитные излучатель и приёмник колебаний, набор стержней из различных материалов. + +\section{Измерения и обработка результатов} +\begin{enumerate} + \item Измерим плотность всех предоставленных нам материалов: + \begin{table}[H] + \centering + \caption{Плотности} + \begin{tabular}{c|c|c|c|} + \hline + & Медь & Дюралюминий & Сталь \\ + \hline + $\rho$, г/см$^3$ & $8.68\pm0.08$ & $2.71\pm0.03$ & $7.73\pm0.05$ \\ + \hline + \end{tabular} + \end{table} + \item Включим генератор. + \item Длина всех исследуемых стержней дана: $L = (600\pm0.5)\;\text{мм}$. + \item Исследуем по порядку медный, дюралюминиевый и стальной стержни на + резонанс. + \begin{table}[H] + \centering + \caption{Найденные частоты резонансов в зависимости от номера резонанса} + \begin{tabular}{|c|c|c|c|c|c|c|} + \hline + & $n$ & 1 & 2 & 3 & 4 & 5 \\ \hline + Медь & $f$, кГц & 3.25329 & 6.49893 & 9.6742 & 12.8739 & 16.0952 \\ \hline + Дюраллюминий & $f$, кГц & 4.23494 & 8.46810 & 12.7060 & 16.0010 & 20.4800 \\ \hline + Сталь & $f$, Кгц & 4.13418 & 8.42034 & 12.5202 & 16.7948 & 20.9340 \\ \hline + \end{tabular} + \end{table} + \item Приведем графики зависимости частоты от номера резонанса: + % TODO: нарисовать крест погрешности руками + \begin{figure}[H] + \centering + \includegraphics[scale=0.5]{med.png} + \caption{Резонансы медного стержня} + \end{figure} + \begin{figure}[H] + \centering + \includegraphics[scale=0.5]{dur.png} + \caption{Резонансы дюралюминиевого стержня} + \end{figure} + \begin{figure}[H] + \centering + \includegraphics[scale=0.5]{steel.png} + \caption{Резонансы стального стержня} + \end{figure} + + \item По полученным угловым коэффициентам вычислим скорость распространения + звуковой волны в стержнях, по которой найдем модуль Юнга. + \begin{table}[H] + \centering + \caption{Рассчитанные угловые коэффициенты} + \begin{tabular}{|c|c|c|c|} + \hline + & Медь & Дюралюминий & Сталь \\ \hline + $k$, кГц & 3.20588 & 4.00230 & 4.19741 \\ \hline + $v$, м/с & 3847 & 4803 & 5037 \\ \hline + $E$, Гпа & 128 & 63 & 196 \\ \hline + $\epsilon_k$ & 0.0010 & 0.0008 & 0.0005 \\ \hline + $\epsilon_v$ & 0.0011 & 0.0009 & 0.0006 \\ \hline + $\epsilon_E$ & 0.02 & 0.02 & 0.02 \\ \hline + \end{tabular} + \end{table} + Таким образом, получаем итоговый результат: + $$ E_{\text{меди}} = (128\pm3)\;\text{ГПа}, $$ + $$ E_{\text{дюр}} = (63\pm2)\;\text{ГПа}, $$ + $$ E_{\text{ст}} = (196\pm4)\;\text{ГПа} $$ + \item Проведем дополнительные измерения в окрестности 1-го резонанса + для меди, чтобы получить добротность: + $$ f_1 = (3.25178\pm0.00003)\; \text{кГц}, \\\ f_2 = (3.26688\pm0.00003)\; \text{кГц}, $$ + $$ \Delta f = 10.01\pm0.06\; \text{Гц}, $$ + $$ Q = \frac{f}{\Delta f} = 325 \pm 3$$ +\end{enumerate} + + \section{Обсуждение результатов} + В результате работы мы: + \begin{enumerate} + \item Нашли добротность медного стержня как колебательной системы. + \item Получили зависимость $f(n)$. Как нетрудно убедиться по рис. 2 -- 4, + во всех случаях аппроксимация прямой действительно применима, причем с очень + хорошей точностью. + \item Так как наше теоретическое предположение выполнилось, на его основе + вычислили скорость звуковой волны во всех данных материалах. + \item Нашли модули Юнга для меди, дюралюминия и стали. + \item Если сравнить результаты с работой 1.3.1, где + проводилось измерения методом прогиба, окажется, что точность опыта в данной + работе существенно выше (в 1.3.1 мы получали погрешность порядка 10\%, тут + около 2\%, причем наиболее существенный вклад в погрешность внесло измерение + плотности). Такое расхождение можно объяснить высокой точностью измерения + частоты по сравнению с точностью измерения деформаций в 1.3.1. + \end{enumerate} + + \section{Вывод} + Таким образом, их всего вышесказанного можно заключить, что использованный + метод акустического резонанса куда лучше подходит для определения модуля + Юнга, нежели метод прямых макродеформаций, использованный в работе 1.3.1. + Точность получилось существенно выше. Кроме того, достаточно сделать точнее + измерение плотности, чтобы снизить погрешность еще на несколько порядков. +\end{document} diff --git a/1.4.8/Baldin_V/data.ods b/1.4.8/Baldin_V/data.ods new file mode 100644 index 00000000..848c7713 Binary files /dev/null and b/1.4.8/Baldin_V/data.ods differ diff --git a/1.4.8/Baldin_V/dur.png b/1.4.8/Baldin_V/dur.png new file mode 100644 index 00000000..920b7df9 Binary files /dev/null and b/1.4.8/Baldin_V/dur.png differ diff --git a/1.4.8/Baldin_V/med.png b/1.4.8/Baldin_V/med.png new file mode 100644 index 00000000..aefbaac0 Binary files /dev/null and b/1.4.8/Baldin_V/med.png differ diff --git a/1.4.8/Baldin_V/scheme.png b/1.4.8/Baldin_V/scheme.png new file mode 100644 index 00000000..38b4a3b2 Binary files /dev/null and b/1.4.8/Baldin_V/scheme.png differ diff --git a/1.4.8/Baldin_V/stand.png b/1.4.8/Baldin_V/stand.png new file mode 100644 index 00000000..fa50fe73 Binary files /dev/null and b/1.4.8/Baldin_V/stand.png differ diff --git a/1.4.8/Baldin_V/steel.png b/1.4.8/Baldin_V/steel.png new file mode 100644 index 00000000..e420abb0 Binary files /dev/null and b/1.4.8/Baldin_V/steel.png differ diff --git a/1.4.8/pdf/Baldin_V.pdf b/1.4.8/pdf/Baldin_V.pdf new file mode 100644 index 00000000..7dba216c Binary files /dev/null and b/1.4.8/pdf/Baldin_V.pdf differ diff --git a/2.1.3/Baldin_V/2.1.3.pdf b/2.1.3/Baldin_V/2.1.3.pdf new file mode 100644 index 00000000..1d928b4f Binary files /dev/null and b/2.1.3/Baldin_V/2.1.3.pdf differ diff --git a/2.1.3/Baldin_V/2.1.3.tex b/2.1.3/Baldin_V/2.1.3.tex new file mode 100644 index 00000000..4022e0d8 --- /dev/null +++ b/2.1.3/Baldin_V/2.1.3.tex @@ -0,0 +1,393 @@ +\documentclass[a4paper,12pt]{article} +\usepackage[a4paper,top=1.3cm,bottom=2cm,left=1.5cm,right=1.5cm,marginparwidth=0.75cm]{geometry} + +% Пакеты +\usepackage{mathtext} +\usepackage{setspace} +\usepackage{tabularx} +\usepackage{cmap} +\usepackage{longtable} +\usepackage{icomma} +\usepackage{euscript} +\usepackage{float} +\usepackage{cutwin} +\usepackage{mathrsfs} +\usepackage{adjustbox} +\usepackage{dashbox} +\usepackage[normalem]{ulem} +\usepackage[T2A]{fontenc} +\usepackage[utf8]{inputenc} %! закрепляет кодировку utf8 +\usepackage[english,russian]{babel} %! подключает русский и английский +%математические шрифты: +\usepackage{amsmath,amsfonts,amssymb,amsthm,mathrsfs,mathtools} +\usepackage[colorlinks, linkcolor = purple]{hyperref} %! оглавление для панели навигации по PDF-документу + гиперссылки +\usepackage{xcolor} %! добавляет цвета +\usepackage{enumitem} %! задание макета перечня. +\usepackage{xpatch} %? 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На измерении скорости звука основан один из наиболее точных методов определения показателя адиабаты. + + Скорость звука в газах определяется формулой: + + \begin{equation} + c=\sqrt{\gamma\frac{RT}{\mu}}. + \label{velocity} + \end{equation} + где $ R $ -- газовая постоянная, $ T $ -- температура газа, а $ \mu $ -- его молярная масса. Преобразуя эту формулу, найдем + \begin{equation}\label{gamma} + \gamma = \frac{\mu}{RT}c^2. + \end{equation} + + Таким образом, для определения показателя адиабаты достаточно измерить температуру газа и скорость распространения звука (молярная масса газа предполагается известной). + + Звуковая волна, распространяющаяся вдоль трубы, испытывает многократные отражения от торцов. Звуковые колебания в трубе являются наложением всех отраженных волн и очень сложны. Картина упрощается, если длина трубы $ L $ равна целому числу полуволн, то есть когда \[ L=n\lambda/2, \] где $ \lambda $ -- длина волны звука в трубе, а $ n $ -- любое целое число. Если это условие выполнено, то волна, отраженная от торца трубы, вернувшаяся к ее началу и вновь отраженная, совпадает по фазе с падающей. Совпадающие по фазе волны усиливают друг друга. Амплитуда звуковых колебаний при этом резко возрастает -- наступает резонанс. + + При звуковых колебаниях слои газа, прилегающие к торцам трубы, не испытывают смещения. Узлы смещения повторяются по всей длине трубы через $ \lambda/2 $. Между узлами находятся максимумы смещения. + + Скорость звука c связана с его частотой $ f $ и длиной волны $ \lambda $ соотношением + + \begin{equation}\label{lambda_f} + c=\lambda f. + \end{equation} + + Подбор условий, при которых возникает резонанс, можно производить двояко: + \begin{enumerate} + \item При неизменной частоте $ f $ звукового генератора (а следовательно, и неизменной длине звуковой волны $ \lambda $) можно изменять длину трубы $ L $. Для этого применяется раздвижная труба. Длина раздвижной трубы постепенно увеличивается, и наблюдается ряд последовательных резонансов. Возникновение резонанса легко наблюдать на осциллографе по резкому увеличению амплитуды колебаний. Для последовательных резонансов имеем \begin{equation}\label{first} + L_n=n\frac{\lambda}{2}, \quad L_{n+1}=(n+1)\frac{\lambda}{2}, \quad \dots, \quad L_{n+k} = n\frac{\lambda}{2}+k\frac{\lambda}{2}, + \end{equation} т. е. $ \lambda/2 $ равно угловому коэффициенту графика, изображающего зависимость длины трубы $ L $ от номера резонанса $ k $. Скорость звука находится по формуле \eqref{lambda_f}. + \item При постоянной длине трубы можно изменять частоту звуковых колебаний. В этом случае следует плавно изменять частоту $ f $ звукового генератора, а следовательно, и длину звуковой волны $ \lambda $. Для последовательных резонансов получим + \begin{equation}\label{4} + L=\frac{\lambda_1}{2}n=\frac{\lambda_2}{2}(n+1)=\dots=\frac{\lambda_{k+1}}{2}(n+k). + \end{equation} + + Из \eqref{lambda_f} и \eqref{4} имеем: + \[ f_1=\frac{c}{\lambda_1}=\frac{c}{2L}n, \quad f_2=\frac{c}{\lambda_2}=\frac{c}{2L}(n+1)=f_1+\frac{c}{2L},\quad \dots, \] + \begin{equation}\label{5} + f_{k+1}=\frac{c}{\lambda_{k+1}}=\frac{c}{2L}(n+k)=f_1+\frac{c}{2L}k. + \end{equation} + Скорость звука, деленная на $ 2L $, определяется, таким образом, по угловому коэффициенту графика зависимости частоты от номера резонанса. + \end{enumerate} + + \section{Экспериментальная установка} + + Соответственно двум методам измерения скорости звука в работе имеются две установки (рис. \ref{img1} и \ref{img2}). В обеих установках звуковые колебания в трубе возбуждаются телефоном Т и улавливаются микрофоном М. Мембрана телефона приводится в движение переменным током звуковой частоты; в качестве источника переменной ЭДС используется звуковой генератор ГЗ. Возникающий в микрофоне сигнал наблюдается на осциллографе ЭО. + + Микрофон и телефон присоединены к установке через тонкие резиновые трубки. Такая связь достаточна для возбуждения и обнаружения звуковых колебаний в трубе и в то же время мало возмущает эти колебания: при расчетах оба торца трубы можно считать неподвижными, а влиянием соединительных отверстий пренебречь. + + Первая установка (рис. \ref{img1}) содержит раздвижную трубу с миллиметровой шкалой. Через патрубок (на рисунке не показан) труба может наполняться воздухом или углекислым газом из газгольдера. На этой установке производятся измерения $ \gamma $ для воздуха и для $ CO_2 $. Вторая установка (рис. \ref{img2}) содержит теплоизолированную трубу постоянной длины. Воздух в трубе нагревается водой из термостата. Температура газа принимается равной температуре омывающей трубу воды. На этой установке измеряется зависимость скорости звука от температуры. + + \begin{figure}[H] + \begin{center} + \includegraphics[width=12cm]{ust1.jpg} + \end{center} + \caption{Установка для измерения скорости звука при помощи раздвижной трубы} + \label{img1} + \end{figure} + + \begin{figure}[H] + \begin{center} + \includegraphics[width=12cm]{ust2.jpg} + \end{center} + \caption{Установка для изучения зависимости скорости звука от температуры} + \label{img2} + \end{figure} + + \section{Ход работы} + + \subsection{Измерение $ C_P/C_V $ для воздуха при помощи установки с раздвижной трубой} + + \label{ident} + + Проведём измерение коэффициента $ C_p/C_v $ для воздуха при помощи установки с раздвижной трубой. Для проведения серии измерений фиксируем частоту звукового сигнала и оставляем её неизменной при до окончания снятия показаний. Увеличиваем и уменьшаем длину трубки, чтобы добиться резонанса, возникновение которого устанавливается при помощи осциллографа. При возникновении резонанса фиксируем то расстояние, на которое была выдвинута трубка прибора. Данные измерения проводим для нескольких значений частот. Полученные результаты заносим в таблицу. + + \begin{table}[H] + \centering + \begin{tabular}{|c|c|c|c|c|c|} + \hline + $L$, см & $f_1$, Гц & $f_2$, Гц & $f_3$, Гц & $f_4$, Гц & $f_5$, Гц \\ \hline + 4 & 245 & 485 & 744 & 976 & 1204 \\ \hline + 5 & 235 & 480 & 731 & 958 & 1170 \\ \hline + 6 & 218 & 431 & 648 & 866 & 1096 \\ \hline + 7 & 210 & 410 & 633 & 828 & 1067 \\ \hline + 8 & 184 & 369 & 537 & 724 & 908 \\ \hline + \end{tabular} + \end{table} + Для каждого измерения величины <<выдвига>> трубы $ \sigma_l = 0,5 $ мм. Также для каждого измерения вычислим $ \Delta L = L - l_0 $. Погрешность определения этой величины составит $ \sigma_{\Delta L}=~\sqrt{2}\sigma_l \approx~0,7 $~мм. + + По полученным данным построим графики зависимости $ f(n) $. + + \begin{figure}[h!] + \centering + \input{data/air.pgf} + \caption{Зависимость $ f $ от $ n $ для воздуха} + \label{graph1} + \end{figure} + + Аппроксимируем полученные зависимости прямыми $ y=ax $ используя метод наименьших квадратов. Коэффициент $ a $ находим согласно следующей формуле: + + \begin{equation}\label{mnk:a} + a=\frac{\left\langle fn \right\rangle}{\left\langle n^2 \right\rangle}. + \end{equation} + + + + Случайную погрешность определения $ a $ оценим следующим образом: + + \begin{equation}\label{mnk:sigma_a} + \sigma^\text{случ}_a=\sqrt{\frac{1}{N-1}\left(\frac{\left\langle f^2 \right\rangle}{\left\langle n^2 \right\rangle}-a^2\right)}, + \end{equation} + где $ N $ -- колличество измерений. Систематическая погрешность определения $ a $ равна $ \sigma_a^\text{сист} = \sigma_{\Delta L} $. Тогда полная погрешность определения коэффициента $ a $ может быть вычислена по следующей формуле: + + \begin{equation}\label{mnk:full_sigma} + \sigma_a=\sqrt{\left(\sigma^\text{случ}_a\right)^2+\left(\sigma^\text{сист}_a\right)^2}. + \end{equation} + + + Согласно \eqref{first}, угловой коэффициент наклона прямой $ a $ равен $ \lambda/2 $. По этой формуле вычислим $ \lambda $ и результаты также занесём в таблицу. + + Согласно \eqref{lambda_f}, скорость звука в воздухе можно вычислить по следующей формуле: + \[ c = \lambda f. \] + + Погрешность такого вычисления равна \[ \sigma_c=c\sqrt{\varepsilon_f^2+\varepsilon_\lambda^2}. \] При этом в каждом измерении примем $ \sigma_f \approx 1 $ Гц. + + Эти результаты также заносим в таблицу \ref{tab:resO2}. + + Таким образом, мы получили значение $ c $ для каждого отдельного значения частоты. Усредняя вычисленные значения, в итоге получаем \[\boxed{ c = (347,9 \pm 1,9) \text{ м/с}}\quad (\varepsilon=0,5\%) \] + + Также, по формуле \eqref{gamma}, вычислим $ C_P/C_V $: + + \[ \frac{C_p}{C_v} = \gamma = \frac{\mu}{RT}c^2. \] + + При этом для воздуха $ \displaystyle \mu \approx 0,02898 \text{ } \frac{\text{кг}}{\text{моль}} $. Во время эксперимента температура в лаборатории равнялась $ T = (26,0 \pm 0,1) \text{ } ^\circ C $. Тогда погрешность такого вычисления можно оценить по следующей формуле: + \[ \sigma_\gamma = \gamma\sqrt{\varepsilon_f^2+\left(2\varepsilon_c\right)^2}.\] + + В итоге получаем: + + \[ \boxed{\gamma = 1,41 \pm 0,01}\quad (\varepsilon=0,6\%) \] + + \subsection{Измерение $ C_P/C_V $ для углекислого газа при помощи установки с раздвижной трубой} + + В этой части работы проведём измерения, аналогичные проведённым в п. \ref{ident}, для трубы, заполненной углекислым газом. Занесем результаты измерений зависимости номера резонанса от величины, на которую выдвинута труба, в таблицу. + + \begin{table}[H] + \centering + \begin{tabular}{|c|c|c|c|c|c|} + \hline + $L$, см & $f_1$, Гц & $f_2$, Гц & $f_3$, Гц & $f_4$, Гц & $f_5$, Гц \\ \hline + 20 & 134 & 253 & 382 & 522 & 661 \\ \hline + 17 & 138 & 289 & 409 & 540 & 680 \\ \hline + 14 & 142 & 290 & 407 & 563 & 796 \\ \hline + \end{tabular} + \end{table} + + Строим графики зависимости $ f(n) $. Аппроксимируем зависимости прямыми $ y=ax+b $. + Вычисляем также $ \lambda $ и $ c $ для каждого значения длины $L$. + + \begin{figure}[h!] + \centering + \input{data/co2.pgf} + \caption{Зависимость $ f $ от $ n $, для углекислого газа} + \label{graph2} + \end{figure} + + Получаем + + \[ \boxed{c=(300{,}8 \pm 3{,}5) \text{ м/с}} \quad (\varepsilon=1,3\%)\] + + Вычисляя $ C_P/C_V $ аналогично п. \ref{ident}, получаем + + \[ \boxed{\gamma = 1,10 \pm 0,02}\quad (\varepsilon=1,4\%) \] + + \subsection{Измерение $ C_P/C_V $ для воздуха при различных температурах} + + Проведём измерения $ C_P/C_V $ для воздуха при различных температурах. Для этого будем использовать трубу постоянного размера $ L = (700 \pm 1) $ мм. Для фиксированной температуры будем изменять частоту звукового сигнала, тем самым изменяя и длину волны, так, чтобы мы могли наблюдать последовательные резонансы. Для каждого резонанса будем фиксировать частоту, при которой он возник. Полученные измерения занесём в таблицу \ref{tab:constL}. + + \begin{table}[H] + \centering + \begin{tabular}{|c|c|c|c|c|} + \hline + $ T $, К & 294,4 & 303,6 & 313,4 & 323,2 \\ \hline + $k$ & $ f_k $, Гц & $ f_k $, Гц & $ f_k $, Гц & $ f_k $, Гц \\ \hline + 0 & 236 &242 & 247& 251 \\ \hline + 1 & 469 &474 & 481 &490\\ \hline + 2 & 700 & 709 & 719&729 \\ \hline + 3 & 930 & 941 & 955 &970 \\ \hline + 4 & 1160 & 1175& 1193 &1211 \\ \hline + \end{tabular} + \caption{Результаты измерений при разных температурах для воздуха} + \label{tab:constL} + \end{table} + + По полученным экспериментальным данным построим графики зависимости $ f_k(k) $. + + \begin{figure}[h!] + \centering + \includegraphics[scale=0.542]{graph2} + \caption{Зависимость $ f_k $ от $ k $} + \label{graph} + \end{figure} + + Аппроксимируем полученные зависимости прямыми $ y=ax $ используя метод наименьших квадратов. Коэффициент $ a $ и погрешности его определения находим согласно формулам \eqref{mnk:a}, \eqref{mnk:sigma_a} и \eqref{mnk:full_sigma}. Результаты вычислений для каждой температуры заносим в таблицу \ref{tab:resConstL}. + + \begin{table}[H] + \centering + \begin{tabular}{|c|c|c|c|c|c|c|} + \hline + $ T $, К & $ a $, с$ ^{-1} $ & $ \sigma_a $, с$ ^{-1} $ & $ c $, м/с & $ \sigma_c $, м/с & $ \gamma $ & $ \sigma_\gamma $ \\ \hline + 294,4 & 242,8 & 1,3 & 339,9 & 1,8 & 1,369 & 0,007 \\ \hline + 303,6 & 246,9 & 0,9 & 345,7 & 1,3 & 1,373 & 0,005 \\ \hline + 313,4 & 250,8 & 1,0 & 351,1 & 1,4 & 1,372 & 0,005 \\ \hline + 323,2 & 254,3 & 0,9 & 356,0 & 1,3 & 1,368 & 0,005 \\ \hline + 333,2 & 258,2 & 0,8 & 361,5 & 01,2 & 1,368 & 0,005 \\ \hline + \end{tabular} + \caption{Результаты вычислений при различных температурах} + \label{tab:resConstL} + \end{table} + + Также, согласно формуле \eqref{5}, коэффициент наклона $ \displaystyle a = \frac{c}{2L}$. Тогда вычислим скорость звука $ c $ при фиксированной температуре и её погрешность, результаты вычислений занесём в таблицу \ref{tab:resConstL}. + + Кроме того, по формуле \eqref{gamma} вычислим $ \gamma $ при фиксированной температуре и погрешность этого вычисления. Результаты занесём в таблицу $ \ref{tab:resConstL} $. + + Согласно полученным данным, можно утверждать, что $ \gamma $ остаётся постоянной в исследуемом диапазоне температур. Поэтому усредним результаты, полученные при различных значениях температуры и получим для воздуха: + + \[ \gamma = 1,37 \pm 0,01 \ (\varepsilon=0,4\%) \] + + \section{Выводы} + + В ходе выполнения работы мы: + + \begin{itemize} + \item измерили частоту колебаний и длину волны при резонансе звуковых колебаний в газе, заполняющем экспериментальную установку; + \item определили разными методами показатель адиабаты с помощью уравнения состояния идеального газа. + \end{itemize} + + В ходе работы показатель адиабаты для воздуха был измерен двумя разными способами. Сначала измерения проводились при фиксированной частоте звукового сигнала, а мы изменяли длину трубы. В ходе таких измерения было получено: + + \[ \gamma_f = 1,41 \pm 0,01 \ (\varepsilon=0,6\%) \] + + Затем измерения проводились на другой установке, на которой длина трубы оставалась постоянной на протяжении всего опыта, а резонанса мы добивались при помощи изменения частоты звукового сигнала. В ходе этих измерений также исследовалась зависимость коэффициента адиабаты $ \gamma $ от температуры газа. Было получено, что показатель адиабаты не зависит от температуры в диапазоне температур $ 20-60 $ $ ^\circ C $ и равняется: + + \[ \gamma_L = 1,37 \pm 0,01 \ (\varepsilon=0,6\%) \] + + Сравним полученные данные с табличными. Согласно справочнику, показатель адиабаты для воздуха при нормальных условиях равен $ \gamma = 1,4 $. Таким образом, можно утверждать, что результат измерения $ \gamma $ на первой установке в пределах погрешности совпадает с табличными данными. Результаты измерения на второй установке незначительно отличаются от табличных. Это может быть связано с большой неточностью определения резонансных частот на второй установке. Чтобы этого избежать, необходимо использовать генератор частоты с возможностью более точной настройки для возможности четкого отслеживания резонансов. + + Также в ходе работы был измерен показатель адиабаты для углекислого газа. Измерения проводились на первой установке. В итоге мы получили \[ {\gamma_{CO_2} = 1,27 \pm 0,02}\ (\varepsilon=1,4\%) \] + + Сравним эти данные с табличными. Согласно справочнику, показатель адиабаты для углекислого газа при нормальных условиях $ \gamma = 1,3 $. Таким образом, полученные данные незначительно отличаются от табличных. Это может быть связано с тем, что при измерениях в трубе находился углекислый газ с примесями (в основном, азот и кислород), которые могли исказить результаты измерений. Для повышения точности, эксперимент стоит проводить в атмосфере углекислого газа, чтобы исключить попадание различных примесей в трубу. + + + + + + +\end{document} diff --git a/2.1.3/Baldin_V/data.csv b/2.1.3/Baldin_V/data.csv new file mode 100644 index 00000000..9ea1f76d --- /dev/null +++ b/2.1.3/Baldin_V/data.csv @@ -0,0 +1,8 @@ +n,1,2,3,4,5,0 +f,245,485,744,976,1204,0 +,235,480,731,958,1170,0 +,218,431,648,866,1095,0 +,210,410,633,828,1067,0 +,184,369,537,724,908,0 +,,,,,, +"c, м/с",300.08,,,,, diff --git a/2.1.3/Baldin_V/data/air.csv b/2.1.3/Baldin_V/data/air.csv new file mode 100644 index 00000000..7d01647d --- /dev/null +++ b/2.1.3/Baldin_V/data/air.csv @@ -0,0 +1,6 @@ +"$L$, см","$f_1$, Гц","$f_2$, Гц","$f_3$, Гц","$f_4$, Гц","$f_5$, Гц" +4,245,485,744,976,1204 +5,235,480,731,958,1170 +6,218,431,648,866,1096 +7,210,410,633,828,1067 +8,184,369,537,724,908 diff --git a/2.1.3/Baldin_V/data/air.pgf b/2.1.3/Baldin_V/data/air.pgf new file mode 100644 index 00000000..bbecda51 --- /dev/null +++ b/2.1.3/Baldin_V/data/air.pgf @@ -0,0 +1,1341 @@ +%% Creator: Matplotlib, PGF backend +%% +%% To include the figure in your LaTeX document, write +%% \input{.pgf} +%% +%% Make sure the required packages are loaded in your preamble +%% \usepackage{pgf} +%% +%% Also ensure that all the required font packages are loaded; for instance, +%% the lmodern package is sometimes necessary when using math font. +%% \usepackage{lmodern} +%% +%% Figures using additional raster images can only be included by \input if +%% they are in the same directory as the main LaTeX file. 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+\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=1.388889in,y=2.716759in,left,base]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle y = 180.30x + 3.50\)}% +\end{pgfscope}% +\end{pgfpicture}% +\makeatother% +\endgroup% diff --git a/2.1.3/Baldin_V/data/co2.csv b/2.1.3/Baldin_V/data/co2.csv new file mode 100644 index 00000000..1349ccc3 --- /dev/null +++ b/2.1.3/Baldin_V/data/co2.csv @@ -0,0 +1,4 @@ +"$L$, см","$f_1$, Гц","$f_2$, Гц","$f_3$, Гц","$f_4$, Гц","$f_5$, Гц" +20,134,253,382,522,661 +17,138,289,409,540,680 +14,142,290,407,563,796 diff --git a/2.1.3/Baldin_V/data/co2.pgf b/2.1.3/Baldin_V/data/co2.pgf new file mode 100644 index 00000000..c1fcb71d --- /dev/null +++ b/2.1.3/Baldin_V/data/co2.pgf @@ -0,0 +1,1522 @@ +%% Creator: Matplotlib, PGF backend +%% +%% To include the figure in your LaTeX document, write +%% \input{.pgf} +%% +%% Make sure the required packages are loaded in your preamble +%% \usepackage{pgf} +%% +%% Also ensure that all the required font packages are loaded; for instance, +%% the lmodern package is sometimes necessary when using math font. +%% \usepackage{lmodern} +%% +%% Figures using additional raster images can only be included by \input if +%% they are in the same directory as the main LaTeX file. For loading figures +%% from other directories you can use the `import` package +%% \usepackage{import} +%% +%% and then include the figures with +%% \import{}{.pgf} +%% +%% Matplotlib used the following preamble +%% +%% \makeatletter\@ifpackageloaded{underscore}{}{\usepackage[strings]{underscore}}\makeatother +%% +\begingroup% +\makeatletter% +\begin{pgfpicture}% +\pgfpathrectangle{\pgfpointorigin}{\pgfqpoint{7.000000in}{4.000000in}}% +\pgfusepath{use as bounding box, clip}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetmiterjoin% +\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.000000pt}% +\definecolor{currentstroke}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{7.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{7.000000in}{4.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{4.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathclose% +\pgfusepath{fill}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetmiterjoin% +\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.000000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetstrokeopacity{0.000000}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{3.520000in}}% +\pgfpathlineto{\pgfqpoint{0.875000in}{3.520000in}}% +\pgfpathlineto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathclose% +\pgfusepath{fill}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.121569,0.466667,0.705882}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{0.121569,0.466667,0.705882}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.015528in}{-0.015528in}}{\pgfqpoint{0.015528in}{0.015528in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathcurveto{\pgfqpoint{0.004118in}{-0.015528in}}{\pgfqpoint{0.008068in}{-0.013892in}}{\pgfqpoint{0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{0.013892in}{-0.008068in}}{\pgfqpoint{0.015528in}{-0.004118in}}{\pgfqpoint{0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{0.015528in}{0.004118in}}{\pgfqpoint{0.013892in}{0.008068in}}{\pgfqpoint{0.010980in}{0.010980in}}% +\pgfpathcurveto{\pgfqpoint{0.008068in}{0.013892in}}{\pgfqpoint{0.004118in}{0.015528in}}{\pgfqpoint{0.000000in}{0.015528in}}% +\pgfpathcurveto{\pgfqpoint{-0.004118in}{0.015528in}}{\pgfqpoint{-0.008068in}{0.013892in}}{\pgfqpoint{-0.010980in}{0.010980in}}% +\pgfpathcurveto{\pgfqpoint{-0.013892in}{0.008068in}}{\pgfqpoint{-0.015528in}{0.004118in}}{\pgfqpoint{-0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{-0.015528in}{-0.004118in}}{\pgfqpoint{-0.013892in}{-0.008068in}}{\pgfqpoint{-0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{-0.008068in}{-0.013892in}}{\pgfqpoint{-0.004118in}{-0.015528in}}{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathclose% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{2.107955in}{1.199441in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.094318in}{1.762151in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.080682in}{2.338642in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{5.067045in}{2.860010in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{6.053409in}{3.346926in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{1.000000,0.498039,0.054902}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{1.000000,0.498039,0.054902}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.015528in}{-0.015528in}}{\pgfqpoint{0.015528in}{0.015528in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathcurveto{\pgfqpoint{0.004118in}{-0.015528in}}{\pgfqpoint{0.008068in}{-0.013892in}}{\pgfqpoint{0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{0.013892in}{-0.008068in}}{\pgfqpoint{0.015528in}{-0.004118in}}{\pgfqpoint{0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{0.015528in}{0.004118in}}{\pgfqpoint{0.013892in}{0.008068in}}{\pgfqpoint{0.010980in}{0.010980in}}% +\pgfpathcurveto{\pgfqpoint{0.008068in}{0.013892in}}{\pgfqpoint{0.004118in}{0.015528in}}{\pgfqpoint{0.000000in}{0.015528in}}% +\pgfpathcurveto{\pgfqpoint{-0.004118in}{0.015528in}}{\pgfqpoint{-0.008068in}{0.013892in}}{\pgfqpoint{-0.010980in}{0.010980in}}% +\pgfpathcurveto{\pgfqpoint{-0.013892in}{0.008068in}}{\pgfqpoint{-0.015528in}{0.004118in}}{\pgfqpoint{-0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{-0.015528in}{-0.004118in}}{\pgfqpoint{-0.013892in}{-0.008068in}}{\pgfqpoint{-0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{-0.008068in}{-0.013892in}}{\pgfqpoint{-0.004118in}{-0.015528in}}{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathclose% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{2.107955in}{1.160395in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.094318in}{1.649609in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.080682in}{2.148009in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{5.067045in}{2.648706in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{6.053409in}{3.176965in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.172549,0.627451,0.172549}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{0.172549,0.627451,0.172549}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.015528in}{-0.015528in}}{\pgfqpoint{0.015528in}{0.015528in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathcurveto{\pgfqpoint{0.004118in}{-0.015528in}}{\pgfqpoint{0.008068in}{-0.013892in}}{\pgfqpoint{0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{0.013892in}{-0.008068in}}{\pgfqpoint{0.015528in}{-0.004118in}}{\pgfqpoint{0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{0.015528in}{0.004118in}}{\pgfqpoint{0.013892in}{0.008068in}}{\pgfqpoint{0.010980in}{0.010980in}}% +\pgfpathcurveto{\pgfqpoint{0.008068in}{0.013892in}}{\pgfqpoint{0.004118in}{0.015528in}}{\pgfqpoint{0.000000in}{0.015528in}}% +\pgfpathcurveto{\pgfqpoint{-0.004118in}{0.015528in}}{\pgfqpoint{-0.008068in}{0.013892in}}{\pgfqpoint{-0.010980in}{0.010980in}}% +\pgfpathcurveto{\pgfqpoint{-0.013892in}{0.008068in}}{\pgfqpoint{-0.015528in}{0.004118in}}{\pgfqpoint{-0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{-0.015528in}{-0.004118in}}{\pgfqpoint{-0.013892in}{-0.008068in}}{\pgfqpoint{-0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{-0.008068in}{-0.013892in}}{\pgfqpoint{-0.004118in}{-0.015528in}}{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathclose% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{2.107955in}{1.142021in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.094318in}{1.601376in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.080682in}{2.113558in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{5.067045in}{2.561429in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{6.053409in}{3.110358in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.839216,0.152941,0.156863}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{0.839216,0.152941,0.156863}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.015528in}{-0.015528in}}{\pgfqpoint{0.015528in}{0.015528in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathcurveto{\pgfqpoint{0.004118in}{-0.015528in}}{\pgfqpoint{0.008068in}{-0.013892in}}{\pgfqpoint{0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{0.013892in}{-0.008068in}}{\pgfqpoint{0.015528in}{-0.004118in}}{\pgfqpoint{0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{0.015528in}{0.004118in}}{\pgfqpoint{0.013892in}{0.008068in}}{\pgfqpoint{0.010980in}{0.010980in}}% 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+\begin{pgfscope}% +\pgfsys@transformshift{4.080682in}{1.893067in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{5.067045in}{2.322564in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{6.053409in}{2.745171in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.580392,0.403922,0.741176}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{0.580392,0.403922,0.741176}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.015528in}{-0.015528in}}{\pgfqpoint{0.015528in}{0.015528in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{-0.015528in}}% 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+\begin{pgfscope}% +\pgfsys@transformshift{5.067045in}{1.952783in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{6.053409in}{2.487932in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{0.000000in}{-0.048611in}}{\pgfqpoint{0.000000in}{0.000000in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.048611in}}% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{1.121591in}{0.440000in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% 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+\pgfpathmoveto{\pgfqpoint{-0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{-0.048611in}{0.000000in}}% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{0.875000in}{2.497119in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=0.569444in, y=2.448894in, left, base]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle {800}\)}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.048611in}{0.000000in}}{\pgfqpoint{-0.000000in}{0.000000in}}{% 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(dfs.__len__()): + for j in range(1, dfs[i].__len__()): + print(nums) + print(dfs[i].iloc[j, 1:]) + plt.scatter(nums, dfs[i].iloc[j, 1:], s=5) + a, b = np.polyfit(nums, dfs[i].iloc[j, 1:], deg=1) + plt.plot(z, a * z + b, label=f'$y = {a:.2f}x + {b:.2f}$') + plt.grid() + plt.legend() + plt.savefig(files[i]) + +dfs = readFiles(['air.csv', 'co2.csv', 'temp.csv']) +drawFreq(dfs[:2], ['air.pgf', 'co2.pgf']) diff --git a/2.1.3/Baldin_V/data/temp.csv b/2.1.3/Baldin_V/data/temp.csv new file mode 100644 index 00000000..890e2d38 --- /dev/null +++ b/2.1.3/Baldin_V/data/temp.csv @@ -0,0 +1,5 @@ +"$T$, \degree C","$f_1$, Гц","$f_2$, Гц","$f_3$, Гц","$f_4$, Гц","$f_5$, Гц" +20,236,469,700,930,1160 +30,242,474,709,941,1175 +40,247,481,719,955,1193 +50,251,490,729,970,1211 diff --git a/2.1.3/Baldin_V/graph1.png b/2.1.3/Baldin_V/graph1.png new file mode 100644 index 00000000..ad6ea305 Binary files /dev/null and b/2.1.3/Baldin_V/graph1.png differ diff --git a/2.1.3/Baldin_V/graph2.png b/2.1.3/Baldin_V/graph2.png new file mode 100644 index 00000000..b65b7f46 Binary files /dev/null and b/2.1.3/Baldin_V/graph2.png differ diff --git a/2.1.3/Baldin_V/graph3.png b/2.1.3/Baldin_V/graph3.png new file mode 100644 index 00000000..27919210 Binary files /dev/null and b/2.1.3/Baldin_V/graph3.png differ diff --git a/2.1.3/Baldin_V/stand.png b/2.1.3/Baldin_V/stand.png new file mode 100644 index 00000000..e857a1a3 Binary files /dev/null and b/2.1.3/Baldin_V/stand.png differ diff --git a/2.1.3/Baldin_V/ust1.jpg b/2.1.3/Baldin_V/ust1.jpg new file mode 100644 index 00000000..7c744b9f Binary files /dev/null and b/2.1.3/Baldin_V/ust1.jpg differ diff --git a/2.1.3/Baldin_V/ust2.jpg b/2.1.3/Baldin_V/ust2.jpg new file mode 100644 index 00000000..29f68dd3 Binary files /dev/null and b/2.1.3/Baldin_V/ust2.jpg differ diff --git a/2.1.3/pdf/Baldin_V.pdf b/2.1.3/pdf/Baldin_V.pdf new file mode 100644 index 00000000..1d928b4f Binary files /dev/null and b/2.1.3/pdf/Baldin_V.pdf differ diff --git a/2.1.6/Baldin_V/2.1.6.ipynb b/2.1.6/Baldin_V/2.1.6.ipynb new file mode 100644 index 00000000..cacefa4e --- /dev/null +++ b/2.1.6/Baldin_V/2.1.6.ipynb @@ -0,0 +1,125 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Эффект Джоуля-Томсона" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib\n", + "from matplotlib import pyplot as plt\n", + "\n", + "matplotlib.use('pgf')\n", + "matplotlib.rcParams.update({\n", + " 'pgf.texsystem': 'pdflatex',\n", + " 'font.family': 'serif',\n", + " 'text.usetex': True,\n", + " 'pgf.rcfonts': False,\n", + "})\n", + "\n", + "def read_files(files):\n", + " data = []\n", + " for f in files:\n", + " data += [pd.read_csv(f, sep=',', skipinitialspace=True)]\n", + " return data\n", + "\n", + "T = [17, 30, 40, 50]\n", + "colors = ['red', 'green', 'blue', 'magenta']\n", + "[data] = read_files(['data/T.csv'])\n", + "\n", + "def get_linear(x, y, xerr, yerr):\n", + " a, b = np.polyfit(x, y, deg=1)\n", + " aerr = np.sqrt(1 / (x.__len__() - 1) * (sum([(it - b)**2 for it in y]) \n", + " / sum([it**2 for it in x]) - a**2))\n", + " syserr = np.sqrt((np.average(xerr) / (max(x) - min(x)))**2\n", + " + (np.average(yerr) / (max(y) - min(y)))**2)\n", + " a_syserr = a * syserr\n", + " berr = aerr * np.sqrt(np.average([it**2 for it in x]))\n", + " b_syserr = b * syserr\n", + " return ((a, b), (np.sqrt(aerr**2 + a_syserr**2), np.sqrt(berr**2 + b_syserr**2)))\n", + "\n", + "plt.figure(figsize=(7, 4))\n", + "\n", + "mu = []\n", + "muerr = []\n", + "P_ERR = 0.1\n", + "# Аппроксимация\n", + "for i in range(1, 5):\n", + " y = data[f'T{i}']\n", + " yerr = data[f'dT{i}']\n", + " z = np.linspace(min(data['P']), max(data['P']), 1000)\n", + " ((a, b), (aerr, berr)) = get_linear(data['P'], y, [P_ERR], yerr)\n", + " plt.plot(z, a * z + b, \n", + " label=f'$T = {T[i - 1]}$ \\\\textdegree C',\n", + " color=colors[i - 1])\n", + " mu.append(a)\n", + " muerr.append(aerr)\n", + " plt.errorbar(data['P'], data[f'T{i}'], xerr=P_ERR, yerr=yerr, \n", + " fmt=f'{colors[i - 1][0]}.')\n", + "\n", + "# Оформление\n", + "plt.xlabel('$\\\\Delta P$, A')\n", + "plt.ylabel('$\\\\Delta T$, \\\\textdegree C')\n", + "plt.grid(linestyle='--')\n", + "plt.legend()\n", + "plt.savefig('data/T.pgf')\n", + "\n", + "inverse_T = [10**3 / (t + 273) for t in T]\n", + "mu_T = pd.DataFrame()\n", + "mu_T['$T$, K'] = T\n", + "mu_T['$1/T$, $10^3$ K$^{-1}$'] = inverse_T\n", + "mu_T['$\\mu$'] = mu\n", + "mu_T['$\\Delta\\mu$'] = muerr\n", + "mu_T.to_csv('data/mu.csv')\n", + "\n", + "R = 8.31\n", + "C_P = 7 / 2 * R\n", + "\n", + "plt.figure(figsize=(7, 4))\n", + "((alpha, beta), (alpha_err, beta_err)) = get_linear(inverse_T, mu, [0], muerr)\n", + "z = np.linspace(min(inverse_T), max(inverse_T), 1000)\n", + "plt.plot(z, alpha * z + beta, label='', color='red')\n", + "coeffs = pd.DataFrame()\n", + "coeffs['a'] = [alpha * C_P * R / 2 / 100, alpha_err * C_P * R / 200, alpha_err / alpha * 100]\n", + "coeffs['b'] = [-beta * C_P / 10, beta_err * C_P / 10, -beta_err / beta * 100]\n", + "coeffs.to_csv('data/coeffs.csv')\n", + "\n", + "plt.errorbar(inverse_T, mu, yerr=muerr, fmt='r.')\n", + "plt.xlabel('$T^{-1}$, $10^{-3}$ K$^{-1}$')\n", + "plt.ylabel('$\\\\mu$, $10^{-5}$')\n", + "plt.grid()\n", + "plt.savefig('data/mu.pgf')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2.1.6/Baldin_V/2.1.6.pdf b/2.1.6/Baldin_V/2.1.6.pdf new file mode 100644 index 00000000..26f6a466 Binary files /dev/null and b/2.1.6/Baldin_V/2.1.6.pdf differ diff --git a/2.1.6/Baldin_V/2.1.6.tex b/2.1.6/Baldin_V/2.1.6.tex new file mode 100644 index 00000000..8a09252c --- /dev/null +++ b/2.1.6/Baldin_V/2.1.6.tex @@ -0,0 +1,236 @@ +\documentclass[12pt]{article} +\usepackage{amsmath} +\usepackage{amsfonts} +\usepackage{amssymb} +\usepackage[utf8]{inputenc} +\usepackage[T1,T2A]{fontenc} +\usepackage[english, russian]{babel} +\usepackage{graphicx} +\usepackage{float} +\usepackage[left=2cm,right=2cm,top=2cm,bottom=2cm]{geometry} +\usepackage{wrapfig} +\usepackage{pgfplots} +\usepackage{setspace} +\usepackage{indentfirst} +\usepackage{subfigure} +\usepackage{hyperref} +\usepackage{mathrsfs} + +\hypersetup{ + colorlinks=true, + linkcolor=red, + urlcolor=magenta, +} + +\graphicspath{{pictures}} + +\title{ + Лабораторная работа 2.1.6 \\ + <<Эффект Джоуля-Томсона>> +} + +\author{Балдин Виктор, Б01-303} + +\begin{document} + \maketitle + \paragraph{Цель работы} + \begin{enumerate} + \item Определение изменения температуры углекислого газа при протекании + через малопроницаемую перегородку при разных начальных значениях. + \item Вычисление по результатам опытов коэффициентов Ван-дер-Ваальса + $a$ и $b$. + \end{enumerate} + \paragraph{Оборудование} Трубка с пористой перегородкой; труба Дьюара; + термостат, термометры; дифференицальная термопара; микровольтметр; + балластный баллон; манометр. + + \section{Теоретическая часть} + Рассмотрим стационарный поток газа между произвольными сечениями трубки + и пористой перегородкой. Для 1 моля можно записать первое начало термодинамики: + \begin{equation} + A_1 - A_2 = \left(U_2 + \frac{\mu v_2^2}{2}\right) - \left(U_1 + \frac{\mu v_1^2}{2}\right), + \label{term1} + \end{equation} + где $A_1 = P_1V_1$ -- работа над газом, необходимая для внесения его в + первое сечение трубки, $A_2 = P_2V_2$ -- работа газа по прохождению второго + сечения. Используя уравнение \ref{term1}, получим: + \begin{equation} + H_1 - H_2 = (U_1 + P_1V_1) - (U_2 + P_2V_2) = \frac{1}{2}\mu (v_2^2 - v_1^2) + \end{equation} + Или: + \begin{equation} + C_P(T_1 - T_2) = \frac{1}{2}\mu (v_2^2 - v_1^2), + \end{equation} + откуда: + \begin{equation} + \Delta T = \frac{\mu}{2C_P}(v_2^2 - v_1^2) + \label{dT} + \end{equation} + При этом: + \begin{equation} + v_1 = \frac{P_2}{P_1}v_2 + \end{equation} + Таким образом, для углекислого газа оценка по формуле \ref{dT} дает + $\Delta T = 7 \cdot 10^{-4}$ К, что ничтожно мало по сравнению с измеряемым + эффектом. + \paragraph{Эффект Джоуля-Томсона} Для дифференциального эффекта Джоуля-Томсона + имеем: + \begin{equation} + \Delta T = \frac{\frac{2a}{RT} - b}{C_P}\Delta P, + \end{equation} + где $a$ и $b$ -- коэффициенты в уравнении Ван-дер-Ваальса: + \begin{equation} + \left(P + \frac{a}{V^2}\right)(V - b) = RT + \label{vdv} + \end{equation} + Таким образом, $a$ и $b$ можно получить из нескольких пар значений + $(\mu, T)$, где + \begin{equation} + \mu = \frac{\frac{2a}{RT} - b}{C_P} + \label{mu} + \end{equation} + Через коэффициенты Ван-дер-Ваальса находим температуру инверсии эффекта + Джоуля-Томсона: + \begin{equation} + T_i = \frac{2a}{Rb} + \label{ti} + \end{equation} + + Критическая точка газа определяется условиями: + \begin{eqnarray} + \left(\frac{\partial P}{\partial V}\right)_T = 0, \ \left(\frac{\partial^2 P}{\partial V^2}\right)_T = 0, + \end{eqnarray} + откуда, используя уравнение \ref{vdv}, получим все параметры газа в + критической точке: + \begin{eqnarray} + V_{\text{к}} = 3b, \ T_{\text{к}} = \frac{8a}{27Rb}, \ P_{\text{к}} = \frac{a}{27b^2} + \label{tk} + \end{eqnarray} + + Связывая формулы \ref{ti} и \ref{tk}, получим: + \begin{equation} + T_i = \frac{27}{4}T_{\text{к}} + \end{equation} + + \section{Экспериментальная установка} + \begin{figure}[h] + \centering + \includegraphics[scale=3]{stand.png} + \caption{Схема экспериментальной установки для изучения эффекта Джоуля-Томсона} + \label{stand} + \end{figure} + Схема используемой установки приведена на рис. \ref{stand}. Основным + элементом установки является трубка 1 с пористой перегородкой 2, через + которую пропускается исследуемый газ. Трубка имеет длину $L = 80$ мм и + сделана из нержавеющей стали в силу ее малой теплопроводности. Диаметр + трубки $d = 3$ мм, толщина стенок 0.2 мм. Толщина трубки $l = 5$ мм + подобрана так, чтобы обеспечить оптимальный поток газа при перепаде + давлений $\Delta P \le 4$ атм, при этом в результате эффекта Джоуля-Томсона + создается достаточная разность температур. + \par Давление газа измеряется измеряется манометром М и регулируется + вентилем В. Манометр М измеряет разность с атмоферным давлением + $\Delta P = P_1 - P_2$. + \par Разность температур газа до перегородки и после нее измеряется + дифференциальной термопарой медь -- константан. + + \section{Ход работы} + \begin{enumerate} + \item Убедимся, что термостат залит водой, все электрические приборы + заземлены. + \item Включим термостат. + \item Включим вольтметр 7. Получим показания вольтметра при $\Delta P = 0$, + используем ее для корректировки: $\mathscr{E} = U(P) - U(0)$. + \item Проведем измерения при температурах $T_1 = 17$ \textdegree C, + $T_2 = 30$ \textdegree C, $T_3 = 40$ \textdegree C, $T_4 = 50$ \textdegree C. + Полученные данные представим в таблице \ref{tabT}. + \begin{table}[h] + \centering + \begin{tabular}{|c|c|c|c|c|c|c|} + \hline + $P$, A & 4.0 & 3.5 & 3.0 & 2.5 & 2.0 & 1.5 \\ \hline + $U_1$, мкВ & 136 & 105 & 85 & 64 & 44 & 29 \\ \hline + $U_2$, мкВ & 107 & 89 & 70 & 49 & 34 & 18 \\ \hline + $U_3$, мкВ & 101 & 80 & 63 & 43 & 27 & 15 \\ \hline + $U_4$, мкВ & 94 & 73 & 56 & 41 & 26 & 13 \\ \hline + $\Delta T_1$, K & 3.42 & 2.64 & 2.14 & 1.61 & 1.11 & 0.73 \\ \hline + $\sigma_{\Delta T_1}$, K & 0.06 & 0.05 & 0.05 & 0.05 & 0.05 & 0.05 \\ \hline + $\Delta T_2$, K & 2.58 & 2.14 & 1.69 & 1.18 & 0.82 & 0.43 \\ \hline + $\sigma_{\Delta T_2}$, K & 0.05 & 0.05 & 0.05 & 0.05 & 0.05 & 0.05 \\ \hline + $\Delta T_3$, K & 2.38 & 1.89 & 1.49 & 1.01 & 0.64 & 0.35 \\ \hline + $\sigma_{\Delta T_3}$, K & 0.05 & 0.05 & 0.05 & 0.05 & 0.05 & 0.05 \\ \hline + $\Delta T_4$, K & 2.18 & 1.69 & 1.30 & 0.95 & 0.60 & 0.30 \\ \hline + $\sigma_{\Delta T_4}$, K & 0.05 & 0.04 & 0.04 & 0.04 & 0.04 & 0.04 \\ \hline + \end{tabular} + \caption{Значения $\Delta T(P)$ при разных температурах} + \label{tabT} + \end{table} + \item По результатам измерений построим графики $\Delta T(P)$ на рисунке + \ref{graphT}. + \begin{figure}[h] + \centering + \input{data/T.pgf} + \caption{Графики $\Delta T(\Delta P)$} + \label{graphT} + \end{figure} + + \item Найдем коэффициенты Джоуля-Томсона методом наименьших квадратов. + Погрешности рассчитаем по формулам: + \begin{eqnarray} + \sigma_{\mu}^{\text{случ}} = \sqrt{\frac{1}{N - 1}\left(\frac{\left<\Delta T^2\right>}{\left} + - \mu^2\right)}, \\ + \sigma_{\mu}^{\text{сист}} = \mu\sqrt{\varepsilon_{\Delta T}^2 + \varepsilon_{P}^2}, \\ + \sigma_{\mu} = \sqrt{(\sigma_{\mu}^{\text{случ}})^2 + (\sigma_{\mu}^{\text{сист}})^2} + \end{eqnarray} + \item Результаты для разных температур представим в таблице \ref{tabmu} и + и на графике $\mu(T^{-1})$ (рис. \ref{graphmu}). + \begin{table}[h] + \centering + \begin{tabular}{|c|c|c|c|c|} + \hline + $T$, \textdegree C & $T^{-1}$, $10^{-3}$ K$^{-1}$ & $\mu$, $10^{-5}$ К/Па & $\sigma_\mu$, $10^{-5}$ К/Па & $\varepsilon_\mu$, \% \\ \hline + 17 & 3.45 & 1.06 & 0.05 & 5 \\ \hline + 30 & 3.30 & 0.87 & 0.04 & 5 \\ \hline + 40 & 3.19 & 0.82 & 0.04 & 5 \\ \hline + 50 & 3.10 & 0.74 & 0.03 & 5 \\ \hline + \end{tabular} + \caption{Значения $\mu(T)$} + \label{tabmu} + \end{table} + + \begin{figure}[h] + \centering + \input{data/mu.pgf} + \caption{График $\mu(T^{-1})$} + \label{graphmu} + \end{figure} + + \item По графику \ref{graphmu} и с помощью формулы \ref{mu} найдем $a$ и $b$ + (см. таблицу \ref{tabcoeffs}). + \begin{table}[h] + \centering + \begin{tabular}{|c|c|c|} + \hline + & $a$, $\frac{\text{Па}\cdot\text{м}^6}{\text{K}\cdot\text{моль}^2}$ & $b$, $10^{-4}$ $\frac{\text{м}^3}{\text{моль}}$ \\ \hline + Значение & 1.06 & 5.77 \\ \hline + $\sigma$ & 0.14 & 0.75 \\ \hline + $\varepsilon$, \% & 13 & 13 \\ \hline + \end{tabular} + \caption{Коэффициенты Ван-дер-Ваальса} + \label{tabcoeffs} + \end{table} + + \item По формуле \ref{ti} найдем температуру инверсии для углекислого + газа $T_i = (442\pm115)$ К, $\varepsilon_{T_i} = \varepsilon_a + \varepsilon_b = 26$\%. + + \item Табличные данные для углекислого газа $a = 0.36\ \frac{\text{Па} \cdot \text{м}^6}{\text{К} \cdot \text{моль}^2}$, + $b = 0.43 \cdot 10^{-4} \ \frac{\text{м}^3}{\text{моль}}$, $T_{\text{инв}} = 2000$ K. + \end{enumerate} + + \section{Вывод} + В ходе работы мы выявили экспериментально наличие эффекта Джоуля-Томсона, + показали его линейность с неплохой степенью точности. Вычислив коэффициенты + $a$ и $b$, мы обнаружили расхождение с табличными значениями на целый порядок, + поэтому наш опыт показывает, что модель газа Ван-дер-Ваальса способна описывать + поведение газа лишь при малых отклонениях температуры, в реальности + расхождение слишком велико, чтобы считать ее адекватной. +\end{document} diff --git a/2.1.6/Baldin_V/data/T.csv b/2.1.6/Baldin_V/data/T.csv new file mode 100644 index 00000000..9c51e64a --- /dev/null +++ b/2.1.6/Baldin_V/data/T.csv @@ -0,0 +1,7 @@ +P,U1,U2,U3,U4,T1,dT1,T2,dT2,T3,dT3,T4,dT4 +4.0,136,107,101,94,3.42,0.06,2.58,0.05,2.38,0.05,2.18,0.05 +3.5,105,89,80,73,2.64,0.05,2.14,0.05,1.89,0.05,1.69,0.04 +3.0,85,70,63,56,2.14,0.05,1.69,0.05,1.49,0.05,1.30,0.04 +2.5,64,49,43,41,1.61,0.05,1.18,0.05,1.01,0.05,0.95,0.04 +2.0,44,34,27,26,1.11,0.05,0.82,0.05,0.64,0.05,0.60,0.04 +1.5,29,18,15,13,0.73,0.05,0.43,0.05,0.35,0.05,0.30,0.04 diff --git a/2.1.6/Baldin_V/data/T.pgf b/2.1.6/Baldin_V/data/T.pgf new file mode 100644 index 00000000..c52951c7 --- /dev/null +++ b/2.1.6/Baldin_V/data/T.pgf @@ -0,0 +1,1593 @@ +%% Creator: Matplotlib, PGF backend +%% +%% To include the figure in your LaTeX document, write +%% \input{.pgf} +%% +%% Make sure the required packages are loaded in your preamble +%% \usepackage{pgf} +%% +%% Also ensure that all the required font packages are loaded; for instance, +%% the lmodern package is sometimes necessary when using math font. +%% \usepackage{lmodern} +%% +%% Figures using additional raster images can only be included by \input if +%% they are in the same directory as the main LaTeX file. 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+\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\pgfsetlinewidth{1.505625pt}% +\definecolor{currentstroke}{rgb}{0.750000,0.000000,0.750000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{4.044150in}{1.461481in}}% +\pgfpathlineto{\pgfqpoint{4.044150in}{1.530617in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\pgfsetlinewidth{1.505625pt}% +\definecolor{currentstroke}{rgb}{0.750000,0.000000,0.750000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{3.130850in}{1.159012in}}% +\pgfpathlineto{\pgfqpoint{3.130850in}{1.228148in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\pgfsetlinewidth{1.505625pt}% +\definecolor{currentstroke}{rgb}{0.750000,0.000000,0.750000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{2.217551in}{0.856543in}}% +\pgfpathlineto{\pgfqpoint{2.217551in}{0.925679in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\pgfsetlinewidth{1.505625pt}% +\definecolor{currentstroke}{rgb}{0.750000,0.000000,0.750000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{1.304251in}{0.597284in}}% +\pgfpathlineto{\pgfqpoint{1.304251in}{0.666420in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{1.000000,0.000000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{1.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.020833in}{-0.020833in}}{\pgfqpoint{0.020833in}{0.020833in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathcurveto{\pgfqpoint{0.005525in}{-0.020833in}}{\pgfqpoint{0.010825in}{-0.018638in}}{\pgfqpoint{0.014731in}{-0.014731in}}% +\pgfpathcurveto{\pgfqpoint{0.018638in}{-0.010825in}}{\pgfqpoint{0.020833in}{-0.005525in}}{\pgfqpoint{0.020833in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{0.020833in}{0.005525in}}{\pgfqpoint{0.018638in}{0.010825in}}{\pgfqpoint{0.014731in}{0.014731in}}% +\pgfpathcurveto{\pgfqpoint{0.010825in}{0.018638in}}{\pgfqpoint{0.005525in}{0.020833in}}{\pgfqpoint{0.000000in}{0.020833in}}% +\pgfpathcurveto{\pgfqpoint{-0.005525in}{0.020833in}}{\pgfqpoint{-0.010825in}{0.018638in}}{\pgfqpoint{-0.014731in}{0.014731in}}% +\pgfpathcurveto{\pgfqpoint{-0.018638in}{0.010825in}}{\pgfqpoint{-0.020833in}{0.005525in}}{\pgfqpoint{-0.020833in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{-0.020833in}{-0.005525in}}{\pgfqpoint{-0.018638in}{-0.010825in}}{\pgfqpoint{-0.014731in}{-0.014731in}}% +\pgfpathcurveto{\pgfqpoint{-0.010825in}{-0.018638in}}{\pgfqpoint{-0.005525in}{-0.020833in}}{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathclose% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{5.870749in}{3.328148in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.957449in}{2.654074in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.044150in}{2.221975in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.130850in}{1.763951in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.217551in}{1.331852in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.304251in}{1.003457in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.500000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.500000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.020833in}{-0.020833in}}{\pgfqpoint{0.020833in}{0.020833in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathcurveto{\pgfqpoint{0.005525in}{-0.020833in}}{\pgfqpoint{0.010825in}{-0.018638in}}{\pgfqpoint{0.014731in}{-0.014731in}}% +\pgfpathcurveto{\pgfqpoint{0.018638in}{-0.010825in}}{\pgfqpoint{0.020833in}{-0.005525in}}{\pgfqpoint{0.020833in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{0.020833in}{0.005525in}}{\pgfqpoint{0.018638in}{0.010825in}}{\pgfqpoint{0.014731in}{0.014731in}}% +\pgfpathcurveto{\pgfqpoint{0.010825in}{0.018638in}}{\pgfqpoint{0.005525in}{0.020833in}}{\pgfqpoint{0.000000in}{0.020833in}}% +\pgfpathcurveto{\pgfqpoint{-0.005525in}{0.020833in}}{\pgfqpoint{-0.010825in}{0.018638in}}{\pgfqpoint{-0.014731in}{0.014731in}}% +\pgfpathcurveto{\pgfqpoint{-0.018638in}{0.010825in}}{\pgfqpoint{-0.020833in}{0.005525in}}{\pgfqpoint{-0.020833in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{-0.020833in}{-0.005525in}}{\pgfqpoint{-0.018638in}{-0.010825in}}{\pgfqpoint{-0.014731in}{-0.014731in}}% +\pgfpathcurveto{\pgfqpoint{-0.010825in}{-0.018638in}}{\pgfqpoint{-0.005525in}{-0.020833in}}{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathclose% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{5.870749in}{2.602222in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.957449in}{2.221975in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.044150in}{1.833086in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.130850in}{1.392346in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.217551in}{1.081235in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.304251in}{0.744198in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,1.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.020833in}{-0.020833in}}{\pgfqpoint{0.020833in}{0.020833in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathcurveto{\pgfqpoint{0.005525in}{-0.020833in}}{\pgfqpoint{0.010825in}{-0.018638in}}{\pgfqpoint{0.014731in}{-0.014731in}}% +\pgfpathcurveto{\pgfqpoint{0.018638in}{-0.010825in}}{\pgfqpoint{0.020833in}{-0.005525in}}{\pgfqpoint{0.020833in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{0.020833in}{0.005525in}}{\pgfqpoint{0.018638in}{0.010825in}}{\pgfqpoint{0.014731in}{0.014731in}}% +\pgfpathcurveto{\pgfqpoint{0.010825in}{0.018638in}}{\pgfqpoint{0.005525in}{0.020833in}}{\pgfqpoint{0.000000in}{0.020833in}}% +\pgfpathcurveto{\pgfqpoint{-0.005525in}{0.020833in}}{\pgfqpoint{-0.010825in}{0.018638in}}{\pgfqpoint{-0.014731in}{0.014731in}}% +\pgfpathcurveto{\pgfqpoint{-0.018638in}{0.010825in}}{\pgfqpoint{-0.020833in}{0.005525in}}{\pgfqpoint{-0.020833in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{-0.020833in}{-0.005525in}}{\pgfqpoint{-0.018638in}{-0.010825in}}{\pgfqpoint{-0.014731in}{-0.014731in}}% +\pgfpathcurveto{\pgfqpoint{-0.010825in}{-0.018638in}}{\pgfqpoint{-0.005525in}{-0.020833in}}{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathclose% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{5.870749in}{2.429383in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.957449in}{2.005926in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.044150in}{1.660247in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.130850in}{1.245432in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.217551in}{0.925679in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.304251in}{0.675062in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.750000,0.000000,0.750000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{0.750000,0.000000,0.750000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.020833in}{-0.020833in}}{\pgfqpoint{0.020833in}{0.020833in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathcurveto{\pgfqpoint{0.005525in}{-0.020833in}}{\pgfqpoint{0.010825in}{-0.018638in}}{\pgfqpoint{0.014731in}{-0.014731in}}% +\pgfpathcurveto{\pgfqpoint{0.018638in}{-0.010825in}}{\pgfqpoint{0.020833in}{-0.005525in}}{\pgfqpoint{0.020833in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{0.020833in}{0.005525in}}{\pgfqpoint{0.018638in}{0.010825in}}{\pgfqpoint{0.014731in}{0.014731in}}% +\pgfpathcurveto{\pgfqpoint{0.010825in}{0.018638in}}{\pgfqpoint{0.005525in}{0.020833in}}{\pgfqpoint{0.000000in}{0.020833in}}% +\pgfpathcurveto{\pgfqpoint{-0.005525in}{0.020833in}}{\pgfqpoint{-0.010825in}{0.018638in}}{\pgfqpoint{-0.014731in}{0.014731in}}% +\pgfpathcurveto{\pgfqpoint{-0.018638in}{0.010825in}}{\pgfqpoint{-0.020833in}{0.005525in}}{\pgfqpoint{-0.020833in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{-0.020833in}{-0.005525in}}{\pgfqpoint{-0.018638in}{-0.010825in}}{\pgfqpoint{-0.014731in}{-0.014731in}}% +\pgfpathcurveto{\pgfqpoint{-0.010825in}{-0.018638in}}{\pgfqpoint{-0.005525in}{-0.020833in}}{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.020833in}}% +\pgfpathclose% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{5.870749in}{2.256543in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.957449in}{1.833086in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.044150in}{1.496049in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.130850in}{1.193580in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.217551in}{0.891111in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.304251in}{0.631852in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetrectcap% +\pgfsetmiterjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{0.875000in}{3.520000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetrectcap% +\pgfsetmiterjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{6.300000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{3.520000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetrectcap% +\pgfsetmiterjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{0.440000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetrectcap% +\pgfsetmiterjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.875000in}{3.520000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{3.520000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetmiterjoin% +\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetfillopacity{0.800000}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{0.800000,0.800000,0.800000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetstrokeopacity{0.800000}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.972222in}{2.634198in}}% +\pgfpathlineto{\pgfqpoint{2.034083in}{2.634198in}}% +\pgfpathquadraticcurveto{\pgfqpoint{2.061861in}{2.634198in}}{\pgfqpoint{2.061861in}{2.661976in}}% +\pgfpathlineto{\pgfqpoint{2.061861in}{3.422778in}}% +\pgfpathquadraticcurveto{\pgfqpoint{2.061861in}{3.450556in}}{\pgfqpoint{2.034083in}{3.450556in}}% +\pgfpathlineto{\pgfqpoint{0.972222in}{3.450556in}}% +\pgfpathquadraticcurveto{\pgfqpoint{0.944444in}{3.450556in}}{\pgfqpoint{0.944444in}{3.422778in}}% +\pgfpathlineto{\pgfqpoint{0.944444in}{2.661976in}}% +\pgfpathquadraticcurveto{\pgfqpoint{0.944444in}{2.634198in}}{\pgfqpoint{0.972222in}{2.634198in}}% +\pgfpathlineto{\pgfqpoint{0.972222in}{2.634198in}}% +\pgfpathclose% +\pgfusepath{stroke,fill}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{1.505625pt}% +\definecolor{currentstroke}{rgb}{1.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{1.000000in}{3.346389in}}% +\pgfpathlineto{\pgfqpoint{1.138889in}{3.346389in}}% +\pgfpathlineto{\pgfqpoint{1.277778in}{3.346389in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=1.388889in,y=3.297778in,left,base]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle T = 17\) \textdegree C}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{1.505625pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.501961,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{1.000000in}{3.152716in}}% +\pgfpathlineto{\pgfqpoint{1.138889in}{3.152716in}}% +\pgfpathlineto{\pgfqpoint{1.277778in}{3.152716in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=1.388889in,y=3.104105in,left,base]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle T = 30\) \textdegree C}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{1.505625pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,1.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{1.000000in}{2.959043in}}% +\pgfpathlineto{\pgfqpoint{1.138889in}{2.959043in}}% +\pgfpathlineto{\pgfqpoint{1.277778in}{2.959043in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=1.388889in,y=2.910432in,left,base]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle T = 40\) \textdegree C}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetrectcap% +\pgfsetroundjoin% 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b/2.1.6/Baldin_V/data/mu.csv new file mode 100644 index 00000000..d817dadb --- /dev/null +++ b/2.1.6/Baldin_V/data/mu.csv @@ -0,0 +1,5 @@ +"$T$, K","$1/T$, $10^3$ K$^{-1}$",$\mu$,$\Delta\mu$,eps_mu +17,3.45,1.06,0.05,5 +30,3.30,0.87,0.04,5 +40,3.19,0.82,0.04,5 +50,3.10,0.74,0.03,5 diff --git a/2.1.6/Baldin_V/data/mu.pgf b/2.1.6/Baldin_V/data/mu.pgf new file mode 100644 index 00000000..3a936fe4 --- /dev/null +++ b/2.1.6/Baldin_V/data/mu.pgf @@ -0,0 +1,877 @@ +%% Creator: Matplotlib, PGF backend +%% +%% To include the figure in your LaTeX document, write +%% \input{.pgf} +%% +%% Make sure the required packages are loaded in your preamble +%% \usepackage{pgf} +%% +%% Also ensure that all the required font packages are loaded; for instance, +%% the lmodern package is sometimes necessary when using math font. +%% \usepackage{lmodern} +%% +%% Figures using additional raster images can only be included by \input if +%% they are in the same directory as the main LaTeX file. For loading figures +%% from other directories you can use the `import` package +%% \usepackage{import} +%% +%% and then include the figures with +%% \import{}{.pgf} +%% +%% Matplotlib used the following preamble +%% +%% \makeatletter\@ifpackageloaded{underscore}{}{\usepackage[strings]{underscore}}\makeatother +%% +\begingroup% +\makeatletter% +\begin{pgfpicture}% +\pgfpathrectangle{\pgfpointorigin}{\pgfqpoint{7.000000in}{4.000000in}}% +\pgfusepath{use as bounding box, clip}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetmiterjoin% +\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.000000pt}% +\definecolor{currentstroke}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{7.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{7.000000in}{4.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{4.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathclose% +\pgfusepath{fill}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetmiterjoin% +\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.000000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetstrokeopacity{0.000000}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{3.520000in}}% +\pgfpathlineto{\pgfqpoint{0.875000in}{3.520000in}}% +\pgfpathlineto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathclose% +\pgfusepath{fill}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{0.803000pt}% 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+\endgroup% diff --git a/2.1.6/Baldin_V/pictures/stand.png b/2.1.6/Baldin_V/pictures/stand.png new file mode 100644 index 00000000..594160f3 Binary files /dev/null and b/2.1.6/Baldin_V/pictures/stand.png differ diff --git a/2.1.6/pdf/Baldin_V.pdf b/2.1.6/pdf/Baldin_V.pdf new file mode 100644 index 00000000..26f6a466 Binary files /dev/null and b/2.1.6/pdf/Baldin_V.pdf differ diff --git a/2.2.6/Baldin_V/2.2.6.ipynb b/2.2.6/Baldin_V/2.2.6.ipynb new file mode 100644 index 00000000..aeee5737 --- /dev/null +++ b/2.2.6/Baldin_V/2.2.6.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib\n", + "from matplotlib import pyplot as plt\n", + "from scipy.optimize import minimize\n", + "from matplotlib.backends.backend_pgf import FigureCanvasPgf\n", + "matplotlib.backend_bases.register_backend('pdf', FigureCanvasPgf)\n", + "\n", + "matplotlib.rcParams.update({\n", + " \"pgf.texsystem\": \"pdflatex\",\n", + " 'text.usetex': True,\n", + " 'font.family': 'serif',\n", + " 'pgf.rcfonts': False,\n", + "})\n", + "\n", + "def read_files(files):\n", + " data = []\n", + " for f in files:\n", + " data += [pd.read_csv(f, sep=',', skipinitialspace=True)]\n", + " return data\n", + "\n", + "[balls, full] = read_files(['data/balls.csv', 'data/full.csv'])\n", + "balls['d'] = (balls['d1'] + balls['d2'] + balls['d3']) / 3\n", + "balls.to_csv('data/balls.csv', float_format='%.2f', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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NJrLpW25uLmz8S8Q8+mAeEvOQmIdkqTw0DaitBe66C9i1S7W53erxQCtWxLRXl6XySIJ48oi19uCZLxPKyckxugtphXlIzENiHhLzkEyfRzgMNDaqvbq8XtU2caJ66PXKlcCoUSN6O9PnkWR65cHVjiYTDAZRX1/PmyJ7MA+JeUjMQ2IekunzaG5Wj/25/HJVeOXmqodet7UBN9004sLL9HkkmZ558MwXERGRmbS2qsuLdXXq9ahRqti64w5g/Hhj+0YxYfFFRERkBvv3A6tWAb/4BRAKqZvnr7tOtU2danTvaARYfBEREaWzI0fUFhFPPAEcP67avvQltVfXeecZ2zeKC1c7mkw4HEYwGITT6eRqFDCPvpiHxDyktM7j2DHglFPU1x99BJx8su4/Mq3zAIB//hN48klVePn9qu2ii9Trz3426T8u7fNIsXjy4OOFLKyrq8voLqQV5iExD4l5SMxDSss8gkHgueeAGTPUMxf9fnWG6/e/Vw/C1qHwikjLPAykVx4svkwmGAyiqamJq1F6MA+JeUjMQ2IeUtrlEQ6rm+jPPRcoKQEOHFD3cv3qV8DWrcCVV+q6SWra5WEwPfPgPV9ERERGW79eneVqblavx49XKxq/+U0gO9vYvlHSsfgiIiIyiterNkhtaFCvTz4Z+MEP1P8sdL8ySSy+TMjp5MfWG/OQmIfEPCTmIRmWx65d6szW6tXqdVaWOst1553AaacZ0ydwfvSlVx5c7UhERMYzYLWjIT74QD30+rnn1I31NhtwzTWqze02uneUIK52tChN03Dw4EFommZ0V9IC85CYh8Q8JOYhpTSPDz9UZ7qmTQOefVYVXosXA1u2AL/+dVoUXpwfkp55sPgymVAohI0bNyIUChndlbTAPCTmITEPiXlIKcnj+HHgRz9SxdVDD6m9uy68EPjTn4C1a4Hzz9fvZ48Q54ekZx68uEtERJRsoZDaIuLee4G9e1XbrFnAww8DV12l65YRlP5YfBERESVLOAz87nfqIdfvvKPaJk9Wz1+87jrA4TC2f5QWWHyZjM1mQ25uLh/90IN5SMxDYh4S85CSnseGDWqvro0b1eu8PFWE3XQTcNJJyfkZOuL8kPTMg6sdiYjIeGZe7bhtm9qrq75evR49Gvj+94FbbwXGjDG2b5RSXO1oUZqmYffu3VyN0oN5SMxDYh4S85ASzuO994BrrwVmz1aFl9MJfOtbag+vBx80XeHF+SHpmQeLL5MJhULYunUrV6P0YB4S85CYh8Q8pLjzOHgQ+M53gPx8tU1EOAysWAG8+y7wzDPAJz6hT4d1xvkh6ZkH7/kiIiKKRWen2jbiRz9Sl0YB4LLL1ArGuXON7RuZCosvIiKioXR3A9XV6lLioUOqragIeOQRYMECY/tGpsTiy2RsNhsmTJjA1Sg9mIfEPCTmITEPadg8QiHgpZeAe+4B3n9ftZ1zjtosdckSy+3Vxfkh6ZkHVzsSEZHx0mm1YzisbqC//XbgzTdV2xlnAPfdB/zbv6kb64kGwNWOFhUKhbB9+3beENmDeUjMQ2IeEvOQBszj9deB//f/gCuvVIWXy6UuL+7cCaxcaenCi/ND0jMPFl8mo2kaWltbuRS4B/OQmIfEPCTmIYk83n5bPfbns58F/vIXtSlqWRng8wHl5WrvLovj/JD0zMO6JTwREdEwcg4ehOPGG09sGeFwADfcoO7zmjTJ6O6RRbH4IiKizNPeDvuDD+LSp5+GPRhUbUuWqBWNM2ca2zeyPBZfJmO32zFlyhTY7bxiDDCPvpiHxDwk5gF1M//jjwOPPgrH0aMAgPDFF8NWWQl85jPG9s1gnB+SnnlwtSMRERmv92rH1la1pUMyffwx8NxzwAMPAP/4h2qbM0fdTL9woeW2jSBjcLWjRYVCIWzZsoWrUXowD4l5SMxDSus8XnjhxNezZgHPP5+c99U04OWX1Xv++7+rwmvaNODllxF64w1smTABId5gDiDN54cB9MzDkOLL6/XC6/UCAHw+X/TrRI/NBJqmYc+ePVyN0oN5SMxDYh5S2uaxbx9w880nXmsaUFqq2uMVDgPr1gGFhcA116hVi6edBjz9NPDOO8BXvgINSM88DJK288MgeuZhSPFVXV2NuXPnwmazobS0FG63OynHEhGRCe3cqQqu3kIhYNeu+N7vjTfUY38WLQK2bAFOPVXdSL9rF/DtbwOjRiXeZ6IEGHLD/dy5c3HkyBEAgMvlStqxRERkQjNmAHa7LMAcDmD69JG9z/btwJ13Aq+8ol5nZwM33aR2qh8/Pnn9JUqQYasdR1JIxXJsd3c3uru7o6+P9qxiCQQCCAQCANTKBYfDgVAoJE4jRtqDwSB6rz9wOByw2+2DtkfeN8LZs/NxMLJseZj2rKwsaJomrifbbDY4nc5B28PhMKZPnx79nhXGNFjfYxlTKBTCOeecY6kx9e77SMcUCoXE/LDCmBL5nDRNQ35+PgCI9zfzmBL5nHrPj7Qa0+mnw/nkk6pQAhB2OBB65hng9NPhBIb/nPbtg+PBB2H75S9h0zSE7XaEr70WobvvBqZMUX3v6UvfNWb5+fn97ukx+nMyau71nh9WGVNvIx2T3W7HjBkzRHssY4qFIcWX3+9HXV0dAKC5uXnIy4mxHltRUYFVq1b1a29oaMDonp2Jp0yZgjlz5mDbtm3Ys2dP9Jj8/HzMnDkTmzZtwqHIE+sBzJ49G1OnTsWGDRvQ2dkZbZ83bx4mTpyIhoYGEfQll1yCnJwc1NfXiz4sXrwYXV1daGpqirY5nU5cccUVaG9vx8aNG6Ptubm5WLBgAfbu3YutW7dG2ydMmID58+fD5/Nh165d2NVzOt4KY9q5cydaW1uj7fGMyeFwYP369ZYaUzyf07p16wAgOj+sMKZkfE67d++23JgS+Zx27dqVfmNaujRafG28804cOu00TNi0acgxvf3nPyPnpz+Fu74e9o8/Vt+86ipsXboUe3JzgbfeAt56a8gxzZw5E2vXrk3LzwkwZu69//77lhtTvJ+T3W6P/l6NZUwtLS2IhSFbTfj9/ujZLK/Xi2XLlqGtrS2hYwc68zV58mS0t7dHl3uavQp3Op3o7u5Gc3Mz5s6dC6fTaYkxJfKvpWAwCK/XiwsuuAAALDGm3n0f6ed0/PhxtLS0ROeHFcaUyOcUCoXg9XpRWFgIW6+tBMw8pkQ+p48//jg6P0aNGpVeY3rySeCWWwAAYbsdoWefBW64YeAxHT8Ox9NPI1xZCZvfDwDQLroI4YcfhuNzn4t5TOFwGJs3b8acOXOi/U3qmEz29ykYDEbnR05OjiXG1NtIPycAeOONN1BQUBB9z+HG1NHRgXHjxg271YQhZ758Ph8KCgoAAG63Gz6fDz6fb8AzWrEem52djezs7H5/PisrC1lZWaLN4XDA4XD0O7b3X75Y2vu+bzztdrt9wA3chmo/fPgwnE6neD8zj2mwvsc6pvb2doTD4aT0fbD2VI9puPbB+u50OgecH2YeU6Kf06FDh2Cz2QY83qxjGqp9qDH1nh+RY9JiTPv2AT/8YfSlTdPg/Pa3gcWLgUmTTowpEAD+4z+AVauAAwdgA4BPfxqoqID9C1+I7tUV65gCgQAOHTrU7+9LUsY0THu6zr3I/Biq72YbU28jGVMgEEB7e/uA82OkY+r382I6Kom8Xi8uvfTSfu15eXkJHUtERCY13GrHcBiorQXOO09tQXHgAHDWWcCLL6rVjIsXc5NUMpWUF19utxuVlZXR1x6PB0uXLhWXFn0+X0zHEhGRBURWO/YWWe34xz+qx/4sXw7s2AFMmAA88YRa2fj1r/f/c0QmkPLLji6XC4WFhaiqqoLL5UJbWxtqa2uj36+oqEBRURHKysqGPTYTORyO6A3mxDz6Yh4S85DSNo9Jk4ClS4E1a060LVoE3HAD0NioXp9yiro0ecstQG5uUn5s2uZhEOYh6ZkHn+1IRETG2rcPmDq1/6VHAMjKAr71LbV/18SJqe8b0Qjw2Y4WFQwGsX79+pj3ErE65iExD4l5SGmbx0D3fAHqgdetrcBPf6pL4ZW2eRiEeUh65mHYJqsUn3A4jM7Ozn4bBWYq5iExD4l5SGmZh98P/OY3/dvtduAXv1CXJHWSlnkYiHlIeubB4ouIiFKvq0s95Prhh4GeR8hFORxAdbWuhReRkXjZkYiIUicYBJ5/HjjnHODWW1Xh9clPAqtXnzjmnXeAG280ro9EOuMN9yajaRra29sxfvz4ATeKyzTMQ2IeEvOQDM0jHAb+53/UjfPvvqvaJk8G7r8fuPZa4PhxtaIRAD76CDj5ZN27xPkhMQ8pnjxirT1YfBERkb7+9CfgttuAv/1Nvc7LU0XYt78NnHSSajt2LOXFF1GycbWjRQUCAaxdu7bfM64yFfOQmIfEPKSU57F1K/CFLwAXX6wKr9GjgbvuAnw+tV9XpPAyCOeHxDwkPfNg8WVCXAYsMQ+JeUjMQ0pJHj4f8LWvAXPmAK+9Bjid6ixXWxvwwAPAmDFD//n9+/XvYw/OD4l5SHrlweKLiIiS4x//AG6+GZg5E3jpJdX2la+oe7yefho4/fTB/+wLL5z4etYsdVM+kUWx+CIiosQcPQrccw8wbRrw1FNAIABcfjng9QIvv6ye0TiUfftU0RahaeoB2vv26dtvIoPwhnuTiWz6lpubC5vNZnR3DMc8JOYhMQ8p6Xl0dwPPPgs89BDQ3q7aPvMZ4JFHgEsuif19mpqABQsGbr/44sT7OQjOD4l5SPHkEWvtwU1WTSgnJ8foLqQV5iExD4l5SEnJIxQC/vM/1dmu3btVW36+KsK+/GVgpP/hnjFD7Wjf+xFDDsfwZ8ySgPNDYh6SXnnwsqPJBINB1NfX86bIHsxDYh4S85ASziMcBn7/e2D2bOD661XhdeaZwHPPAW+9BSxZMvLCC1A72T/55InXKdrhnvNDYh6Snnmw+CIiouH99a/A5z4HfOlLqtAaOxaoqlIPxf7GN9SKxkRcf/2Jr7nDPVkcLzsSEdHg3noLuOMOdcYLAHJygO9+FygrUwWYHs48U5/3JUoTLL6IiKi/3bvVPV0vvqguNzoc6mzUvfcCZ5xhdO+ITI2rHU0mHA4jGAzC6XRyNQqYR1/MQ2IeUkx5tLerG+efeQb4+GPVtmyZ2hw1P1+/zhnweCHOD4l5SPHkwccLWVhXV5fRXUgrzENiHhLzkAbN46OPVIHldgOPP64Kr0svBTZtAtas0bfwMhDnh8Q8JL3yYPFlMsFgEE1NTVyN0oN5SMxDYh7SgHl8/LHaGHXaNHWZsbMTKCgAGhoAjwcoKjKuwzrj/JCYh6RnHrzni4goE2ka8F//Bdx9t3oWI6D21XroIWDpUrXvFhHpgsUXEVEmCYdhe+01VXT9/e+q7fTT1Y30N94IZGUZ2z+iDMDiy4Scie6nYzHMQ2IeEvM4wfbGG7jo7rvhfOst1XDqqUB5udo6IgU3uKcjzg+JeUh65cHVjkREVvfuu8CddwL//d/qdXa2epD1bbcB48YZ27cIA1Y7EiUbVztalKZpOHjwILTez0DLYMxDYh5Sxuexd6+6lHjeecB//zfCdju6vvpVaK2twKOPpk/hZZCMnx99MA9JzzxYfJlMKBTCxo0bEQqFjO5KWmAeEvOQMjaPw4eBW29VD6z+xS/UzfVXX42g14uGFSsQ4iapADJ4fgyCeUh65sGLu0REVnHsGPDTn6pnLn74oWr7/OeBRx4BLrwQCASA9983tItExOKLiMj8AgHg+eeBVauADz5QbeefD1RUAIsWAdytnCitsPgyGZvNhtzcXD76oQfzkJiHZPk8NA2oqwPuugvYuVO1nX222qn+q1/tt1eX5fMYIeYhMQ9Jzzy42pGIyIwaG4HbbwdaWtTriRPV3l0lJcCoUcb2LR5c7UgWwNWOFqVpGnbv3s3VKD2Yh8Q8JEvmsXkzUFwMXHaZKrxOOUVdbty1C/j3fx+y8LJkHglgHhLzkPTMg8WXyYRCIWzdupWrUXowD4l5SJbKY8cOYPly9azFP/5RFVnf/a56NNA99wC5ucO+haXySALmITEPSc88eM8XEVE6O3BAndl6/nkgFFI3z197rWo76yyje0dEcWDxRUSUjvx+oLJSbR3R1aXarrwSePhh4FOfMrRrRJQYFl8mY7PZMGHCBK5G6cE8JOYhmTKPri7gqafUNhFHjqi2z35W7dV10UUJvbUp89AR85CYh6RnHlztSESUDoJB4Je/BO67D9i/X7Wde64qwq680vp7dXG1I1kAVztaVCgUwvbt23lDZA/mITEPyRR5hMPAK6+oS4krV6rCa8oUVYj9/e/AF7+YtMLLFHmkEPOQmIekZx4svkxG0zS0trZyKXAP5iExDynt82hqUo/9WbIE2L5dPej6Jz9RKxuvvx5wOJL649I+jxRjHhLzkPTMg/d8ERGl2pYtaoPUdevU65NPBm65BfjhD4FMvU3i5JPVWUCiDMDii4goVdra1C70L7+sXmdlAaWl6vFAp51mbN+IKGXiuuz4m9/8BoWFhVixYgV+/vOf4/33309yt2gwdrsdU6ZMgd3OK8YA8+iLeUhpk8cHH6jd52fOPFF4XXONutT45JMpK7zSJo80wTwk5iHpmUdcqx2fe+45LF++HJs3b0ZjYyM8Hg/ee+89FBcXY+HChSguLsZZBm/+x9WORGS4o0eBRx9V93EdO6baFi1SKxhnzza0a0SUfLqudszLy8OYMWNw6aWX4pFHHsHmzZtx+PBhlJSUYNeuXSgvL4+74zS0UCiELVu2cDVKD+YhMQ/JsDyOH1cFl9sNPPigKrwuuEDdYP/qq4YVXpwfEvOQmIekZx5xFV8FBQVYv359v/ZIMbZ69eqEO0YD0zQNe/bs4WqUHsxDYh5SyvMIhdQWEfn56gb6w4fVpcZXXgE2bgQuvjg1/RgE54fEPCTmIemZR1w33FdWVsLj8cBms4lLjby8R0QZKRwGfv974I47gLffVm1nnqmev3j99YCTa5uI6IS4znzNnTsXu3btwubNm1FcXIyGhgZceumlye4bEVH6+/Of1WN/rrpKFV5jx6r7vHbuBG68kYUXEfUT12+F5cuX45VXXkFxcTGWLFmCJUuWJLtfNAi73Y78/HyuRunBPCTmIemax5tvqr261q5Vr3NygO99DygrA1yu5P+8JOD8kJiHxDwkPfPgsx2JiEbi/feBe+4Bfv1rdbnR4QC+8Q3VdsYZRveOiAzEZztaVDAYxOuvv45gMGh0V9IC85CYh5TUPA4dAr77XeCcc4AXX1SF1/LlwDvvAD/7mSkKL84PiXlIzEPSM4+kFV/caDU1wuEwDh06BIuesBwx5iExDykpeXR2qhvn3W7giSeAQAAoLgaam4HVq1UxZhKcHxLzkJiHpGcecd8JunXrVnR0dERfV1dXc4sJIrKO7m6gpgZ44AF11gsA5s4FHnlEFV9ERHGK+4Z7v98PV6+bSrds2ZKsPhERGUfTgJdeUs9gjJzRnzEDeOghYMkSgDcjE1GC4iq+Fi5ciJUrV4q23/zmN0npEA3N4XBg9uzZcDgcRnclLTAPiXlII8ojHFa7z99+O7Btm2r7xCeAe+8FbrhBPQTb5Dg/JOYhMQ9JzzziKr6mTZsWUxsln91ux9SpU43uRtpgHhLzkGLOY+NG4LbbgA0b1OsxY4DycnWD/ejR+nYyhTg/JOYhMQ9JzzziOn/e1taGFStW4LHHHsNjjz2GRx99tN+ZMNJHMBjE+vXruRqlB/OQmIc0bB7vvANcfTUwf74qvLKzgVtvBXw+dQbMQoUXwPnRF/OQmIekZx5xnfmqrq5GcXGxWAEwktUAXq8XgHpGpM/ng9/vR0FBwYDH+nw+1NXVwe12w+fzoaSkRNxrlmnC4TA6Ozu5GqUH85CYhzRoHnv3qsuJL7yg7vGy24F/+zfgvvuASZMM6WsqcH5IzENiHpKeecT9bMe+jxMqHsHqn+rqatTU1ET/XG1t7aDHLlu2DC0tLQBUIbZy5cohjyciGtThw0BFBfDUU2o1IwB8+cvqZvqZM43tGxFljLiKr4Ge42iz2WL+83PnzsWRI0cAYMizWD6fT7x2u93weDwx/xwiIgDAsWPA008DVVXA0aOq7eKL1bYRF1xgaNeIKPPEVHz9/Oc/H/L7R44cwZo1a9Dc3BzzD47l0qHH40FeXp5oy8vLg9frHfQypdU5HA7MmzePq1F6MA+JeUgOTcOC1lY4v/lN4IMPVOPs2ers1+WXAyP4R6MVcH5IzENiHpKeecRUfP3sZz/DihUrhjxmJNdE/X4/6urqAADNzc0oLS2F2+0e8LiB9N7cNaK7uxvdkcsIUM9XAoBAIIBAIABArVxwOBwIhULQNC16bKQ9GAyKcTgcDtjt9kHbI+8b4XSqOPvenDdYe1ZWFjRNQygUirbZbDY4nc5B28PhMMaOHYtQKIRQKGSJMQ3W91jHNH78eMuNKd7PKRQKiflhhTHF9TlpGmx1dXDcdx9yd+0CAITdboTuuw/h5cthczjgtNnMNaYeiX5OkfkRDoctM6a+7SMZ08SJExEIBMTPNfuYEvmcIvPDbrdbZkwR8XxO48aNi/4+jXVMsYip+BroHq++RnLPV++b5t1uNxYuXIi2traY//xARVlFRQVWrVrVr72hoQGje1YsTZkyBXPmzMG2bduwZ8+e6DH5+fmYOXMmNm3ahEORnawBzJ49G1OnTsWGDRvQ2dkZbZ83bx4mTpyIhoYGEfQll1yCnJwc1NfXiz4sXrwYXV1daGpqirY5nU5cccUVaG9vx8aNG6Ptubm5WLBgAfbu3YutW7dG2ydMmID58+dj+/bt2NXzHxOrjGnnzp1obW2Ne0x2ux2LFi3Cn//8Z8uMyYqfU0rGdOgQdj37LD754otw9dy20O1yofN738Pr556LcFYW8Npr5hqTFT+nNBlTUVFRdINwq4zJip+TUWMqKirCq6++KgrB4cYUuUd9OLawAcsael829Pv9GDt2LNra2vqd/aqpqUF1dbUYzNixY1FbW9uv2BvozNfkyZPR3t4efbK4Farw48ePY926dVi4cCGysrIsMaZE/rUUCATQ2NiIxYsXw2azWWJMvfs+0s+pq6sLjY2N0flhhTHF/Dlt3YrwbbfBtn49ACCcm4vg97+PdbNmYeG//AvsvXamN82Ykvw5dXd3R+dHdna2JcaUyOekaRpee+216N8XK4wpkc8p8vt04cKFGD16tCXG1NtIP6dwOIz6+noxP4YbU0dHB8aNG4cPP/wwWnsMJO5nO8bL6/Xi0ksvjd5wH9H33i5AnU2rrq7u115YWNivLTs7G9nZ2f3as7KyxF8qQH2QA13DjXxgsbb3fd942u12u/iPwnDtkX73HZfZxzRQ3zmm+MfUd36YbkzHjgGjRqn21tYBH14dPb61FbjrLqCuDjZA/bmbboLtjjuAMWMQqq+H3W4fMAOjP6dY2pP5OfWeH5FjzD6mRD6nyH/MB/rvxGB9H6w9XcY0VB9jbe9daFhlTBEjGdNQ82OkY+r382I6KoncbjcqKyujrz0eD5YuXRq9DOn1eqOrHPueCfP5fCgsLMzofb6IMsILL5z4etYs4Pnn+x+zfz9QUgKcey5QV6dunr/+emDHDuDHPwbGj09df4mIRsCwy44ejwculwttbW2iGFu2bBmKiopQVlYGQBVc1dXVKCoqQnNzM26//faYiq+jR49izJgxw576M5vIpm+5ubkj2t7DqpiHZIk89u0Dpk5Vm59GOBzqIdeTJgFHjgCVlcBPfwocP66+/6Uvqb26zjtPvJUl8kgi5iExD4l5SPHkEWvtYUjxlQpWLr6CwSCcTif/coB59GXqPI4dA045ZfDvv/oq8Pe/q725IotuLrpIvf7sZwf8I6bOQwfMQ2IeEvOQ4skj1toj5ZcdKTHBYBD19fV89lYP5iFZJo++v+jsduBf/1U9/NrvV2e4fv979TzGQQovwEJ5JAnzkJiHxDwkPfNI+Q33RETD6ntCXtOAf/xDXY584AHgmmvUpUgiIhNi8UVE6WH//sG/N368WtH4zW8CA6xqJiIyExZfRJQeem0eLFx3HfDkk4CF7t0koszGG+5NhjdESsxDMm0eu3YB3/mOuqm+t96rHONg2jx0wjwk5iExD4k33JPQ1dVldBfSCvOQTJXHBx8A3/622stroMKrujruwivCVHmkAPOQmIfEPCS98mDxZTLBYBBNTU1cjdKDeUimyePDD9U9XNOmAc8+CwSDwGWXyWPeeQe48caEfoxp8kgR5iExD4l5SHrmweKLiFLn+HHgRz8C3G61Keo//wlceCHwv/8LvPKKPPbMMw3pIhGR3njDPRHpLxQCfvUr4N57gb17VdusWcDDDwNXXaX29Tp2zNg+EhGlCM98mVCsD+7MFMxDSqs8wmHgt78FPv1p4IYbVOE1aZJ6VuO2bcDVV/ffUDViqK0nRiCt8kgDzENiHhLzkPTKg6sdiUgfGzaoHek3blSv8/KAO+4AbroJOOmk/sc/84z6XoTNBjz3XML3fRERpQpXO1qUpmk4ePAgtN4PHc5gzENKizy2bQOuuAL4/OdV4TV6NHDnnYDPB/zgBwMXXvv2Af/+77ItHAZKS9X34pQWeaQR5iExD4l5SHrmweLLZEKhEDZu3IhQKGR0V9IC85AMzeO994BrrwVmzwbq6wGnE/jWt9QeXg8+CIwZM/if3bmz/yOFAHWv2GCbr8aA80NiHhLzkJiHpGcevLhLRIk5eFAVVz/7GRAIqLYVK1Tb9OmxvceMGeoyY98CzOGI/T2IiEyCZ76IKD6dncB996m9up58UhVel10GbN4M/Nd/jaxomjQJeOop2Wa3J2WTVSKidMPiy2RsNhtyc3P56IcezENKSR7d3cATT6iia9Uq4KOPgKIi4I9/BNatA+bOje99r79evn733YRvtuf8kJiHxDwk5iHpmQdXOxJRbEIh4KWXgHvuUc9bBIBzzlGbpS5ZMviWEbE6dgw45ZQTrz/6CDj55MTek4gohbja0aI0TcPu3bu5GqUH85B0ySMcBtauBebMAa67ThVeZ5wB1NQAb78NLF2aeOGlE84PiXlIzENiHpKeebD4MplQKIStW7dyNUoP5iElPY/XX1dbRlx5JfDmm4DLBTzyiFqduHKlWtGYxjg/JOYhMQ+JeUh65pHevzmJyBhvv602RP3d79Trk04CvvMdtWnq2LHG9o2IyORYfBHRCbt3qxWMv/oVoGlqq4cbblD3eXHVIRFRUrD4MhmbzYYJEyZwNUoP5iHFnUd7u3rI9dNPAx9/rNqWLFF7dc2cmfyOpgjnh8Q8JOYhMQ9Jzzy42pEok330EfD448CjjwJHj6q2Sy5R93V95jOp7Uvv1Y5c6UhEJsTVjhYVCoWwfft23hDZg3lIMefx8cfqLNf06cDdd6vCa84ctU/XH/+Y+sJLJ5wfEvOQmIfEPCQ982DxZTKapqG1tZVLgXswD2nYPDQNePllYNYs9SDrf/xDbZb68stqZ/rLLjNu24iTT1bbWoTDSTvrxfkhMQ+JeUjMQ9IzD97zRZQJwmGgoQG4/XZgyxbVdtpp6kb6b3wDGDXK2P4REWUQFl9EVvfGG2qLiP/9X/X61FOBsjLgu9+VO8oTEVFKsPgyGbvdjilTpsBu5xVjgHn0JfLYvh24807glVfUN0eNUpcab78dGD/e2I6mCOeHxDwk5iExD0nPPLjakchq9u1TD7z+xS/UPV52u3os0KpVwJQpRveOiMiyuNrRokKhELZs2cLVKD2YRy8dHdB++ENo06cDP/+5KryuugrYtg34j//IyMKL80NiHhLzkJiHpGceLL5MRtM07Nmzh6tRejAPAP/8p9qXa9o02H/0I9i7u6FddBHw178C//M/wLnnGt1Dw3B+SMxDYh4S85D0zIP3fBGZVSCgzmitWgUcOAAACH/qU/jbVVeh8O67YecKRiKitMQzX0RmEw4DtbXAeecBpaWq8DrrLODFFxFsbsbBwkLj9uoiIqJh8cyXydjtduTn53M1So+My+OPf1TbRmzerF5PmKB2qC8pAbKzYQ+FMiuPYWTc/BgG85CYh8Q8JD3z4GpHIjNoaVFbRDQ2qtennAL88IfALbcAubnG9o2IiABwtaNlBYNBvP766wgGg0Z3JS1YPo+dO4EVK4DCQlV4ZWUB3/kO0NYG3Htvv8LL8nmMEPOQmIfEPCTmIemZBy87mkw4HMahQ4dg0ROWI2bZPP7v/4D771dbRgSD6h6ur39d3Vx/9tmD/jHL5hEn5iExD4l5SMxD0jMPFl9E6cTvBx59FHj8cbWFBABccQXw8MPApz9tZM+IiChJWHwRpYPjx4GnngIqKoCODtU2bx5QWQl87nPG9o2IiJKKxZfJOBwOzJ49Gw6Hw+iupAXT5xEMAr/6lbp/a98+1fbJT6oi7ItfHPGWEabPI8mYh8Q8JOYhMQ9Jzzy42pHICOGw2n3+zjuBd99VbZMnq/u8rr0W4C8/IiLT4WpHiwoGg1i/fj1Xo/QwZR5/+hMwfz7w5S+rwisvD/jRj4AdO4B//deECi9T5qEj5iExD4l5SMxD0jMPXnY0mXA4jM7OTq5G6WGqPLZuVXt1vfaaej16tNqn64c/BMaMScqPMFUeKcA8JOYhMQ+JeUh65sHii0hvPp/ahf6ll9Rrp1PtSH/33cDppxvbNyIiSjkWX0R6+cc/gAcfBKqr1UOwAeArXwEeeACYPt3YvhERkWF4w73JaJqG9vZ2jB8/3rrP3zp2TD0+BwA++gg4+eRBD03LPI4eBR57DPjxj9VYAODyy9UKxjlzdP3RaZmHgZiHxDwk5iExDymePGKtPVh8UfoZQfGVVrq7gWefBR56CGhvV22f+QzwyCPAJZcY2zciItIdVztaVCAQwNq1axGIXMbKcGmRRyik9urKzwe+/31VeOXnA3V1wN/+ltLCKy3ySCPMQ2IeEvOQmIekZx6858uEuAxYMiyPcBj4wx+AO+4A3npLtZ15JnDffWrLCKcxf704PyTmITEPiXlIzEPSKw8WX0Tx+OtfgfJy9f8A4HKpbSRuvhnIyTG0a0RElN5YfBGNxFtvqTNdv/+9en3SScB3v6sKsbFjje0bERGZAm+4N5nIpm+5ubmwjfC5f6YxghvuU5bH7t3APfcAL76oLjc6HMCNN6q2M8/U7+eOUEbMjxFgHhLzkJiHxDykePKItfbgmS8TyuFlLUHXPNrb1erFZ54BPv5YtS1bpvbqys/X7+cmgPNDYh4S85CYh8Q8JL3y4GpHkwkGg6ivr+dNkT10y+Ojj1SB5XYDjz+uCq9LLwU2bQLWrEnbwovzQ2IeEvOQmIfEPCQ98+CZL6LePv4YqKlRhdfBg6qtoEDt1bVwobF9IyIiSzD8zFd5eTn8fv+g3/d6vfB6vQAAn88X/ZooqTRNPXtx1iy1YvHgQfUIoNWrgeZmFl5ERJQ0hhZfXq8XVVVVQx5TXV2NuXPnwmazobS0FG63O0W9o4wQDgOvvqrObn3ta+oh2Kefrnaqf+cdYPlygI/ZICKiJDJ0tWNdXR3Ky8vR0tICl8s14DE1NTVYvnw5AAx6zECsvNoxGAzC6XRadzXKCFc7xp3H3/4G3HYb8Kc/qdennqq2jPjud83zSKM+MmJ+jADzkJiHxDwk5iHFk0faP16orq4OS5cujelYl8s1osLL6rq6uozuQloZcR7vvgt8+cvAvHmq8MrOBn74Q3XW6447TFt4RXB+SMxDYh4S85CYh6RXHobccO/3+2Mupvx+P+rq6gAAzc3Ng1567O7uRnd3d/T10aNHAahnM0Wey2S32+FwOBAKhaBpWvTYSHswGETvE4EOhwN2u33Q9r7Pe3L2PE6m78qIwdqzsrKgaRpCoVC0zWazwel0Dtre3d2NpqYmLFy4EFlZWZYYU7++h0Jw9HwdCASAPp9f774HAgE0NTVh8eLFsNlsQ49p7144HnwQthdegE3TELbbEb7uOoTuvhuYPFmNqedfOkkfU4o+p66uLjE/dP2cTDD3gsEgmpqasGjRIth7XT4285gS+Zx6//7Izs62xJgS+Zw0TRN/X6wwpkQ+p8jv04ULF2L06NGWGFNvI/2cwuFwv/kRy5hiYUjxtWbNGpSUlMR0bElJSbRQc7vdWLhwIdra2vodV1FRgVWrVvVrb2howOjRowEAU6ZMwZw5c7Bt2zbs2bMnekx+fj5mzpyJTZs24dChQ9H22bNnY+rUqdiwYQM6Ozuj7fPmzcPEiRPR0NAggr7kkkuQk5OD+vp60YfFixdH/6MY4XQ6ccUVV6C9vR0bN26Mtufm5mLBggXYu3cvtm7dGm2fMGEC5s+fHx17Y2OjZca0c+dOtLa2RtvPmjAB5/d8vW7dOoROOmnIMUUMNqam3/wGZ69eDffatbD3/OXVvvQl/O/CheicPBl4803gzTd1HVOqPqfIvIj8vxXGlMjcO6Xn8vX+/fvx5ptvWmJMyficGhsbLTcmYOSfU1FREQCgqanJMmNKxufU1NRkuTHF8zlF5kfk92ksY2ppaUEsUn7Pl8fjQWFhYbSgmjZt2pD3fHm9XhQUFABQZ8HGjh2Ltra2fme/BjrzNXnyZLS3t0evu5q9Cnc6nTh+/DjWrVtn7TNfXV1wjBkDAAgcORK9DDjYma/GxsaBz3x1d8P+5JMIV1bC9uGHAADt//0/2B55BLjwQkv+q7arqwuNjY0889UjGAyioaGBZ756nfmKzA+e+VJnvl577TWe+cKJM1+R+cEzX+q/J/X19SM689XR0YFx48al5w73a9asiX7t8/lQUVGBFStWRIusCK/Xi0svvRRHjhwR7Xl5ef3eMzs7G9nZ2f3as7KyxF8qQH2QDoej37GRDyzW9r7vG0+73W4X/1EYrt3hcMDpdPYbl9nHJPoe2Uk+8v59fkbfvkdeR9sDAeD554H77wf+7/9gA4DzzwcqKmBftAjouXEypWMapj2Zn9NA88PsY4q1faC+O51O2O32AY8365iGah9qTL3nR+QYs48pkc8pEAgM+PdlqL4P1p4uYxqqj7G0R/IYqu9mG1NvIxnTUPNjpGPqy/BnO9psNnEmy+v1wuVywe12w+/3i0uUdXV1WL16NWpra4d9X6uudswII1jtKGgaUFcH3HUXsHOnajv7bLVh6le/yi0jiIhIV2m/2tHv90f3+KqsrIxunlpRURG9wd7lcqGwsBBVVVWoqalBc3NzTIWXlWmahoMHD4rTnZksmse6dcBnPgOsWKEKr4kTgSefBLZvV/t3ZUjhxfkhMQ+JeUjMQ2Iekp55GH7mSy9WPfMVCARQX1+PxYsXD3qq1fRGcOYr+Le/4UhpKSZs26YaTjkFuPVW4PvfB3JzU9DZ9JIR82MEmIfEPCTmITEPKZ480v7MF2WgY8fU/VY2m/p6qOMG+rq3HTuA5cvhnDcPE7ZtQ3jUKLU5qs8H3HNPRhZeRERkDnywNqVO36Iqns1MDxwAVq1SN9SHQgjbbNh78cX4RHU1smbMSF5fiYiIdMLiy2RsNhtyc3PN/+iHAwfUfVmx8vuBykrgpz8FIjsOX3klQvffj11HjuCMs8/WpZtmY5n5kSTMQ2IeEvOQmIekZx6854tS5yc/AW65RX1ttwM1NcCNN/Y/7uBB4LTT1Nfvvw+sWQNUVACRLUfmz1eF2EUXpaTbREREseA9XxalaRp2795tvtUo+/ap5ydGaBpQWqrahzJvHlBWpgqvc88Ffvc74C9/iRZeps1DJ8xDYh4S85CYh8Q8JD3zYPFlMqFQCFu3bhU78ZrCzp2q4OotFAJ27ZJt4TCwdu2J1//3f8CUKcAvfwn8/e/AF78Y3SRVvYVJ89AJ85CYh8Q8JOYhMQ9JzzxYfFFqzJghiiYA6vX06SdeNzUBF14I3HDDibYf/ABobQWuvx4YYDdhIiIis2HxRcaJFGNbtgCLFgELFgCbNgGjRp045ic/Af7zP43pHxERkQ5YfJmMzWbDhAkTzLcaZedOdUmxN01TZ7QKCoB169QzHP/1X4HeD0Md5t4w0+ahE+YhMQ+JeUjMQ2Iekp55cLUjpca+fcDUqf3v+4q45hr1DMbdu9UZsL6amoCLL9a1i0RERIngakeLCoVC2L59u/luiJw0Ceh5lqfwuc+py47/+Z+A233isUJ9DbIhq2nz0AnzkJiHxDwk5iExD0nPPFh8mYymaWhtbTXXUuDjx9W9Ww8/3P97f/0r0NJy4vVHHw38HoM8ZsiUeeiIeUjMQ2IeEvOQmIekZx4svkg/oZDaIiI/X22u2tHR/5i+93TNmKE2YO3N4ZCrIomIiEyMxRclXzisNkM9/3zg3/4N2LMHOPNM4OmnBz6+935fkyYBjz124nsOB1BdrdqJiIgsgMWXydjtdkyZMgX2vmeH0sVf/qLu47rqKuDtt4GxY4FHH1WrHb/9bWDv3uHPbH3taye+3rx54EcQ9Uj7PFKMeUjMQ2IeEvOQmIekZx5c7UjJ8eabwB13AH/4g3qdkwN873vq0UAulzz2mWeAm25SX0fObPUusHo/2/Ef/xjZA7iJiIgMwtWOFhUKhbBly5b0WY3y/vvAddepS4x/+IMqpkpL1WXEhx/uX3gBJ86ANTWpP/+Vr6gNV222QW+sH0za5WEw5iExD4l5SMxDYh6Snnmw+DIZTdOwZ88e41ejHDqkzmzl5wMvvqju81q+HHjnHeBnPwPOOGPoPz9pktq3K8F7udImjzTBPCTmITEPiXlIzEPSMw9n0t+RrK2zE/jxj9VN8ZFtIYqLgYoKoLAw8fffv3/gs2VEREQWweKLYtPdDdTUqF3oDx1SbXPnAo88ooqvRLzwwomvZ80CnnzyxOtBNlclIiIyKxZfJmO325Gfn5+61SiaBrz0EnD33er+LEDtxfXQQ8CSJf1XLo7Uvn3AzTfLn/ed78T8x1OeR5pjHhLzkJiHxDwk5iHpmQdXO9LAwmHg1VeB228Htm1TbZ/4BHDvvcANN6iHYCdDU9PAz3KM+Ogjnv0iIiJT4GpHiwoGg3j99dcRDAb1+yEbN6qb4a+4QhVeY8aolYu7dqmVjMkqvIDBd7SPUUryMBHmITEPiXlIzENiHpKeebD40suxY3FvnzCUcDiMQ4cOQZcTlu+8A1x9NTB/PrBhA5CdDdx6K+DzqTNgo0cn/2dOmiTv8XI4gCeeiPmP65qHCTEPiXlIzENiHhLzkPTMg8UXqT23brgB+NSngN/+Vp2JuvFGdaarqgrIy9P3519//Ymv33lHviYiIrIY3nCfyQ4fVltEPPWUWs0IAP/yL+pm+lmzjOnTmWca83OJiIhShMWXyTgcDsyePRuOEdwX1c+xY8Djj6uzWkePqraLL1bbRlxwQTK6mTJJycNCmIfEPCTmITEPiXlIeubB1Y56OXYMOOUU9XW6rNgLBICf/xy4/37ggw9U2+zZ6uzX5Zer+9PSQTpmR0RENAyudrSoYDCI9evXj2z1haYBq1cDn/ykeq7iBx8Abrfav6ulBVi0KH0KrxGKKw8LYx4S85CYh8Q8JOYh6ZkHLzuaTDgcRmdnZ2yrL8JhoLFRrVT0elXbxInAPfcAK1cCo0bp29kUGFEeGYB5SMxDYh4S85CYh6RnHiy+rKq5GbjtNmD9evU6NxcoK1MPw45c0iMiIqKUY/FlNa2twF13AXV16vWoUcBNNwF33AGMH29s34iIiIj3fKXE/v1JeyuHw4F58+b1X32xfz9QUgKce64qvGw2tV/Wjh3Aj39s2cJr0DwyFPOQmIfEPCTmITEPSc88WHzp5YUXTnw9axbw/PNJeVu73Y6JEyeeeNDnkSPq8uL06cBzzwGhEPClL6nHAv3yl8DUqUn5uemqXx4ZjnlIzENiHhLzkJiHpGceTFgP+/YBN9984rWmqWci7tuX8FsHAgGsXbsWgaNH1T5dbjdQWQkcPw5cdBHwl7+oXerPOy/hn2UG0TwCAaO7khaYh8Q8JOYhMQ+JeUh65sF7vvSwc6cquHoLhdTjeiZNSuy9g0GcWV8PZ0kJcOCAajvvPLVX1xVXmHbLiERwWbTEPCTmITEPiXlIzEPSKw8WX3qYMUM9H7F3AeZwqEuD8QqHgVdegfOOOzB7xw7VNnUq8MADwDXXqPcnIiKitMfLjnqYNAl48skTrx0OoLo6/rNe69erx/4sXQrbjh3oPvVUhH70I7Wy8dprWXgRERGZCB8vpJfej8hpbQXOOWfk7+H1qg1SGxrU65NPRviWW9BZUoLcM8+EzaqXGEfweKHIJni5ubnWzWMEmIfEPCTmITEPiXlI8eQRa+3By46pcOaZIzt+1y61V9fq1ep1VhbwzW8Cd94JTJyIHF6TF3JycozuQlphHhLzkJiHxDwk5iHplQcvO6aTDz5Qz16cNUsVXjYb8LWvAdu3A088AZx2GoLBIOrr63lTZA/mITEPiXlIzENiHhLzkPTMg2e+0sGHHwKPPgr85CfAP/+p2r7wBbWC8fzzje0bERERJRWLLyMdPw48/TTw8MNAR4dqu/BC4JFHgM9/3ti+ERERkS5YfBkhFAJ+9Svg3nuBvXtV26xZqgi76qqM3KtLOPlktbUGERGRBXG1YyL27VMbqs6Y0X8biYFW7IXDwO9+px5y/c476nuTJgGrVgHXXQc4h6+Fw+EwgsEgnE4nV6OAefTFPCTmITEPiXlIzEOKJ49Yaw/ecB+vZ54BJk8GFixQm50O9+zGDRuAz34WuPpqVXjl5QGPPaYefH3DDTEVXhFdXV2J9d1imIfEPCTmITEPiXlIzEPSKw8WX/EY6bMblyxR93Bt3Ajk5KgzX21twA9+oF6PQDAYRFNTE1ej9GAeEvOQmIfEPCTmITEPSc88WHzFY7BnN/70pydeHzwIfP3r6v6tdevULvTf/KYquh56CHC5UtplIiIiSg8svuIReXZjXz/+MbB1K/Cd7wD5+cCvf63u81qxAnj3XeDZZ4FPfCLl3SUiIqL0weIrHpMmAbfc0r9d04B589RzHQMB4LLLgM2bgf/6L1WwJYlzBPeHZQLmITEPiXlIzENiHhLzkPTKg6sd47VvHzBlysBbIhQVqb26FixI/s8lIiKitMTVjnr7xCeAa6+VbaedBtTWAm+8oVvhpWkaDh48CK3vPWcZinlIzENiHhLzkJiHxDwkPfNg8RWvhga1USoAjBsHVFaqs2FLl+q6SWooFMLGjRsRCoV0+xlmwjwk5iExD4l5SMxDYh6Snnnw4m68Fi0CLr8cuOQSte3E6NEDHzfURqxERESUcVh8xctmA159deizXM88A9x0k/rabgdqaoAbb0xN/4iIiCgtGX7Zsby8HH6/f9Dv+3w+VFVVoa6uDlVVVUMem3JDFV4j3Yg15h9pQ25uLh/90IN5SMxDYh4S85CYh8Q8JD3zMHS1o9frxdy5c3HkyBG4Btl0dO7cuWhpaQGgCrHy8nLU1tYO+94pebbjUJqaBr7pvqkJuPjilHeHiIiI9GWK1Y4+nw9ut3vI7/fmdrvh8Xj07lZyDLQRq8MBTJ+e0Ntqmobdu3dzNUoP5iExD4l5SMxDYh4S85D0zMOw4quurg5Lly4d8hiPx4O8vDzRlpeXB6/Xq2fXkmPSJLXZaoTDAVRXJ3zTfSgUwtatW7kapQfzkJiHxDwk5iExD4l5SHrmYcgN936/f9DLjH2PG0hHR0e/tu7ubnR3d0dfHz16FAAQCAQQCAQAAHa7HQ6HA6FQSFSykfZgMIjeV2EdDgfsdvug7ZH3jYjshBt9COfKlcAXvgDn++8D06cjePrpauf7HllZWdA0TXywNpsNTqdz0PZIm2FjGqY93jEN1PdYxtS7v1YZU+++j3RMkfbI/1thTIl8TpFjNE0T72/mMSXyOfWeH1YZUyKfU+TPWmlMiXxOveeHVcbU20jHFNH758YyplgYUnytWbMGJSUlcf/5gYqyiooKrFq1ql97Q0MDRvdsAzFlyhTMmTMH27Ztw549e6LH5OfnY+bMmdi0aRMOHToUbZ89ezamTp2KDRs2oLOzM9o+b948TJw4EQ0NDSLoSy65BDk5Oaivrxd9WLx4Mbq6utDUq93pdOKKK65Ae3s7Nm7cGG3Pzc3FggULsHfvXmzdujXaPmHCBMyfPx9tbW0AgMbGxvQYU1NTwmPauXMnWltbo+0jHVOElcYU7+cUmReR/7fCmBL5nE455RQAwP79+/Hmm29aYkzJ+JwaGxstNyZg5J9TUVERAKCpqckyY0rG59TU1GS5McXzOUXmR+T3aSxjityjPpyU33Dv8XhQWFgYPfM1bdo0tLS0DHgmrKamBtXV1WIwY8eORW1tLYqLi8WxA535mjx5Mtrb26M3vZm9Cnc6neju7kZzczPmzp0Lp9NpiTEl8q+lYDAIr9eLCy64AAAsMabefR/p53T8+HG0tLRE54cVxpTI5xQKheD1elFYWCj+JWvmMSXyOX388cfR+TFq1ChLjCmRzykcDmPz5s2YM2eOeIafmceUyOcUDAaj8yMnJ8cSY+otnjNfb7zxBgoKCqLvOdyYOjo6MG7cuGFvuDek+Op9I31paSnKysqwYsUKFBQUiGN9Ph+WLVvWr/h67733hr1safhqRyIiIsooabvasbi4GCUlJdH/AaoAixReXq83Wpz1XQnp8/nEWbNMFAqFsH37dt4Q2YN5SMxDYh4S85CYh8Q8JD3zMGy1o9/vR1VVFQCgsrIyuoKxoqICdXV10eNqa2tRXl6Ouro6VFdXx7THl5VpmobW1lYuBe7BPCTmITEPiXlIzENiHpKeeRj2eCGXy4WysjKUlZWJ9r7FldvtRmVlJQAMuzUFERERUboz/PFCRERERJmExZfJ2O12TJkyBfa+u+dnKOYhMQ+JeUjMQ2IeEvOQ9MzD0Gc76omrHYmIiCiV0na1IyUmFAphy5YtXI3Sg3lIzENiHhLzkJiHxDwkPfNg8WUymqZhz549XI3Sg3lIzENiHhLzkJiHxDwkPfNg8UVERESUQoZtNaG3yK1skQdsW0UgEMA///lPHD16FFlZWUZ3x3DMQ2IeEvOQmIfEPCTmIcWTR6TmGO52essWX5GHck6ePNngnhAREVEm6ezsxJgxYwb9vmVXO2qahgMHDiA3N1c8UNfsIg8M37t3L1dxgnn0xTwk5iExD4l5SMxDiiePcDiMzs5OnHHGGUNuUWHZM192ux2TJk0yuhu6OfXUU/mXoxfmITEPiXlIzENiHhLzkEaax1BnvCJ4wz0RERFRCrH4IiIiIkohFl8mk52djXvvvRfZ2dlGdyUtMA+JeUjMQ2IeEvOQmIekZx6WveGeiIiIKB3xzBcRERFRCrH4IiIiIkohy241YTZerxcejwcA0NzcjOeeew4ul2vAY30+H6qrqzFt2jS0tbXh9ttvjx7r9XoBAAUFBfD5fPD7/SgoKEjFEJIqkoXf70dzczNWrFgx6Dh8Ph/q6urgdrvh8/lQUlISzWOo75lJsvLIxPkBqHGvXLkSLS0toj0T5wcweB6ZOD+G+t2bifNjqDwycX4MdWxC8yNMaaGyslJ8XVBQMOixbrc7fOTIkXA4HA63tLSES0pKot8rKSkJAwgDCBcXF0ePMxuXyxVuaWkJh8PhcHV1ddjtdg96bO+s2trawkuXLo3pe2aSrDwycX7U1taGW1pawgP9usvE+TFUHpk4P4b63ZuJ82OoPDJxfgx1bCLzg8VXGmhpaQm7XK7o67a2tjCAcFtbW79jGxsb+02U3r9Eq6urw0eOHDHtX4qIxsbG6NfV1dWDFqNtbW39vhfJcqjvmU0y8oj82UyaH731LTYycX70NlDxlWnzY6jfvZk4P4b7b1GmzY+hjk10fvCerzRQUFCA5557Lvra7/cDAPLy8vodG/leX5HTwQDgcrlMeWq8t+Li4ujXtbW1KC0tHfA4j8fTL6e8vLzoqfPBvmc2ycgjIpPmx1AycX7EIpPmx1C/ezNxfsTy36JMmh9DHZvo/OA9X2li6dKl0a9Xr16N4uLiASd45Fp7ROSD7ujoAKD+stTV1QFQ1+tLS0vhdrt17Ll+vF4vVq9ejYULF6KkpGTAYwYrRjs6Oob8nhklmkfk+5k0P4aSifNjOJk4Pwb73Zup82Oo/xZl4vwY7NhE5weLrzQTmdx9b4SNcLvdqKysRE1NDZYvXx4txCIVeO8b/txuNxYuXIi2traU9D3ZCgoK4Ha7UV5ejrq6OvFLYTiD/cUY7nvpLBl5cH4MLxPnR0Qmz4/hfvf2Ps6MkpFHps6PkRwb6/zgZcc0U15ejsbGxiFP65aVlaG4uBg+ny96SjTyr4/eZ8UiKzB6t5mNy+XCsmXLsGzZsgEntcvl6vcvjY6Ojuip8cG+Z1aJ5AFk3vwY7s9m2vwYTibPj76/ezN9fgz036JMnh99j010frD4SiNVVVUoLy+H2+2G3+8fdDL4fD643e7oJciCggK4XC54vV5ceuml/Y4f6N6xdObxeDB27Njo64EKy4je1+N7KywsHPJ7ZpKsPDJxfgwlE+fHUDJ5fgz0uzeT58dAeWTi/Bjq2ETnB4uvNFFXVxc9ten3+7FmzRqxt0rviTF37txoYVZdXY3KykoAJy5JRng8HixdutR0/1LLy8sTE9vr9cLlckX3VumdR9/7DXw+HwoLC+FyuYb8npkkM49Mmx999f4HTSbOj7765pGJ82Ow372ZOj+GyiPT5sdQxyY6P/hsxzTg8/kwbdo00eZyuXDkyBEAwLJly1BUVISysjIAQE1NDfLy8tDR0QG3291vcng8HrhcLrS1tYm/LGZSV1cXPaXb2NiIysrK6GTvm0dk09mioiI0NzeLTWeH+p6ZJCuPTJwfHo8HjY2NqKqqQllZGYqKiqL3bGTi/Bgqj0ybH8P97s20+TFcHpk2P4Y7NpH5weKLiIiIKIV42ZGIiIgohVh8EREREaUQiy8iIiKiFGLxRURERJRCLL6IiIiIUojFFxEREVEKsfgiIopRXV0dPB4PysvLU/5YFb/fj/Lycni93pT+XCJKPhZfRJTW0uVBxn6/H83NzSguLkZRUVHKN5jcvHlz2mRBRIlh8UVEhvB6vZg7d+6wx1VUVES/XrhwIUpLS6PPnhs7diyqqqpQU1OD0tJSlJaWJvzzfT4fqqqqUFdXh6qqqmjB43K5ogVXY2PjkD/L6/WitLQUNput39mqZcuWYdq0aSgvL4+5r4B6FqUZd1cnov6cRneAiDJPXV0d3G73iC6h+Xw+LFu2DCUlJdH38Hg80ceAAOqBwIn+/GXLlqGlpSX6M1euXIna2tro9yOPVxmqECooKEB5eTlqamr6PXKkqKgIzz33HAspogzG4ouIUi7yLMHh1NXVYcWKFQDU2aRI4QWos0+9n2sK9H9Y9kh/ft/7uNxuNzwej2grLi5GXl4eSktL0djYOOjPyMvL69dWU1MjisWIurq6Ae8hW7p0acxjIiLzYPFFRGmrsbER1dXVANCv0PJ4PP3uu+p7zEh5PJ5+RVNeXh68Xm/0nquysjK4XK4R33BfU1OD5cuXD/i9WItRIrIGFl9ElJb8fj+mTZsWfd37Mp3f74fP5+tXbCV6KW+wG9o7OjqwfPlyeDweeDweNDY2ikuRw4lcgux95m6kPB6PuExaUFAQ93sRkbFYfBFRWhqqWNm8eTPcbnfK7pvy+/1wuVzRM1QjOcNWUVGB22+/PbpFRbyrJIuLixM+s0dE6YGrHYkoLbW1tQ1aXA10v1cyuFwudHR0iLaOjo6EirzS0lK4XC7U1taiqqqK+3QREYsvIko/Xq8XCxcuHPT7Ho8npm0qRmqwgq6wsDDu94zcQ+Z2u1FWVoZly5bF/V5EZA0svojIUAPdZ7V69eohb0L3er1Dnvnyer0x3xDf++f3XVno8/lQWFgY15mvvmfQAEQvOca6JQYRWRPv+SKilIvctA6oe6KKioqGXfHn9/uxZs2a6J+L7NU10J+LbMw62E3xQ/382tpalJeXo6ioCM3NzSO6sT7C6/VGV2lWVFRgxYoVKCgoiBaE5eXlOHz4cL89wIgoM9jC4XDY6E4QEUVEiqpEV/PV1dVxCwciSku87EhEaaWxsZHbKBCRpbH4IqK00Xdvr3h5PB5uy0BEaYuXHYmIiIhSiGe+iIiIiFKIxRcRERFRCrH4IiIiIkohFl9EREREKcTii4iIiCiFWHwRERERpRCLLyIiIqIUYvFFRERElEIsvoiIiIhSiMUXERERUQr9f9ZVUDuKSp0tAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "inv_T = 1000 / (full['T'] + 273)\n", + "ln_eta = np.log(full['eta'])\n", + "\n", + "plt.figure(figsize=(7, 4))\n", + "plt.errorbar(inv_T, ln_eta, fmt='r.', yerr=full['deta'] / full['eta'])\n", + "z = np.linspace(min(inv_T), max(inv_T), 1000)\n", + "[a, b], cov = np.polyfit(inv_T, ln_eta, deg=1, cov=True)\n", + "plt.plot(z, a * z + b, color='red')\n", + "\n", + "plt.xlabel('$1/T$, $10^3$ K$^{-1}$')\n", + "plt.ylabel('$\\ln \\eta$')\n", + "plt.grid(linestyle='--')\n", + "plt.savefig('data/graph.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a = 5.53, aerr = 0.09, sys_err = 0.15, full_err = 0.17, eps = 0.03145145675233652\n", + "W = 7.625449798978944e-20 pm 2.398315045696995e-21\n" + ] + } + ], + "source": [ + "K_BOLTZ = 1.38e-23\n", + "\n", + "a_err = np.sqrt(np.diag(cov))[0]\n", + "sys_err = np.mean(full['deta'] / (full['eta'] * (max(ln_eta) - min(ln_eta))))\n", + "full_err = np.sqrt(a_err**2 + (a * sys_err)**2)\n", + "eps = full_err / a\n", + "print(f'a = {a:.2f}, aerr = {a_err:.2f}, sys_err = {a * sys_err:.2f},\\\n", + " full_err = {full_err:.2f}, eps = {eps}')\n", + "W = a * K_BOLTZ * 10**3\n", + "print(f'W = {W} pm {W * eps}')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "[times] = read_files(['data/times.csv'])\n", + "\n", + "for key in ['t1', 't2', 't3']:\n", + " tmp = np.floor(times[key])\n", + " tmp += 10 / 3 * (times[key] - tmp)\n", + " times[key] = tmp\n", + "\n", + "times.to_csv('data/times_sec.csv', float_format='%.1f', index=False)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2.2.6/Baldin_V/2.2.6.pdf b/2.2.6/Baldin_V/2.2.6.pdf new file mode 100644 index 00000000..b61240dc Binary files /dev/null and b/2.2.6/Baldin_V/2.2.6.pdf differ diff --git a/2.2.6/Baldin_V/2.2.6.tex b/2.2.6/Baldin_V/2.2.6.tex new file mode 100644 index 00000000..2a500404 --- /dev/null +++ b/2.2.6/Baldin_V/2.2.6.tex @@ -0,0 +1,268 @@ +\documentclass[a4paper, 12pt]{article} +\usepackage[left=2cm,right=2cm,top=2cm,bottom=2cm]{geometry} +\usepackage{wrapfig} \usepackage{graphicx} \usepackage{mathtext} +\usepackage{amsmath} \usepackage{siunitx} % Required for alignment +\usepackage{subfigure} \usepackage{multirow} \usepackage{rotating} +\usepackage[T1,T2A]{fontenc} \usepackage[russian]{babel} \usepackage{caption} +\usepackage{hyperref} +\usepackage[]{float} + +\hypersetup{ + colorlinks=true, + linkcolor=red, + urlcolor=magenta, + } +\graphicspath{{pictures/}} + + +\title{\begin{center}Лабораторная работа №2.2.6\end{center} Определение энерги +активации по температурной зависимости вязкости жидкости} \author{Балдин Виктор +Б01-303} \date{\today} + +\begin{document} +\maketitle + +\textbf{Цель работы:} 1) измерение скорости падения шариков при разной +температуре жидкости; 2) вычисление вязкости жидкости по закону Стокса и расчет +энергии активации. + +\textbf{В работе используются:} стеклянный цилиндр с исследуемой жидкостью +(глицерин); термостат; секундомер; горизонтальный компаратор; микроскоп; мелкие +шарики (диаметром 1 -- 2 мм). + +\section{Теоретическая часть} \subsection{Энергия активации} Для того чтобы +перейти в новое состояние, молекула жидкости должна преодолеть участки с большой +потенциальной энергией, превышающей среднюю тепловую энергию молекул. Для этого +тепловая энергия молекул должна — вследствие флуктуации — увеличиться на +некоторую величину $W$ , называемую энергией активации. Температурная +зависимость вязкости жидкости при достаточно грубых предположениях можно опистаь +формулой \begin{equation} \label{activation_energy:1} \eta \sim A e^{W/kT} +\end{equation} + +Из формулы (\ref{activation_energy:1}) следует, что существует линейня +зависимость между величинами $ln\eta$ и $1/T$, и энергию активации можно найти +по формуле + +\begin{equation} \label{activation_energy:2} W = k \frac{d(\ln\eta)}{d(1/T)} +\end{equation} + +\subsection{Измерение вязкости} По формуле Стокса, если шарик радиусом $r$ и со +скоростью $v$ движется в среде с вязкостью $\eta$, и при этом не наблюдается +турбулентных явлении, тормозящую силу можно найти по формуле (\ref{stokes}) + +\begin{equation}\label{stokes} F = 6\pi\eta rv \end{equation} + + +Для измерения вязкости жидкости рассмотрим свободное падение шарика в жидкости. +При медленных скоростях на шарик действуют силы Архимеда и Стокса, выражения для +которых мы знаем. Отсюда находим выражения для установившейся скорости шарика и +вязкости жидкости + +\begin{eqnarray} v_{уст} =\frac{2}{9}gr^2\frac{\rho - \rho_{\text{ж}}}{\eta}\label{v_ust},\\ + \eta =\frac{2}{9}gr^2\frac{\rho - \rho_{\text{ж}}}{v_{\text{уст}}}\label{eta} +\end{eqnarray} + +Как видим, измерив установившуюся скорость шарика и параметры системы можно +получить вязкость по формуле (\ref{eta}). + +\subsection{Экспериментальная установка} Для измерений используется стеклянный +цилиндрчиеский сосуд B, наполненный исследуемой жидкостью (глицерин). Диаметр +сосуда $\approx 3$ см, длина $\approx 25$ см. На стенках сосуда нанесены две +метки на некотором расстоянии друг от друга. Верхняя метка должна располагаться +ниже уровня жидкости с таким расчетом, чтобы скорость шарика к моменту +прохождения этой метки успевала установиться. Измеряя расстояние между метками, +b время падения определяют установившуюся скорость шарика $v_{уст}$. Сам сосуд B +помещен в рубашку D, омываемую водой из термостата. При работающем термостате +температура воды в рубашке D, а потому и температура жидкости 12 равна +температуре воды в термостате. Схема прибора (в разрезе) показана на +рис.~\ref{stand}. \begin{figure}[h] +\center{\includegraphics[scale=0.5]{stand.png}} \caption{Установка для +определения коэффициента вязкости жидкости.} \label{stand} \end{figure} + + +\section{Ход работы} \subsection{Подготовительные работы} Для начала отбираем +примерно 25 шариков, и измеряем их диаметры. Диаметры измеряем в трех случайных +направлениях и усредняем. Это делается по той причине, что некоторые шарики (в +частности металлические) имеют неидеальную геометрию. Данные измерении приведены +в таблице \ref{diams}. В таблице шарики с номерами вида $s\#$ стеклянные, а вида +$m\#$ металлические. Погрешности измерении диаметров $\sigma_d = 0.02\ \text{мм}$. +Плотности шариков в эксперименте + +\begin{align*} \rho_{стекло} & =2.5\ \text{г}/\text{см}^3 \\ \rho_{металл} & =7.8\ \text{г}/\text{см}^3 +\end{align*} + + +\begin{table}[h!] + \vspace{5pt} + \begin{center} + \subtable + { + \begin{tabular}{|l|rrr|r|} + \hline + № & $d_1$ & $d_2$ & $d_3$ & $\langle d \rangle$ \\ + & мм & мм & мм & мм \\ + \hline + s1 & 2.10 & 2.04 & 2.10 & 2.08 \\ + s2 & 2.08 & 2.06 & 2.06 & 2.07 \\ + s3 & 2.10 & 2.10 & 2.10 & 2.10 \\ + s4 & 2.06 & 2.08 & 2.08 & 2.07 \\ + s5 & 2.06 & 2.08 & 2.06 & 2.07 \\ + s6 & 2.08 & 2.08 & 2.08 & 2.08 \\ + s7 & 2.04 & 2.06 & 2.08 & 2.06 \\ + s8 & 2.04 & 2.10 & 2.08 & 2.07 \\ + s9 & 2.10 & 2.08 & 2.06 & 2.08 \\ + s10 & 2.08 & 2.08 & 2.08 & 2.08 \\ + s11 & 2.10 & 2.10 & 2.10 & 2.10 \\ + m1 & 0.66 & 0.70 & 0.70 & 0.69 \\ + m2 & 0.68 & 0.66 & 0.68 & 0.67 \\ + \hline + \end{tabular} + } + \subtable + { + \begin{tabular}{|l|rrr|r|} + \hline + № & $d_1$ & $d_2$ & $d_3$ & $\langle d \rangle$ \\ + & мм & мм & мм & мм \\ + \hline + m3 & 0.84 & 0.84 & 0.84 & 0.84 \\ + m4 & 0.82 & 0.82 & 0.82 & 0.82 \\ + m5 & 0.76 & 0.76 & 0.76 & 0.76 \\ + m6 & 0.82 & 0.88 & 0.72 & 0.81 \\ + m7 & 0.92 & 0.88 & 0.94 & 0.91 \\ + m8 & 0.88 & 0.92 & 0.88 & 0.89 \\ + m9 & 0.90 & 0.90 & 0.88 & 0.89 \\ + m10 & 0.88 & 0.88 & 0.86 & 0.87 \\ + m11 & 0.96 & 0.92 & 0.92 & 0.93 \\ + m12 & 1.00 & 1.04 & 0.88 & 0.97 \\ + m13 & 0.88 & 0.90 & 0.88 & 0.89 \\ + m14 & 0.94 & 0.98 & 1.00 & 0.97 \\ + m15 & 0.90 & 0.90 & 0.90 & 0.90 \\ + \hline + \end{tabular} + } + + \caption{Измеренные диаметры шариков.} + \label{diams} + \end{center} +\end{table} + +Измеряем длины частей цилиндра установки (см. рис. \ref{ustanovka}) +\begin{equation*} l_1=l_2=(10.2\pm0.1)\ \text{см} \end{equation*} + +\subsection{Измерение установившихся скоростей} Мы знаем путь, который проходит +шарик от одной отметки цилиндра к другой. Осталось измерить время прохождения +между этими отметками для получения скорости. В данной работе время падения +определяется секундомером. Получаем следующие данные (см. +таблицу \ref{times_in_frames}). Видео снималось с частотой 30 кадров в секунду, +следовательно единица времени в таблице $1/30 с$. Как видим $t_1$ и $t_2$ всегда +бизки. Отсюда можно предаоложить что на рассматриваемых участках скорость не +меняется. В дальнейшем будем считать это предположение правдивым, которое в +дальнейшем подтверждается малостью времени и пути релаксации. + + +\begin{table}[H] \vspace{5pt} \begin{center} \subtable { +\begin{tabular}{|l|r|r|r|} \hline {№} & {$T, ^\circ C$} & {$t_1$} & {$t_2$} \\ +\hline s1 & 30.86 & 459 & 452 \\ s2 & 30.86 & 455 +& 453 \\ s3 & 41.53 & 255 & 252 \\ s4 & 41.50 +& 249 & 250 \\ s5 & 50.93 & 146 & 150 \\ s6 & 51.05 +& 139 & 146 \\ s7 & 62.00 & 84 & 88 \\ s8 & 61.97 +& 81 & 87 \\ s9 & 61.85 & 79 & 81 \\ s10 & 68.40 +& 60 & 61 \\ s11 & 68.47 & 59 & 61 \\ \hline +\end{tabular} } \subtable { \begin{tabular}{|l|r|r|r|} \hline {№} & {$T, ^\circ +C$} & {$t_1$} & {$t_2$} \\ \hline m3 & 30.84 & 505 & 507 \\ +m4 & 30.85 & 523 & 523 \\ m5 & 41.51 & 320 & +313 \\ m6 & 41.53 & 300 & 302 \\ m7 & 51.09 & +132 & 135 \\ m8 & 51.05 & 135 & 143 \\ m10 & 61.53 +& 83 & 81 \\ m11 & 61.16 & 79 & 78 \\ m12 & 61.16 +& 71 & 71 \\ m14 & 67.81 & 60 & 59 \\ m15 & 67.73 +& 58 & 60 \\ \hline \end{tabular} } + +\caption{Измеренные времена падения шариков в кадрах.} \label{times_in_frames} +\end{center} \end{table} Для каждого измерения считаем $v$, $\eta$, $Re$, +$\tau$, $S$ где $v$ это скорость шарика на участке 1+2, $\tau$ это время +релаксации (см. формулу \ref{relaxation_time}), а $S=v\tau$ это путь релаксации. +\begin{equation}\label{relaxation_time} \tau = \frac{2r^2\rho}{9\eta} +\end{equation} + +Плотность жидкости берем из графика \ref{density} \begin{figure}[h] +\center{\includegraphics[width=0.7\linewidth]{density}} \caption{Плотность +глицерина при различных температурах.} \label{density} \end{figure} \paragraph{} +Данные всех расчетов приведены в таблице \ref{data} + +\begin{table} + \begin{center} + \begin{tabular}{|l|l|rr|rr|lll|} + \hline + {№} & {$T,\ \text{\textdegree C}$} & {$v,\ \text{см}/\text{с}$} & {$\Delta v, \text{см}/\text{с}$} & {$\eta, \text{мПа} \cdot \text{с}$} & {$\Delta\eta,\ \text{мПа}\cdot\text{с}$} & {Re} & {$\tau,\ \text{мс}$} & {$S,\ \mu\text{м}$} \\ + \hline + s1 & 30.86 & 0.67 & 0.007 & 441 & 21 & 0.02 & 0.70 & 0.05 \\ + s2 & 30.86 & 0.67 & 0.007 & 435 & 8 & 0.02 & 0.70 & 0.05 \\ + s3 & 41.53 & 1.20 & 0.012 & 251 & 3 & 0.06 & 1.20 & 0.1 \\ + s4 & 41.50 & 1.21 & 0.013 & 240 & 5 & 0.07 & 1.20 & 0.1 \\ + s5 & 50.93 & 2.05 & 0.023 & 143 & 3 & 0.18 & 2.10 & 0.4 \\ + s6 & 51.05 & 2.13 & 0.024 & 139 & 2 & 0.20 & 2.20 & 0.5 \\ + s7 & 62.00 & 3.52 & 0.045 & 83 & 3 & 0.55 & 3.50 & 1.2 \\ + s8 & 61.97 & 3.61 & 0.047 & 81 & 4 & 0.57 & 3.60 & 1.3 \\ + s9 & 61.85 & 3.79 & 0.05 & 78 & 2 & 0.63 & 3.80 & 1.4 \\ + s10 & 68.40 & 5.01 & 0.077 & 59 & 1 & 1.09 & 5.00 & 2.5 \\ + s11 & 68.47 & 5.05 & 0.078 & 60 & 1 & 1.10 & 5.10 & 2.6 \\\hline + m3 & 30.84 & 0.60 & 0.006 & 420 & 4 & 0.01 & 0.10 & 0.01 \\ + m4 & 30.85 & 0.58 & 0.006 & 414 & 4 & 0.01 & 0.10 & 0.01 \\ + m5 & 41.51 & 0.96 & 0.01 & 215 & 2 & 0.02 & 0.20 & 0.02 \\ + m6 & 41.53 & 1.01 & 0.01 & 233 & 66 & 0.02 & 0.20 & 0.02 \\ + m7 & 51.09 & 2.27 & 0.025 & 130 & 12 & 0.10 & 0.40 & 0.09 \\ + m8 & 51.05 & 2.18 & 0.024 & 130 & 10 & 0.09 & 0.40 & 0.09 \\ + m10 & 61.53 & 3.70 & 0.049 & 73 & 3 & 0.27 & 0.70 & 0.3 \\ + m11 & 61.16 & 3.86 & 0.052 & 80 & 6 & 0.28 & 0.70 & 0.3 \\ + m12 & 61.16 & 4.27 & 0.06 & 79 & 19 & 0.33 & 0.80 & 0.3 \\ + m14 & 67.81 & 5.09 & 0.079 & 66 & 6 & 0.46 & 1.00 & 0.5 \\ + m15 & 67.73 & 5.14 & 0.08 & 56 & 1 & 0.51 & 1.00 & 0.5 \\ + \hline + \end{tabular} + \end{center} + + \caption{Значения вязкостей в экспериментах} + \label{data} +\end{table} + +Как видим, времена и пути релаксации очень малые величины, поэтому предположение +что установившейся скорость достигается на участках 1 и 2 оправдано. Как видим, +числа Рейнольдса в основном меньше 1. Можно предположить что формула Стокса +работает, но окончательный вердикт вынесет график зависимости $ln(\eta)(1/T)$. +Собственно построим график этой зависимости. + +Построив график (см. на рис. \ref{graph}) и апроксимировав точки прямой линией, +получаем следующий угловой коэффициент: +\begin{figure}[H] + \centering + \includegraphics{data/graph.pdf} + \caption{График зависимости $\ln\eta (1 / T)$} + \label{graph} +\end{figure} + +$$W/k = 5.53\cdot10^3\ \text{К} $$ + +Погрешность будем вычислять как $\sigma_{W/k} = \sqrt{(\sigma_{W/k}^{\text{случ}})^2 + +(\sigma_{W/k}^{\text{сист}})^2}$. + +\begin{align*} +\sigma_{W/k}^{\text{случ}} & = \sqrt{\frac{1}{N - + 1}\left(\frac{\left<(\ln\eta)^2\right>}{\left<(1/T)^2\right>} + - \left(\frac{W}{k}\right)^2 \right)} = 0.09\cdot10^3\ \text{К},\\ +\sigma_{W/k}^{\text{сист}} & = \frac{W}{k}\sqrt{\varepsilon_{\ln\eta}^2 + + \varepsilon_{1/T}^2} = 0.15\cdot 10^3\ \text{К}, \\ +\frac{W}{k} & = (5.53 \pm 0.17)\cdot 10^3\ \text{К},\ \varepsilon_{W/k} = 3\%, +\end{align*} +В итоге для энергии активации: +$$ +W = (76\pm 2)\cdot 10^{-21}\ \text{Дж},\ \varepsilon_W = 3\% +$$ +\section{Обсуждение результатов} + +\paragraph{} Проанализируем полученные результаты. На графике +\ref{graph} видно, что значение вязкостей заметно отличаютя при низких температурах, при +которых наш метод работает лучше всего. Различие можно обьяснить различием +состава глицерина и, возможно, неравномерностью нагрева в нашей установке, т.к. +при низких температурах вязкость меняется резче, и это +может серьезно повлиять на среднюю вязкость. \end{document} diff --git a/2.2.6/Baldin_V/data/balls.csv b/2.2.6/Baldin_V/data/balls.csv new file mode 100644 index 00000000..39d02beb --- /dev/null +++ b/2.2.6/Baldin_V/data/balls.csv @@ -0,0 +1,27 @@ +ball,d1,d2,d3,d +s1,2.10,2.04,2.10,2.08 +s2,2.08,2.06,2.06,2.07 +s3,2.10,2.10,2.10,2.10 +s4,2.06,2.08,2.08,2.07 +s5,2.06,2.08,2.06,2.07 +s6,2.08,2.08,2.08,2.08 +s7,2.04,2.06,2.08,2.06 +s8,2.04,2.10,2.08,2.07 +s9,2.10,2.08,2.06,2.08 +s10,2.08,2.08,2.08,2.08 +s11,2.10,2.10,2.10,2.10 +m1,0.66,0.70,0.70,0.69 +m2,0.68,0.66,0.68,0.67 +m3,0.84,0.84,0.84,0.84 +m4,0.82,0.82,0.82,0.82 +m5,0.76,0.76,0.76,0.76 +m6,0.82,0.88,0.72,0.81 +m7,0.92,0.88,0.94,0.91 +m8,0.88,0.92,0.88,0.89 +m9,0.90,0.90,0.88,0.89 +m10,0.88,0.88,0.86,0.87 +m11,0.96,0.92,0.92,0.93 +m12,1.00,1.04,0.88,0.97 +m13,0.88,0.90,0.88,0.89 +m14,0.94,0.98,1.00,0.97 +m15,0.90,0.90,0.90,0.90 diff --git a/2.2.6/Baldin_V/data/full.csv b/2.2.6/Baldin_V/data/full.csv new file mode 100644 index 00000000..05822b80 --- /dev/null +++ b/2.2.6/Baldin_V/data/full.csv @@ -0,0 +1,23 @@ +No,T,v,dv,eta,deta,Re,tau,S,mu +s1,30.86,0.67,0.007,441,21,0.02,0.70,0.05 +s2,30.86,0.67,0.007,435,8,0.02,0.70,0.05 +s3,41.53,1.20,0.012,251,3,0.06,1.20,0.1 +s4,41.50,1.21,0.013,240,5,0.07,1.20,0.1 +s5,50.93,2.05,0.023,143,3,0.18,2.10,0.4 +s6,51.05,2.13,0.024,139,2,0.20,2.20,0.5 +s7,62.00,3.52,0.045,83,3,0.55,3.50,1.2 +s8,61.97,3.61,0.047,81,4,0.57,3.60,1.3 +s9,61.85,3.79,0.05,78,2,0.63,3.80,1.4 +s10,68.40,5.01,0.077,59,1,1.09,5.00,2.5 +s11,68.47,5.05,0.078,60,1,1.10,5.10,2.6 +m3,30.84,0.60,0.006,420,4,0.01,0.10,0.006 +m4,30.85,0.58,0.006,414,4,0.01,0.10,0.006 +m5,41.51,0.96,0.01,215,2,0.02,0.20,0.02 +m6,41.53,1.01,0.01,233,66,0.02,0.20,0.02 +m7,51.09,2.27,0.025,130,12,0.10,0.40,0.09 +m8,51.05,2.18,0.024,130,10,0.09,0.40,0.09 +m10,61.53,3.70,0.049,73,3,0.27,0.70,0.3 +m11,61.16,3.86,0.052,80,6,0.28,0.70,0.3 +m12,61.16,4.27,0.06,79,19,0.33,0.80,0.3 +m14,67.81,5.09,0.079,66,6,0.46,1.00,0.5 +m15,67.73,5.14,0.08,56,1,0.51,1.00,0.5 \ No newline at end of file diff --git a/2.2.6/Baldin_V/data/graph.pdf b/2.2.6/Baldin_V/data/graph.pdf new file mode 100644 index 00000000..460e51e2 Binary files /dev/null and b/2.2.6/Baldin_V/data/graph.pdf differ diff --git a/2.2.6/Baldin_V/data/times.csv b/2.2.6/Baldin_V/data/times.csv new file mode 100644 index 00000000..bae31613 --- /dev/null +++ b/2.2.6/Baldin_V/data/times.csv @@ -0,0 +1,23 @@ +ball,T,t1,t2,t3 +s1,30.86,0.12,15.21,30.23 +s2,30.86,2.11,17.16,32.19 +s3,41.53,0.19,9.04,17.16 +s4,41.5,0.09,8.18,16.28 +s5,50.93,0.07,5.03,10.03 +s6,51.05,0.1,4.29,9.25 +s7,62,0.07,3.01,5.29 +s8,61.97,3.13,6.04,9.01 +s9,61.85,0.15,3.04,5.25 +s10,68.4,14.08,16.08,18.09 +s11,68.47,10.01,12,14.01 +m3,30.84,33.26,50.21,67.18 +m4,30.85,34.09,51.22,69.05 +m5,41.51,28.14,39.04,49.17 +m6,41.53,29.25,39.25,49.27 +m7,51.09,56,60.12,64.27 +m8,51.05,49.12,53.27,58.2 +m10,61.53,12.15,15.08,17.29 +m11,61.16,92.12,95.01,97.19 +m12,61.16,7.24,10.05,12.16 +m14,67.81,22.24,24.24,26.23 +m15,67.73,15.28,17.26,19.26 diff --git a/2.2.6/Baldin_V/data/times_sec.csv b/2.2.6/Baldin_V/data/times_sec.csv new file mode 100644 index 00000000..cc38950b --- /dev/null +++ b/2.2.6/Baldin_V/data/times_sec.csv @@ -0,0 +1,23 @@ +ball,T,t1,t2,t3 +s1,30.9,0.4,15.7,30.8 +s2,30.9,2.4,17.5,32.6 +s3,41.5,0.6,9.1,17.5 +s4,41.5,0.3,8.6,16.9 +s5,50.9,0.2,5.1,10.1 +s6,51.0,0.3,5.0,9.8 +s7,62.0,0.2,3.0,6.0 +s8,62.0,3.4,6.1,9.0 +s9,61.9,0.5,3.1,5.8 +s10,68.4,14.3,16.3,18.3 +s11,68.5,10.0,12.0,14.0 +m3,30.8,33.9,50.7,67.6 +m4,30.9,34.3,51.7,69.2 +m5,41.5,28.5,39.1,49.6 +m6,41.5,29.8,39.8,49.9 +m7,51.1,56.0,60.4,64.9 +m8,51.0,49.4,53.9,58.7 +m10,61.5,12.5,15.3,18.0 +m11,61.2,92.4,95.0,97.6 +m12,61.2,7.8,10.2,12.5 +m14,67.8,22.8,24.8,26.8 +m15,67.7,15.9,17.9,19.9 diff --git a/2.2.6/Baldin_V/pictures/density.png b/2.2.6/Baldin_V/pictures/density.png new file mode 100644 index 00000000..833973ef Binary files /dev/null and b/2.2.6/Baldin_V/pictures/density.png differ diff --git a/2.2.6/Baldin_V/pictures/stand.png b/2.2.6/Baldin_V/pictures/stand.png new file mode 100644 index 00000000..b2a9df53 Binary files /dev/null and b/2.2.6/Baldin_V/pictures/stand.png differ diff --git a/2.2.6/pdf/Baldin_V.pdf b/2.2.6/pdf/Baldin_V.pdf new file mode 100644 index 00000000..b61240dc Binary files /dev/null and b/2.2.6/pdf/Baldin_V.pdf differ diff --git a/2.3.1/Baldin_V/2.3.1.pdf b/2.3.1/Baldin_V/2.3.1.pdf new file mode 100644 index 00000000..5064812e Binary files /dev/null and b/2.3.1/Baldin_V/2.3.1.pdf differ diff --git a/2.3.1/Baldin_V/2.3.1.tex b/2.3.1/Baldin_V/2.3.1.tex new file mode 100644 index 00000000..e7a3e76e --- /dev/null +++ b/2.3.1/Baldin_V/2.3.1.tex @@ -0,0 +1,201 @@ +\documentclass[12pt]{article} +\usepackage{amsmath} +\usepackage{amsfonts} +\usepackage{amssymb} +\usepackage[utf8]{inputenc} +\usepackage[T1,T2A]{fontenc} +\usepackage[english, russian]{babel} +\usepackage{graphicx} +\usepackage{float} +\usepackage[left=2cm,right=2cm,top=2cm,bottom=2cm]{geometry} +\usepackage{wrapfig} +\usepackage{pgfplots} +\usepackage{setspace} +\usepackage{indentfirst} +\usepackage{subfigure} +\usepackage{hyperref} + +\title{ +Лабораторная работа 2.3.1 \\ +<<Получение и измерение вакуума>> +} + +\author{Балдин Виктор, Б01-303} + +\begin{document} + \maketitle + + \paragraph{Цель работы:} 1) измерение объемов форвакуумной и высоковакуумной + частей установки; + 2) определение скорости откачки системы в стационарном режиме, + а также по ухудшению и улучшению вакуума. + \paragraph{В работе используются:} вакуумная установка с манометрами: масляным, + термопарным и ионизационным. + + \section{Теоретическая часть} + \subsection{Процесс откачки} + + Пусть W --- объем газа, удаляемого из + сосуда при данном давлении за единицу времени, $Q_i$ для различных значений + $i$ обозначим различные притоки газа в сосуд (в единицах $PV$), такие как + течи извне $Q_\text{и}$, десорбция с поверхностей внутри сосуда + $Q_\text{д}$, обратный ток через насос $Q_\text{н}$. Тогда имеем: + \begin{equation} -VdP = \left(PW - \sum Q_i\right)dt \end{equation} При + достижении предельного вакуума устанавливается $P_{\text{пр}}$, и $dP = 0$. + В таком случае: \begin{equation} W = \frac{\sum Q_i}{ P_{\text{пр}}} + \end{equation} Поскольку обычно $Q_\text{и}$ постоянно, а $Q_\text{н}$ и + $Q_\text{д}$ слабо зависят от времени, также считая постоянной W, можем + проинтегрировать (1) и получить: \begin{equation} P - P_{\text{пр}} = (P_0 - + P_{\text{пр}})\exp\left(-\frac{W}{V}t\right) \label{exp} \end{equation} + Полная скорость откачки $W$, собственная скорость откачки насоса + $W_{\text{н}}$ и проводимости элементов системы $C_1, C_2,\;\ldots$ + соотносятся согласно формуле (4), и это учтено в конструкции установки. + \begin{equation} \frac{1}{W} = \frac{1}{W_\text{н}} + \frac{1}{C_1} + + \frac{1}{C_2} + \ldots \end{equation} + + \subsection{Течение газа через трубу} + + Характер течения газа существенно зависит от соотношения между размерами + системы и длиной свободного пробега молекул. При атмосферном и форвакуумном + давлениях длина свободного пробега меньше диаметра трубок, и течение газа + определяется его вязкостью, т.е. взаимодействием молекул. При переходе к + высокому вакууму столкновения молекул между собой начинают играть меньшую + роль, чем соударения со стенками. + + Для количества газа, протекающего через трубу длины $l$ и радиуса $r$ в + условиях высокого вакуума, справедлива формула: \begin{equation} + \frac{d(PV)}{dt} = \frac{4}{3}r^3\sqrt{\frac{2\pi RT}{\mu}}\cdot\frac{P_2 - + P_1}{l} \label{kap} \end{equation} Если труба соединяет установку с + насосом, то + давлением $P_1$ у его конца можно пренебречь. Давление в сосуде $P = P_2$. + Тогда пропускная способность трубы: \begin{equation} C_\text{тр} = + \left(\frac{dV}{dt}\right)_\text{тр} = \frac{4r^3}{3l}\sqrt{\frac{2\pi + RT}{\mu}} \label{ty} \end{equation} + + \section{Экспериментальная установка} + + Установка изготовлена из стекла, + и состоит из форвакуумного баллона (ФБ), высоковакуумного диффузионного + насоса (ВН), высоковакуумного баллона (ВБ), масляного (М) и ионизационного + (И) манометров, термопарных манометров ($\text{М}_1$ и $\text{М}_2$), + форвакуумного насоса (ФН) и соединительных кранов ($\text{K}_1, + \text{K}_2,\; \ldots \;\text{K}_6$) (Рис. \ref{facility}). Кроме того, в + состав установки входят: реостат и амперметр для регулирования тока + нагревателя диффузионного насоса. + + \begin{figure}[H] + \centering + \includegraphics[scale=2]{stand.png} + \caption{Схема экспериментальной установки} + \label{facility} + \end{figure} + + \section{Ход работы} + \subsection{Определение объема форвакуумной и высоковакуумной частей + установки} + \begin{enumerate} + \item Атмосферное давление равно $P_{\text{A}} = (748\pm1)$ торр. + \item Впустим в установку атмосферный воздух через краны К1 и К2. + \item Закроем краны К5 и К6, запрем $V_{\text{зап}} = 50$ см$^3$ + воздуха. + \item Закроем краны К1 и К2, включим форвакуумный насос. Подключим + установку к форвакуумному насосу краном К2 и откачаем ее до давления + 10$^-2$ торр. + \item Повернув рукоятку крана К2, отсоединим установку от форвакуумного + насоса. Откроем кран К1. + \item Перекрыв К3, отделим ВБ от ФБ. + \item Закроем К4. + \item Откроем К5, измерим уровень масла слева и справа, которые + дадут нам давление $P_1$. + Из закона Бойля-Мариотта $V_{\text{фв}} = V_{\text{зап}}P_{\text{A}}/ + P_1$. + \item Аналогичным методом измерим объем $V_{\text{вв}}$, открыв кран + К3. + \item Повторим все измерения еще раз. Все результаты в таблице. + Погрешность измерения уровня примем $\Delta h = 0{,}1$ см. + \begin{table}[H] + \centering + \begin{tabular}{|c|c|c|c|c|c|} + \hline + $h_1$, см & $h_2$, см & $P_1$, торр & $h_3$, см & $h_4$, см & $P_2$, торр \\\hline + 34{,}6 & 6{,}2 & 18{,}6 & 29{,}6 & 11{,}4 & 11{,}8 \\\hline + 34{,}5 & 6{,}1 & 18{,}6 & 29{,}8 & 11{,}4 & 12{,}0 \\\hline + \end{tabular} + \caption{Таблица показаний масляного манометра} + \end{table} + \item Получим $V_{\text{фв}} = (2010\pm40)$ см$^3$, + $V_{\text{вв}} = (1150\pm30)$ см$^3$. Относительная погрешность может + быть вычислена в обоих случаях как $\varepsilon_V = \varepsilon_P + + \varepsilon_{P_{\text{A}}}$. $\varepsilon_{V_{\text{фв}}} = 0{,}2$, + $\varepsilon_{V_{\text{вв}}} = 0{,}3$. + + \end{enumerate} + + \subsection{Получение высокого вакуума и измерение скорости откачки} + \begin{enumerate} + \setcounter{enumi}{11} + \item Установим ток в лампе $I_0 = 0{,}6$ А. + \item После того, как давление упало ниже $3\cdot 10^{-2}$ торр, + закроем К6 и установим ток $I_{\text{max}} = 1{,}29$ А для нагревания + масла. + \item Когда давление достигнет $10^{-3}$ торр, включим ионизационный + манометр. + \item По достижении $1{,}6\cdot 10^{-4}$ торр начнем дегазацию. + \item Получаем предельное давление $P_{\text{пр}} = 5{,}6\cdot 10^{-5}$ + торр. + \item Остановим откачку и откроем кран К3. Снимем зависимость $P(t)$ + в процессе ухудшения, а затем в процессе улучшения вакуума. + \item Все результаты представим на графиках: + \begin{figure}[H] + \centering + \input{data/data2.pgf} + \caption{Откачка 1} + \label{graph1} + \end{figure} + \begin{figure}[H] + \centering + \input{data/data1.pgf} + \caption{Ухудшение вакуума 1} + \label{graph2} + \end{figure} + \begin{figure}[H] + \centering + \input{data/data4.pgf} + \caption{Откачка 2} + \label{graph3} + \end{figure} + \begin{figure}[H] + \centering + \input{data/data3.pgf} + \caption{Ухудшение вакуума 2} + \label{graph4} + \end{figure} + \item По графикам \ref{graph1} и \ref{graph3} + $W = -\overline{k}V_{\text{вв}} = (207\pm 15)$ см$^3$/с + $(\varepsilon_W = 0{,}07)$. + \item По графикам \ref{graph2} и \ref{graph4} + $Q_{\text{н}} + Q_{\text{д}} = \overline{k}V_{\text{вв}} = + (19{,}5\pm 0{,}1)\cdot 10^{-3}$ торр $\cdot$ см$^3$/с. Пренебрегая + эффектом десорбации, считаем $Q_{\text{н}} \approx Q_{\text{н}} + Q_{\text{д}}$, + $\varepsilon_Q = 0{,}005$ + \item Теперь попробуем получить тот же результат для $W$ методом + создания искусственной течи. Для этого откроем кран К6 и подождем, + пока + давление установится. + Используя формулу \ref{kap} для расчета + скорости истечения газа, получаем: + $$ (P_{\text{уст}} - P_{\text{пр}})W = \frac{4}{3}r^3 + \sqrt{\frac{2\pi RT}{\mu}}\frac{P_{\text{фв}}}{L}, $$ + \item В результате создания искусственной течи получим + $P_{\text{уст}} = (5{,}9\pm0{,}2)\cdot 10^{-5}$ торр ($\varepsilon_P = 0{,}3$), + откуда $W = (114 \pm 20)$ см$^3$/c ($\varepsilon_W = 0{,}18$). + \end{enumerate} + + \section{Выводы} + В данной работе мы получили вычислили параметры установки: объемы баллонов, + производительность насоса $W$, обратный ток газа через насос + $Q_{\text{н}}$. Как можно видеть, при создании искусственной течи $W$ + существенно изменяется. Это можно обосновать тем, что после открытия К6 + эффективная пропусная способность системы вместе с капилляром снижается, + что не позволяет достичь максимальной производительности насоса. +\end{document} diff --git a/2.3.1/Baldin_V/data/data1.csv b/2.3.1/Baldin_V/data/data1.csv new file mode 100644 index 00000000..6e67ddda --- /dev/null +++ b/2.3.1/Baldin_V/data/data1.csv @@ -0,0 +1,32 @@ +t,P +0,1 +1,1.1 +2,1.3 +3,1.5 +4,1.7 +5,2 +6,2.2 +7,2.4 +8,2.7 +9,2.8 +10,3 +11,3.2 +12,3.3 +13,3.5 +14,3.7 +15,3.9 +16,4 +17,4.2 +18,4.4 +19,4.6 +20,4.8 +21,4.9 +22,5 +23,5.1 +24,5.2 +25,5.3 +26,5.5 +27,5.7 +28,5.8 +29,6 +30,6.1 diff --git a/2.3.1/Baldin_V/data/data1.pgf b/2.3.1/Baldin_V/data/data1.pgf new file mode 100644 index 00000000..036e5647 --- /dev/null +++ b/2.3.1/Baldin_V/data/data1.pgf @@ -0,0 +1,1779 @@ +%% Creator: Matplotlib, PGF backend +%% +%% To include the figure in your LaTeX document, write +%% \input{.pgf} +%% +%% Make sure the required packages are loaded in your preamble +%% \usepackage{pgf} +%% +%% Also ensure that all the required font packages are loaded; for instance, +%% the lmodern package is sometimes necessary when using math font. +%% \usepackage{lmodern} +%% +%% Figures using additional raster images can only be included by \input if +%% they are in the same directory as the main LaTeX file. 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+\pgfpathmoveto{\pgfqpoint{1.000000in}{3.346389in}}% +\pgfpathlineto{\pgfqpoint{1.138889in}{3.346389in}}% +\pgfpathlineto{\pgfqpoint{1.277778in}{3.346389in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=1.388889in,y=3.297778in,left,base]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle y=0.17x+1.16\)}% +\end{pgfscope}% +\end{pgfpicture}% +\makeatother% +\endgroup% diff --git a/2.3.1/Baldin_V/data/data2.csv b/2.3.1/Baldin_V/data/data2.csv new file mode 100644 index 00000000..af325d7f --- /dev/null +++ b/2.3.1/Baldin_V/data/data2.csv @@ -0,0 +1,16 @@ +t,P +0,6.4 +1,6 +2,5.6 +3,4.9 +4,4.3 +5,3.7 +6,3.2 +7,2.7 +8,2.2 +9,1.9 +10,1.7 +11,1.5 +12,1.4 +13,1.3 +14,1.2 diff --git a/2.3.1/Baldin_V/data/data2.pgf b/2.3.1/Baldin_V/data/data2.pgf new file mode 100644 index 00000000..cf260ecc --- /dev/null +++ b/2.3.1/Baldin_V/data/data2.pgf @@ -0,0 +1,1273 @@ +%% Creator: Matplotlib, PGF backend +%% +%% To include the figure in your LaTeX document, write +%% \input{.pgf} +%% +%% Make sure the required packages are loaded in your preamble +%% \usepackage{pgf} +%% +%% Also ensure that all the required font packages are loaded; for instance, +%% the lmodern package is sometimes necessary when using math font. +%% \usepackage{lmodern} +%% +%% Figures using additional raster images can only be included by \input if +%% they are in the same directory as the main LaTeX file. For loading figures +%% from other directories you can use the `import` package +%% \usepackage{import} +%% +%% and then include the figures with +%% \import{}{.pgf} +%% +%% Matplotlib used the following preamble +%% +%% \makeatletter\@ifpackageloaded{underscore}{}{\usepackage[strings]{underscore}}\makeatother +%% +\begingroup% +\makeatletter% +\begin{pgfpicture}% +\pgfpathrectangle{\pgfpointorigin}{\pgfqpoint{7.000000in}{4.000000in}}% +\pgfusepath{use as bounding box, clip}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetmiterjoin% +\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.000000pt}% +\definecolor{currentstroke}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{7.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{7.000000in}{4.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{4.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathclose% +\pgfusepath{fill}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetmiterjoin% +\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.000000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetstrokeopacity{0.000000}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{3.520000in}}% +\pgfpathlineto{\pgfqpoint{0.875000in}{3.520000in}}% +\pgfpathlineto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathclose% +\pgfusepath{fill}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.121569,0.466667,0.705882}% 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+\pgfpathcurveto{\pgfqpoint{-0.013892in}{0.008068in}}{\pgfqpoint{-0.015528in}{0.004118in}}{\pgfqpoint{-0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{-0.015528in}{-0.004118in}}{\pgfqpoint{-0.013892in}{-0.008068in}}{\pgfqpoint{-0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{-0.008068in}{-0.013892in}}{\pgfqpoint{-0.004118in}{-0.015528in}}{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathclose% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{1.121591in}{3.219426in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.473864in}{3.145215in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.826136in}{3.065334in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.178409in}{2.908934in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% 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a/2.3.1/Baldin_V/data/data3.csv b/2.3.1/Baldin_V/data/data3.csv new file mode 100644 index 00000000..30bf7850 --- /dev/null +++ b/2.3.1/Baldin_V/data/data3.csv @@ -0,0 +1,32 @@ +t,P +0,1.1 +1,1.2 +2,1.3 +3,1.5 +4,1.7 +5,2 +6,2.2 +7,2.4 +8,2.6 +9,2.8 +10,3 +11,3.2 +12,3.3 +13,3.5 +14,3.6 +15,3.8 +16,4 +17,4.2 +18,4.4 +19,4.6 +20,4.8 +21,4.9 +22,5 +23,5.1 +24,5.2 +25,5.4 +26,5.5 +27,5.7 +28,5.8 +29,6 +30,6.1 diff --git a/2.3.1/Baldin_V/data/data3.pgf b/2.3.1/Baldin_V/data/data3.pgf new file mode 100644 index 00000000..d4772761 --- /dev/null +++ b/2.3.1/Baldin_V/data/data3.pgf @@ -0,0 +1,1779 @@ +%% Creator: Matplotlib, PGF backend +%% +%% To include the figure in your LaTeX document, write +%% \input{.pgf} +%% +%% Make sure the required packages are loaded in your preamble +%% \usepackage{pgf} +%% +%% Also ensure that all the required font packages are loaded; for instance, +%% the lmodern package is sometimes necessary when using math font. +%% \usepackage{lmodern} +%% +%% Figures using additional raster images can only be included by \input if +%% they are in the same directory as the main LaTeX file. For loading figures +%% from other directories you can use the `import` package +%% \usepackage{import} +%% +%% and then include the figures with +%% \import{}{.pgf} +%% +%% Matplotlib used the following preamble +%% +%% \makeatletter\@ifpackageloaded{underscore}{}{\usepackage[strings]{underscore}}\makeatother +%% +\begingroup% +\makeatletter% +\begin{pgfpicture}% +\pgfpathrectangle{\pgfpointorigin}{\pgfqpoint{7.000000in}{4.000000in}}% +\pgfusepath{use as bounding box, clip}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetmiterjoin% +\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.000000pt}% +\definecolor{currentstroke}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{7.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{7.000000in}{4.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{4.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathclose% +\pgfusepath{fill}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetmiterjoin% +\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.000000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetstrokeopacity{0.000000}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{3.520000in}}% +\pgfpathlineto{\pgfqpoint{0.875000in}{3.520000in}}% +\pgfpathlineto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathclose% +\pgfusepath{fill}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.121569,0.466667,0.705882}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{0.121569,0.466667,0.705882}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.015528in}{-0.015528in}}{\pgfqpoint{0.015528in}{0.015528in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathcurveto{\pgfqpoint{0.004118in}{-0.015528in}}{\pgfqpoint{0.008068in}{-0.013892in}}{\pgfqpoint{0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{0.013892in}{-0.008068in}}{\pgfqpoint{0.015528in}{-0.004118in}}{\pgfqpoint{0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{0.015528in}{0.004118in}}{\pgfqpoint{0.013892in}{0.008068in}}{\pgfqpoint{0.010980in}{0.010980in}}% +\pgfpathcurveto{\pgfqpoint{0.008068in}{0.013892in}}{\pgfqpoint{0.004118in}{0.015528in}}{\pgfqpoint{0.000000in}{0.015528in}}% +\pgfpathcurveto{\pgfqpoint{-0.004118in}{0.015528in}}{\pgfqpoint{-0.008068in}{0.013892in}}{\pgfqpoint{-0.010980in}{0.010980in}}% +\pgfpathcurveto{\pgfqpoint{-0.013892in}{0.008068in}}{\pgfqpoint{-0.015528in}{0.004118in}}{\pgfqpoint{-0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{-0.015528in}{-0.004118in}}{\pgfqpoint{-0.013892in}{-0.008068in}}{\pgfqpoint{-0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{-0.008068in}{-0.013892in}}{\pgfqpoint{-0.004118in}{-0.015528in}}{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathclose% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{1.121591in}{0.632762in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.285985in}{0.685524in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.450379in}{0.738286in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.614773in}{0.843810in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.779167in}{0.949334in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.943561in}{1.107619in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.107955in}{1.213143in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.272348in}{1.318667in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.436742in}{1.424191in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.601136in}{1.529715in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.765530in}{1.635239in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.929924in}{1.740763in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.094318in}{1.793525in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.258712in}{1.899049in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.423106in}{1.951811in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.587500in}{2.057335in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.751894in}{2.162858in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{3.916288in}{2.268382in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.080682in}{2.373906in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.245076in}{2.479430in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.409470in}{2.584954in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.573864in}{2.637716in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.738258in}{2.690478in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{4.902652in}{2.743240in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{5.067045in}{2.796002in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{5.231439in}{2.901526in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{5.395833in}{2.954288in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{5.560227in}{3.059812in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{5.724621in}{3.112574in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{5.889015in}{3.218097in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{6.053409in}{3.270859in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.690196,0.690196,0.690196}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{1.121591in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{1.121591in}{3.520000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{0.000000in}{-0.048611in}}{\pgfqpoint{0.000000in}{0.000000in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.048611in}}% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{1.121591in}{0.440000in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=1.121591in,y=0.342778in,,top]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle {0}\)}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.690196,0.690196,0.690196}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{1.943561in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{1.943561in}{3.520000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{0.000000in}{-0.048611in}}{\pgfqpoint{0.000000in}{0.000000in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.048611in}}% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{1.943561in}{0.440000in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=1.943561in,y=0.342778in,,top]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle {5}\)}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.690196,0.690196,0.690196}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{2.765530in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{2.765530in}{3.520000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{0.000000in}{-0.048611in}}{\pgfqpoint{0.000000in}{0.000000in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.048611in}}% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{2.765530in}{0.440000in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=2.765530in,y=0.342778in,,top]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle {10}\)}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.690196,0.690196,0.690196}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{3.587500in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{3.587500in}{3.520000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{0.000000in}{-0.048611in}}{\pgfqpoint{0.000000in}{0.000000in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.048611in}}% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{3.587500in}{0.440000in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=3.587500in,y=0.342778in,,top]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle {15}\)}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.690196,0.690196,0.690196}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{4.409470in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{4.409470in}{3.520000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{0.000000in}{-0.048611in}}{\pgfqpoint{0.000000in}{0.000000in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.048611in}}% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{4.409470in}{0.440000in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=4.409470in,y=0.342778in,,top]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle {20}\)}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.690196,0.690196,0.690196}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{5.231439in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{5.231439in}{3.520000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{0.000000in}{-0.048611in}}{\pgfqpoint{0.000000in}{0.000000in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.048611in}}% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{5.231439in}{0.440000in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=5.231439in,y=0.342778in,,top]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle {25}\)}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.690196,0.690196,0.690196}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{6.053409in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.053409in}{3.520000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{0.000000in}{-0.048611in}}{\pgfqpoint{0.000000in}{0.000000in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.048611in}}% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{6.053409in}{0.440000in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=6.053409in,y=0.342778in,,top]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle {30}\)}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=3.587500in,y=0.163766in,,top]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle t\), c}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetrectcap% +\pgfsetroundjoin% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.690196,0.690196,0.690196}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.875000in}{0.580000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{0.580000in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.803000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.048611in}{0.000000in}}{\pgfqpoint{-0.000000in}{0.000000in}}{% +\pgfpathmoveto{\pgfqpoint{-0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{-0.048611in}{0.000000in}}% +\pgfusepath{stroke,fill}% +}% 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+\pgfpathmoveto{\pgfqpoint{1.000000in}{3.346389in}}% +\pgfpathlineto{\pgfqpoint{1.138889in}{3.346389in}}% +\pgfpathlineto{\pgfqpoint{1.277778in}{3.346389in}}% +\pgfusepath{stroke}% +\end{pgfscope}% +\begin{pgfscope}% +\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{textcolor}% +\pgfsetfillcolor{textcolor}% +\pgftext[x=1.388889in,y=3.297778in,left,base]{\color{textcolor}\rmfamily\fontsize{10.000000}{12.000000}\selectfont \(\displaystyle y=0.17x+1.17\)}% +\end{pgfscope}% +\end{pgfpicture}% +\makeatother% +\endgroup% diff --git a/2.3.1/Baldin_V/data/data4.csv b/2.3.1/Baldin_V/data/data4.csv new file mode 100644 index 00000000..fce5aa6d --- /dev/null +++ b/2.3.1/Baldin_V/data/data4.csv @@ -0,0 +1,15 @@ +t,P +0,5.9 +1,5.2 +2,4.3 +3,3.4 +4,2.7 +5,2.3 +6,1.9 +7,1.6 +8,1.4 +9,1.3 +10,1.2 +11,1.2 +12,1.1 +13,1.1 diff --git a/2.3.1/Baldin_V/data/data4.pgf b/2.3.1/Baldin_V/data/data4.pgf new file mode 100644 index 00000000..1dfa8359 --- /dev/null +++ b/2.3.1/Baldin_V/data/data4.pgf @@ -0,0 +1,1201 @@ +%% Creator: Matplotlib, PGF backend +%% +%% To include the figure in your LaTeX document, write +%% \input{.pgf} +%% +%% Make sure the required packages are loaded in your preamble +%% \usepackage{pgf} +%% +%% Also ensure that all the required font packages are loaded; for instance, +%% the lmodern package is sometimes necessary when using math font. +%% \usepackage{lmodern} +%% +%% Figures using additional raster images can only be included by \input if +%% they are in the same directory as the main LaTeX file. For loading figures +%% from other directories you can use the `import` package +%% \usepackage{import} +%% +%% and then include the figures with +%% \import{}{.pgf} +%% +%% Matplotlib used the following preamble +%% +%% \makeatletter\@ifpackageloaded{underscore}{}{\usepackage[strings]{underscore}}\makeatother +%% +\begingroup% +\makeatletter% +\begin{pgfpicture}% +\pgfpathrectangle{\pgfpointorigin}{\pgfqpoint{7.000000in}{4.000000in}}% +\pgfusepath{use as bounding box, clip}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetmiterjoin% +\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.000000pt}% +\definecolor{currentstroke}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{7.000000in}{0.000000in}}% +\pgfpathlineto{\pgfqpoint{7.000000in}{4.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{4.000000in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{0.000000in}}% +\pgfpathclose% +\pgfusepath{fill}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsetbuttcap% +\pgfsetmiterjoin% +\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{0.000000pt}% +\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetstrokeopacity{0.000000}% +\pgfsetdash{}{0pt}% +\pgfpathmoveto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{0.440000in}}% +\pgfpathlineto{\pgfqpoint{6.300000in}{3.520000in}}% +\pgfpathlineto{\pgfqpoint{0.875000in}{3.520000in}}% +\pgfpathlineto{\pgfqpoint{0.875000in}{0.440000in}}% +\pgfpathclose% +\pgfusepath{fill}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfpathrectangle{\pgfqpoint{0.875000in}{0.440000in}}{\pgfqpoint{5.425000in}{3.080000in}}% +\pgfusepath{clip}% +\pgfsetbuttcap% +\pgfsetroundjoin% +\definecolor{currentfill}{rgb}{0.121569,0.466667,0.705882}% +\pgfsetfillcolor{currentfill}% +\pgfsetlinewidth{1.003750pt}% +\definecolor{currentstroke}{rgb}{0.121569,0.466667,0.705882}% +\pgfsetstrokecolor{currentstroke}% +\pgfsetdash{}{0pt}% +\pgfsys@defobject{currentmarker}{\pgfqpoint{-0.015528in}{-0.015528in}}{\pgfqpoint{0.015528in}{0.015528in}}{% +\pgfpathmoveto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathcurveto{\pgfqpoint{0.004118in}{-0.015528in}}{\pgfqpoint{0.008068in}{-0.013892in}}{\pgfqpoint{0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{0.013892in}{-0.008068in}}{\pgfqpoint{0.015528in}{-0.004118in}}{\pgfqpoint{0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{0.015528in}{0.004118in}}{\pgfqpoint{0.013892in}{0.008068in}}{\pgfqpoint{0.010980in}{0.010980in}}% +\pgfpathcurveto{\pgfqpoint{0.008068in}{0.013892in}}{\pgfqpoint{0.004118in}{0.015528in}}{\pgfqpoint{0.000000in}{0.015528in}}% +\pgfpathcurveto{\pgfqpoint{-0.004118in}{0.015528in}}{\pgfqpoint{-0.008068in}{0.013892in}}{\pgfqpoint{-0.010980in}{0.010980in}}% +\pgfpathcurveto{\pgfqpoint{-0.013892in}{0.008068in}}{\pgfqpoint{-0.015528in}{0.004118in}}{\pgfqpoint{-0.015528in}{0.000000in}}% +\pgfpathcurveto{\pgfqpoint{-0.015528in}{-0.004118in}}{\pgfqpoint{-0.013892in}{-0.008068in}}{\pgfqpoint{-0.010980in}{-0.010980in}}% +\pgfpathcurveto{\pgfqpoint{-0.008068in}{-0.013892in}}{\pgfqpoint{-0.004118in}{-0.015528in}}{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathlineto{\pgfqpoint{0.000000in}{-0.015528in}}% +\pgfpathclose% +\pgfusepath{stroke,fill}% +}% +\begin{pgfscope}% +\pgfsys@transformshift{1.121591in}{3.341149in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.500962in}{3.195392in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{1.880332in}{2.971714in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% +\pgfsys@transformshift{2.259703in}{2.686156in}% +\pgfsys@useobject{currentmarker}{}% +\end{pgfscope}% +\begin{pgfscope}% 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files: + data += [pd.read_csv(f)] + return data + +def drawGraphs(data, files): + if data.__len__() != files.__len__(): + raise 'mismatch of sizes' + for i in range(data.__len__()): + plt.figure(figsize=(7, 4)) + plt.xlabel('$t$, c') + x = data[i]['t'] + z = np.linspace(0, max(x), num=1000) + if i % 2 == 0: + y = data[i]['P'] + plt.ylabel('$P$, $10^{-4}$ torr') + plt.errorbar(x, y, xerr=0, yerr=0.1, fmt='k.') + else: + P_LIM = 0.56 + y = [np.log(data[i]['P'][j] - P_LIM) + for j in range(data[i].__len__())] + plt.errorbar(x, y, xerr=0, yerr=[np.abs(0.2/(j - P_LIM)) + for j in data[i]['P']], fmt='k.') + plt.ylabel('$\ln (P - P_{{lim}})$') + plt.scatter(x, y, s=5) + [a, b] = np.polyfit(x, y, deg=1) + plt.plot(z, a * z + b, label=f'$y={a:.2f}x+{b:.2f}') + plt.grid() + plt.legend() + plt.savefig(files[i]) + +data = readFiles(['data1.csv', 'data2.csv', 'data3.csv', 'data4.csv']) +drawGraphs(data, ['data1.pgf', 'data2.pgf', 'data3.pgf', 'data4.pgf']) diff --git a/2.3.1/Baldin_V/stand.png b/2.3.1/Baldin_V/stand.png new file mode 100644 index 00000000..cdbf0747 Binary files /dev/null and b/2.3.1/Baldin_V/stand.png differ diff --git a/2.3.1/pdf/Baldin_V.pdf b/2.3.1/pdf/Baldin_V.pdf new file mode 100644 index 00000000..5064812e Binary files /dev/null and b/2.3.1/pdf/Baldin_V.pdf differ diff --git a/2.4.1/Baldin_V/2.4.1.ipynb b/2.4.1/Baldin_V/2.4.1.ipynb new file mode 100644 index 00000000..34ccf60d --- /dev/null +++ b/2.4.1/Baldin_V/2.4.1.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Определение теплоты испарения жидкости" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a = -5.32, sigma = 0.10\n", + "a = -4.27, sigma = 0.23\n", + "L = 43601\n", + "L = 43601\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib\n", + "from matplotlib import pyplot as plt\n", + "from scipy.optimize import minimize\n", + "from matplotlib.backends.backend_pgf import FigureCanvasPgf\n", + "matplotlib.backend_bases.register_backend('pdf', FigureCanvasPgf)\n", + "\n", + "matplotlib.rcParams.update({\n", + " \"pgf.texsystem\": \"pdflatex\",\n", + " 'text.usetex': True,\n", + " 'font.family': 'serif',\n", + " 'pgf.rcfonts': False,\n", + "})\n", + "\n", + "def read_files(files):\n", + " data = []\n", + " for f in files:\n", + " data += [pd.read_csv(f, sep=',', skipinitialspace=True)]\n", + " return data\n", + "\n", + "def get_linear(x, y, xerr, yerr):\n", + " [a, b], cov = np.polyfit(x, y, deg=1, cov=True)\n", + " aerr = np.sqrt(np.diag(cov))[0]\n", + " syserr = np.sqrt((np.average(xerr) / (max(x) - min(x)))**2\n", + " + (np.average(yerr) / (max(y) - min(y)))**2)\n", + " a_syserr = a * syserr\n", + " berr = aerr * np.sqrt(np.average([it**2 for it in x]))\n", + " b_syserr = b * syserr\n", + " return ((a, b), (np.sqrt(aerr**2 + a_syserr**2), np.sqrt(berr**2 + b_syserr**2)))\n", + "\n", + "def fit(f, params, x, y):\n", + " def err(par, x_, y_):\n", + " y1 = f(par, x_)\n", + " return np.sum((y1 - y_)**2)\n", + "\n", + " return minimize(err, x0=params, args=(x, y)).x\n", + "\n", + "# par[0] = L\n", + "def pressure(par, T):\n", + " return par[1] * np.exp(par[0] / 8.31 * (par[2] - 1 / (T + 273)))\n", + "\n", + "def dp(par, T):\n", + " return par[0] / 8.31 * 1 / ((T + 273)**2) * pressure(par, T)\n", + "\n", + "labels = ['Heating', 'Cooling']\n", + "\n", + "[data, cooling] = read_files(['data/data.csv', 'data/cooling.csv'])\n", + "colors = ['red', 'green']\n", + "\n", + "plt.figure(figsize=(7, 4))\n", + "\n", + "i = 0\n", + "for it in [data, cooling]:\n", + " inverse_T = it['1/T']\n", + " ln_P = it['lnP']\n", + " plt.errorbar(inverse_T, ln_P, xerr=0, yerr=0, fmt=f'{colors[i][0]}.')\n", + " ((a, b), (aerr, berr)) = get_linear(inverse_T, ln_P, [0], [0])\n", + " print(f'a = {a:.2f}, sigma = {aerr:.2f}')\n", + " z = np.linspace(min(inverse_T), max(inverse_T), 1000)\n", + " plt.plot(z, a * z + b, color=colors[i], label=labels[i])\n", + " i += 1\n", + "\n", + "# Оформление\n", + "plt.xlabel('$1 / T$, $10^{-3}$ K$^{-1}$')\n", + "plt.ylabel('$\\\\ln P$')\n", + "plt.grid(linestyle='--')\n", + "plt.legend()\n", + "plt.savefig('data/lin.pdf')\n", + "\n", + "plt.figure(figsize=(7, 4))\n", + "\n", + "i = 0\n", + "params = [0] * 3\n", + "for it in [data, cooling]:\n", + " T = it['T']\n", + " P = it['P']\n", + " plt.errorbar(T, P, xerr=0.1, yerr=0.1, fmt=f'{colors[i][0]}.')\n", + " params = fit(pressure, params, T, P)\n", + "\n", + " z = np.linspace(min(T), max(T), 1000)\n", + " if i == 0:\n", + " # Tangents\n", + " start = min(T)\n", + " end = max(T)\n", + " plt.plot(z, dp(params, start) * (z - start) + pressure(params, start), color='red')\n", + " plt.plot(z, dp(params, end) * (z - end) + pressure(params, end), color='red')\n", + " plt.ylim((40, 140))\n", + " print(f'L = {params[0]:.0f}')\n", + " plt.plot(z, pressure(params, z), color=colors[i], label=labels[i])\n", + " i += 1\n", + "\n", + "# Оформление\n", + "plt.xlabel('$T$, \\\\textdegree C')\n", + "plt.ylabel('$P$, mm hg')\n", + "plt.grid(linestyle='--')\n", + "plt.legend()\n", + "plt.savefig('data/curve.pdf')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2.4.1/Baldin_V/2.4.1.pdf b/2.4.1/Baldin_V/2.4.1.pdf new file mode 100644 index 00000000..ab2a54fc Binary files /dev/null and b/2.4.1/Baldin_V/2.4.1.pdf differ diff --git a/2.4.1/Baldin_V/2.4.1.tex b/2.4.1/Baldin_V/2.4.1.tex new file mode 100644 index 00000000..952b070d --- /dev/null +++ b/2.4.1/Baldin_V/2.4.1.tex @@ -0,0 +1,186 @@ +\documentclass[12pt]{article} +\usepackage{amsmath} +\usepackage{amsfonts} +\usepackage{amssymb} +\usepackage[utf8]{inputenc} +\usepackage[T1,T2A]{fontenc} +\usepackage[english, russian]{babel} +\usepackage{graphicx} +\usepackage{float} +\usepackage[left=2cm,right=2cm,top=2cm,bottom=2cm]{geometry} +\usepackage{wrapfig} +\usepackage{pgfplots} +\usepackage{setspace} +\usepackage{indentfirst} +\usepackage{subfigure} +\usepackage{hyperref} +\usepackage{mathrsfs} + +\hypersetup{ + colorlinks=true, + linkcolor=red, + urlcolor=magenta, +} + +\graphicspath{{pictures}} + +\title{ + Лабораторная работа 2.4.1 \\ + <<Определение теплоты испарения жидкости>> +} + +\author{Балдин Виктор, Б01-303} + +\begin{document} + \maketitle + \paragraph{Цель работы} + \begin{enumerate} + \item Измерение давления насыщенного пара жидкости при различной + температуре. + \item Вычисление теплоты испарения с помощью уравнения Клайперона- + Клаузиуса. + \end{enumerate} + \paragraph{Оборудование} Термостат, герметичный сосуд, заполненный + исследуемой жидкостью, отсчетный микроскоп. + + \section{Теоретическая часть} + Для теплоты испарения можно записать уравнение Клайперона-Клаузиуса: + \begin{equation} + \frac{dP}{dT} = \frac{L}{T(V_2 - V_1)}, + \label{klaus} + \end{equation} + где $V_2$ -- объем газа, $V_1$ -- объем жидкости. + + В данном случае мы используем модель идеального газа применительно + к парам исследуемой жидкости: + \begin{equation} + V = \frac{RT}{P} + \label{klaip} + \end{equation} + + Объединяя \ref{klaus} и \ref{klaip}, получим: + \begin{equation} + L = \frac{RT^2}{P}\frac{dP}{dT} = -R\frac{d(\ln P)}{d(1 / T)} + \label{L} + \end{equation} + + Или в интегральной форме: + \begin{equation} + P = P_0 \exp \left(\frac{L}{R}\left(\frac{1}{T_0} - \frac{1}{T}\right)\right) + \label{integral} + \end{equation} + + \section{Установка} + Схема экспериментальной установки приведена на рисунке \ref{stand}. + Погрешности: термостат -- 0.1 \textdegree C, манометр -- 0.1 мм. рт. ст.. + + \begin{figure}[h!] + \centering + \includegraphics[scale=1]{stand.png} + \caption{Схема установки для определения удельной теплоты испарения} + \label{stand} + \end{figure} + + \section{Ход работы} + \begin{enumerate} + \item Измерим разность уровней в ртутном U-образом манометре с помощью + микроскопа. + \item Включим термостат. + \item Будем измерять давление насыщенного пара с интервалом 1 \textdegree C + до 40 \textdegree C. + Данные занесем в таблицу \ref{heat}. + \item Теперь установим термостат на комнатную температуру и снимем еще немного + точек при охлаждении с интервалом 2 \textdegree C для верификации результатов + предыдущего пункта. Результаты в таблице \ref{cool}. Систематические погрешности + для величин $1/T$ и $\ln P$ много меньше случайных. + + \begin{table}[H] + \centering + \begin{tabular}{|c|c|c|c|} + \hline + $T$, \textdegree C & $P$, мм. рт. ст. & $1/T$, K$^{-1}$ & $\ln P$ \\ \hline + 23 & 47.70 & 3.38 & 3.86 \\ \hline + 24 & 49.96 & 3.37 & 3.91 \\ \hline + 25 & 52.05 & 3.36 & 3.95 \\ \hline + 26 & 54.19 & 3.34 & 3.99 \\ \hline + 27 & 58.38 & 3.33 & 4.07 \\ \hline + 28 & 61.95 & 3.32 & 4.13 \\ \hline + 29 & 66.56 & 3.31 & 4.20 \\ \hline + 30 & 70.81 & 3.30 & 4.26 \\ \hline + 31 & 74.65 & 3.29 & 4.31 \\ \hline + 32 & 79.05 & 3.28 & 4.37 \\ \hline + 33 & 84.40 & 3.27 & 4.44 \\ \hline + 34 & 89.56 & 3.26 & 4.49 \\ \hline + 35 & 93.99 & 3.25 & 4.54 \\ \hline + 36 & 97.62 & 3.24 & 4.58 \\ \hline + 37 & 104.13 & 3.23 & 4.65 \\ \hline + 38 & 110.42 & 3.22 & 4.70 \\ \hline + 39 & 116.00 & 3.21 & 4.75 \\ \hline + 40 & 121.86 & 3.19 & 4.80 \\ \hline + \end{tabular} + \caption{Измерения $P(T)$ при нагревании} + \label{heat} + \end{table} + + \begin{table}[H] + \centering + \begin{tabular}{|c|c|c|c|} + \hline + $T$, \textdegree C & $P$, мм. рт. ст. & $1/T$, K$^{-1}$ & $\ln P$ \\ \hline + 40 & 121.86 & 3.19 & 4.80 \\ \hline + 38 & 108.81 & 3.22 & 4.69 \\ \hline + 36 & 99.01 & 3.24 & 4.60 \\ \hline + 34 & 89.86 & 3.26 & 4.50 \\ \hline + \end{tabular} + \caption{Измерения $P(T)$ при охлаждении} + \label{cool} + \end{table} + + \item Построим графики по полученным данным в координатах + $P(T)$ на рис. \ref{curve} и $\ln P(1/T)$ на рис. \ref{lin}. + Для графика $P(T)$ проведем наилучшую кривую, соответветствующую формуле + \ref{integral}, пользуясь методом МНК. + + \begin{figure}[H] + \centering + \includegraphics{data/curve.pdf} + \caption{Зависимость $P(T)$} + \label{curve} + \end{figure} + + \begin{figure}[H] + \centering + \includegraphics{data/lin.pdf} + \caption{Линеаризованная зависимость $\ln P(1 / T)$} + \label{lin} + \end{figure} + + \item По линеаризованному графику: + $$ + \frac{d(\ln P)}{d(1/T)} = (-5.3 \pm 0.1)\cdot 10^3\ \text{K}, \ \varepsilon = 2\% + $$ + При этом погрешность считалась равной случайной погрешности аппроксимации, в силу + малости приборных: $\sigma \approx \sigma^{\text{случ}}$. + Отсюда по формуле \ref{L} получим: + $$ + L = (44.0 \pm 0.9)\ \text{кДж} / \text{моль},\ \varepsilon = 2\% + $$ + + \item Получим $L$ из изначального графика. Для этого проведем касательные к графику + в начале, в середине и в конце (см. рис. \ref{curve}). Из аппроксимации получим: + $$ + L = (43.6\pm 0.9)\ \text{кДж} / \text{моль},\ \varepsilon = 2\% + $$ + Усреднив полученные значения, найдем: + $$ + \overline{L} = (43.8 \pm 0.9)\ \text{кДж}/\text{моль},\ \varepsilon = 2\% + $$ + \end{enumerate} + + \section{Вывод} + Полученные нами разными методами значения теплты испарения $L$ лежат + в пределах погрешности друг друга. Табличное значение для нормальных + условий $L_{\text{табл}} = 40.7$ кДж/моль. Это не слишком отличается + от измеренного нами. Значит, эксперимент можно считать довольно + качественным. +\end{document} diff --git a/2.4.1/Baldin_V/data/cooling.csv b/2.4.1/Baldin_V/data/cooling.csv new file mode 100644 index 00000000..0a46a27b --- /dev/null +++ b/2.4.1/Baldin_V/data/cooling.csv @@ -0,0 +1,5 @@ +T,P,1/T,lnP +40,121.86,3.19,4.80 +38,108.81,3.22,4.69 +36,99.01,3.24,4.60 +34,89.86,3.26,4.50 diff --git a/2.4.1/Baldin_V/data/curve.pdf b/2.4.1/Baldin_V/data/curve.pdf new file mode 100644 index 00000000..0f8925f9 Binary files /dev/null and b/2.4.1/Baldin_V/data/curve.pdf differ diff --git a/2.4.1/Baldin_V/data/data.csv b/2.4.1/Baldin_V/data/data.csv new file mode 100644 index 00000000..ea790360 --- /dev/null +++ b/2.4.1/Baldin_V/data/data.csv @@ -0,0 +1,19 @@ +T,P,1/T,lnP +23,47.70,3.38,3.86 +24,49.96,3.37,3.91 +25,52.05,3.36,3.95 +26,54.19,3.34,3.99 +27,58.38,3.33,4.07 +28,61.95,3.32,4.13 +29,66.56,3.31,4.20 +30,70.81,3.30,4.26 +31,74.65,3.29,4.31 +32,79.05,3.28,4.37 +33,84.40,3.27,4.44 +34,89.56,3.26,4.49 +35,93.99,3.25,4.54 +36,97.62,3.24,4.58 +37,104.13,3.23,4.65 +38,110.42,3.22,4.70 +39,116.00,3.21,4.75 +40,121.86,3.19,4.80 diff --git a/2.4.1/Baldin_V/data/lin.pdf b/2.4.1/Baldin_V/data/lin.pdf new file mode 100644 index 00000000..f3abe5a3 Binary files /dev/null and b/2.4.1/Baldin_V/data/lin.pdf differ diff --git a/2.4.1/Baldin_V/pictures/stand.png b/2.4.1/Baldin_V/pictures/stand.png new file mode 100644 index 00000000..02f353f9 Binary files /dev/null and b/2.4.1/Baldin_V/pictures/stand.png differ diff --git a/2.4.1/pdf/Baldin_V.pdf b/2.4.1/pdf/Baldin_V.pdf new file mode 100644 index 00000000..ab2a54fc Binary files /dev/null and b/2.4.1/pdf/Baldin_V.pdf differ diff --git a/2.5.1/Baldin_V/2.5.1.ipynb b/2.5.1/Baldin_V/2.5.1.ipynb new file mode 100644 index 00000000..96366272 --- /dev/null +++ b/2.5.1/Baldin_V/2.5.1.ipynb @@ -0,0 +1,84 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Поверхностное натяжение" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib\n", + "from matplotlib import pyplot as plt\n", + "\n", + "matplotlib.use('pgf')\n", + "matplotlib.rcParams.update({\n", + " 'pgf.texsystem': 'pdflatex',\n", + " 'font.family': 'serif',\n", + " 'text.usetex': True,\n", + " 'pgf.rcfonts': False,\n", + "})\n", + "\n", + "def read_files(files):\n", + " data = []\n", + " for f in files:\n", + " data += [pd.read_csv(f, sep=',', skipinitialspace=True)]\n", + " return data\n", + "\n", + "[sigma] = read_files(['data/sigma_T.csv'])\n", + "\n", + "def plot_linear(x, y, label, color):\n", + " z = np.linspace(min(x), max(x), 1000)\n", + " a, b = np.polyfit(x, y, deg=1)\n", + " plt.plot(z, a * z + b, label=label, color=color)\n", + " \n", + "plt.figure(figsize=(7, 4))\n", + "\n", + "# Аппроксимация\n", + "plot_linear(sigma['T'], sigma['sigma'], label='$\\\\sigma$', color='blue')\n", + "plot_linear(sigma['T'], sigma['q'], label='$q$', color='red')\n", + "plot_linear(sigma['T'], sigma['U_F'], label='$U/\\Pi$', color='green')\n", + "\n", + "# Сами точки\n", + "plt.errorbar(sigma['T'], sigma['sigma'], xerr=0.2, yerr=sigma['dsigma'], fmt='b.')\n", + "plt.errorbar(sigma['T'], sigma['q'], xerr=0.2, yerr=sigma['dq'], fmt='r.')\n", + "plt.errorbar(sigma['T'], sigma['U_F'], xerr=0.2, yerr=sigma['dU_F'], fmt='g.',\n", + " ecolor='green')\n", + "\n", + "# Оформление\n", + "plt.xlabel('$T$, \\\\textdegree C')\n", + "plt.grid(linestyle='--')\n", + "plt.legend()\n", + "plt.savefig('data/sigma_T.pgf')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2.5.1/Baldin_V/2.5.1.pdf b/2.5.1/Baldin_V/2.5.1.pdf new file mode 100644 index 00000000..68f5308c Binary files /dev/null and b/2.5.1/Baldin_V/2.5.1.pdf differ diff --git a/2.5.1/Baldin_V/2.5.1.tex b/2.5.1/Baldin_V/2.5.1.tex new file mode 100644 index 00000000..ec2663ae --- /dev/null +++ b/2.5.1/Baldin_V/2.5.1.tex @@ -0,0 +1,268 @@ +\documentclass[12pt]{article} +\usepackage{amsmath} +\usepackage{amsfonts} +\usepackage{amssymb} +\usepackage[utf8]{inputenc} +\usepackage[T1,T2A]{fontenc} +\usepackage[english, russian]{babel} +\usepackage{graphicx} +\usepackage{float} +\usepackage[left=2cm,right=2cm,top=2cm,bottom=2cm]{geometry} +\usepackage{wrapfig} +\usepackage{pgfplots} +\usepackage{setspace} +\usepackage{indentfirst} +\usepackage{subfigure} +\usepackage{hyperref} + +\hypersetup{ + colorlinks=true, + linkcolor=red, + urlcolor=magenta, +} + +\graphicspath{{pictures}} + +\title{ + Лабораторная работа 2.5.1 \\ + <<Измерение коэффициента поверхностного натяжения жидкости>> +} + +\author{Балдин Виктор, Б01-303} + +\begin{document} + \maketitle + \section{Аннотация} + \paragraph{Цель работы.} + \begin{enumerate} + \item Измерение коэффициента поверхностного натяжения исследуемой + жидкости при разных температурах. + \item Определение полной поверхностной энергии и теплоты, необходимой + для изотермического образования единицы поверхности жидкости. + \end{enumerate} + \paragraph{Оборудование.} + Прибор Ребиндера с термостатом; исследуемые жидкости; стаканы. + + \section{Теоретическая часть} + Наличие поверхностного слоя приводит к различию давлений по разные стороны + от искривлённой границы раздела двух сред. Для сферического пузырька внутри + жидкости избыточное давление даётся формулой Лапласа (5.15): + \begin{equation} + \Delta P=P_{\text {внутри }}-P_{\text {Снаружи }}=\frac{2 \sigma}{r} + \label{sigma} + \end{equation} + + Эта формула лежит в основе предлагаемого метода определения коэффициента + поверхностного натяжения жидкости. Измеряется давление, необходимое для + выталкивания в жидкость пузырька газа. + + Таким образом, по формуле \ref{sigma} можно найти коэффициент + поверхностного натяжения по разности давлений. + + \paragraph{Термодинамика поверхностного натяжения.} + Запишем первое начало термодинамики для пленки: + \begin{equation} + \delta Q = dU - \sigma d\Pi + \end{equation} + + Введем энтропию: + \begin{equation} + dU = TdS + \sigma d\Pi + \label{dU} + \end{equation} + + Для свободной энергии: + \begin{equation} + dF = -SdT + \sigma d\Pi, + \end{equation} + + отсюда + \begin{eqnarray} + S = -\left(\frac{\partial F}{\partial T}\right)_{\Pi}, \label{S}\\ + \sigma = -\left(\frac{\partial F}{\partial \Pi}\right)_T. + \label{dFdPi} + \end{eqnarray} + + Проинтегрируем \ref{dFdPi}, считая $F = 0$ при $\Pi = 0$: + \begin{equation} + F = \sigma \Pi + \end{equation} + + Подставим это в формулу \ref{S}: + \begin{equation} + S = -\Pi \frac{d\sigma}{dT} + \end{equation} + + Тогда формула \ref{dU} примет вид: + \begin{equation} + U = \left(\sigma - T\frac{d\sigma}{dT}\right)\Pi + \label{U} + \end{equation} + + Можно ввести теплоту образования поверхности на единицу площади + \begin{equation} + q = -T\frac{d\sigma}{dT} + \label{q} + \end{equation} + + \section{Экспериментальная установка} + Исследуемая жидкость (этиловый спирт) наливается в сосуд В. + Дистиллированная вода наливается в сосуд Е. Сосуды закрыты пробками. Через + пробку сосуда, в котором проводятся измерения, проходит полая металлическая игла + С, нижний конец которой погружен в жидкость, а верхний открыт в атмосферу. Если + другой сосуд герметично закрыт, то в сосуде с иглой создается разрежение, + и пузырьки воздуха начинают пробулькивать через жидкость через жидкость. + + \begin{figure}[H] + \centering + \includegraphics[scale=2]{stand.png} + \caption{ + Схема установки для измерения температурной зависимости + коэффициента поверхностного натяжения + } + \end{figure} + Для стабилизации температуры исследуемой жидкости через рубашку D непрерывно + прогоняется вода из термостата. + + Обычно кончик иглы лишь касаёется поверхности жидкости, чтобы исключить + влияние гидростатического давления столба жидкости. Однако при измерении + температурной зависимости коэффициента поверхностного натяжения возникает + ряд сложностей. Во-первых, большая теплопроводность металлической трубки + приводит к тому, что температура на конце трубки заметно ниже, чем в глубине + жидкости. Вовторых, тепловое расширение поднимает уровень жидкости при + увеличении температуры. Это гидростатическое давление вычитается из падения + лапласова давления вследствие уменьшения $\sigma$, и в опыте с анилином, + например, наблюдаемый эффект меняет знак при высоте столба жидкости порядка + пяти сантиметров. + + Обе погрешности можно устранить, погрузив кончик трубки до самого дна. + Полное давление, измеренное при этом микроманометром, $P=\Delta P+\rho g h$. + Заметим, что $\rho g h$ от температуры практически не зависит, так как + подъём уровня жидкости компенсируется уменьшением её плотности (произведение + $\rho h$ определяется массой всей жидкости и поэтому постоянно). Величину + $\rho g h$ следует измерить экспериментально двумя методами. Во-первых, + замерить величину $\Delta P_1 = \Delta P'$, когда кончик трубки только + касается поверхности жидкости. Затем при этой же температуре опустить иглу + до дна и замерять $P_2 = \rho gh + \Delta P''$. Из-за несжимаемости + жидкости можно положить $\Delta P' = \Delta P''$ и тогда $\rho gh = P_2 - + P_1$. Во-вторых, при измерениях $P_1$ и $P_2$ замерить линейкой глубину + погружения иглы $h_1$ и $h_2$. Это легко сделать, замеряя расстояние + между верхним концом иглы и любой неподвижной частью прибора. + + \section{Ход работы} + \begin{enumerate} + \item Убедимся в исправности установки. + \item Подберем частоту падения капель так, чтобы максимальное + давление оставалось постоянным (примерно по 1 капле в 5 секунд). + \item Опустим иглу так, чтобы она коснулась поверхности спирта. + Откроем кран К1 и измерим максимальное давление 3 раза. При этом + погрешность считаем равной $\Delta P = 1$ мм, а манометр установлен + с угловым коэффицентом $K = 0.2$. Плотность спирта в манометре + указана $\rho_{\text{спирта}} = 809.5$ кг/м$^3$. + \begin{table}[H] + \centering + \begin{tabular}{|c|c|c|c|} + \hline + $N$ & 1 & 2 & 3 \\ \hline + $P_{\text{спирта}}$, мм & 41 & 42 & 43 \\ \hline + \end{tabular} + \caption{Измерения давления при пробулькивании через спирт} + \end{table} + Отсюда получим $\left = 42$ мм, $\Delta P_ + {\text{спирта}} = \sigma_P^{\text{случ}} + \Delta P = 2 $ мм. + \item Вычислим по полученному значению радиус иглы с помощью + формулы \ref{sigma}, получим $r = (0.68 \pm 0.02)$ мм, + $\varepsilon_r = 3$\%. + \item При помощи микроскопа получим $r = (0.53\pm 0.03)$ мм, + $\varepsilon_r = 5$\%. + \item Очевидно, что измерение радиуса через поверхностное натяжение + спирта получилось неточным. Возможно, это связано с загрязнением + иглы. + \item Достанем иглу из спирта, прочистим ее и вставим в воду. + Сначала проведем измерения аналогично п. 3, получим $P_1 = + 113$ мм при $h_1 = 60$ мм. Потом погрузим иглу до упора, получим + давление $P_2 = 186$ мм при $h_2 = 46$ мм. Считаем $\Delta h = 1$ мм. + \item Через разность давлений получим значение $\Delta h = (12 \pm 1)$ + мм, $\varepsilon_h = 3$\%. + При помощи линейки добиваемся $\Delta h = (14 \pm 2)$ мм, + $\varepsilon_h = 14$\%. Как видно, + в пределах погрешностей значения совпадают, при этом гораздо точнее + метод измерения по разности давлений. + \item Включим термостат. + \item Измерим зависимость давления, которое показывает манометр, + от температуры при нагревании термостатом. Данные занесем в таблицу + \ref{table}. + \begin{table}[H] + \centering + \begin{tabular}{|c|c|c|c|c|c|c|c|} + \hline + $T$, \textdegree C & $P$, мм & $\sigma$, мН/м & $\Delta\sigma$, мН/м & $q$, мН/м & $\Delta q$, мН/м & $U/\Pi$, мН/м & $\Delta (U/\Pi)$, мН/м \\ \hline + 18.3 & 186 & 76.7 & 0.4 & 52.4 & 3.1 & 129.2 & 3.6 \\ \hline + 25.0 & 182 & 75.1 & 0.4 & 53.6 & 3.2 & 128.7 & 3.6 \\ \hline + 30.1 & 180 & 74.3 & 0.4 & 54.6 & 3.3 & 128.8 & 3.7 \\ \hline + 35.0 & 178 & 73.4 & 0.4 & 55.4 & 3.3 & 128.9 & 3.7 \\ \hline + 40.1 & 176 & 72.6 & 0.4 & 56.4 & 3.4 & 129.0 & 3.8 \\ \hline + 44.4 & 174 & 71.8 & 0.4 & 57.1 & 3.4 & 128.9 & 3.8 \\ \hline + 49.5 & 172 & 71.0 & 0.4 & 58.1 & 3.5 & 129.0 & 3.9 \\ \hline + 54.5 & 170 & 70.1 & 0.4 & 58.9 & 3.5 & 129.1 & 3.9 \\ \hline + 59.8 & 167 & 68.9 & 0.4 & 59.9 & 3.6 & 128.8 & 4.0 \\ \hline + \end{tabular} + \caption{Зависимости $\sigma(T)$, $q(T)$ и $U/\Pi(T)$} + \label{table} + \end{table} + \item Вычислим поверхностное натяжение $\sigma(T)$ по формуле + \ref{sigma} теплоту образования единицы площади поверхности $q(T)$ + по формуле \ref{q} и + поверхностную энергию единицы площади $U/\Pi(T)$ по формуле \ref{U}. + Данные занесем в таблицу \ref{table}. + \item Построим на одном графике полученные зависимости + (см. рис. \ref{graph}). + \begin{figure}[h!] + \centering + \input{data/sigma_T.pgf} + \caption{Графики $\sigma(T)$, $q(T)$, $U/\Pi(T)$} + \label{graph} + \end{figure} + \item Видно, что точки $\sigma(T)$ хорошо ложатся на прямую, так что + имеет смысл провести линейную аппроксимацию всех представленных + зависимостей. Угловые коэффиценты и их погрешность считаем по формулам: + \begin{eqnarray} + \frac{d\sigma}{dT} = \frac{\left} + {\left}, \label{koef} \\ + \sigma_{d\sigma/dT}^{\text{случ}} = + \sqrt{\frac{1}{N - 1}\left(\frac{\left<\sigma^2\right>} + {\left} - + \left(\frac{d\sigma}{dT}\right)^2\right)}\label{dkoef}. + \end{eqnarray} + + \item Вычисленные по формулам \ref{koef} и \ref{dkoef} + угловые коэффициенты и их погрешности + представлены в таблице: + \begin{table}[H] + \centering + \begin{tabular}{|c|c|c|c|} + \hline + & $d\sigma/dT$ & $dq/dT$ & $d(U/\Pi)/dT$ \\ \hline + Значение, мН/(м$\cdot$К) & $-0.18 \pm 0.01$ & $0.18\pm 0.01 $ & $0.00 \pm 0.02$ \\ \hline + $\varepsilon$, \% & 3 & 6 & \\ \hline + \end{tabular} + \end{table} + + \end{enumerate} + + \section{Вывод} + В результате данной работы мы: + \begin{enumerate} + \item Зафиксировали эффект поверхностного натяжения. + \item Измерили радиус иглы 2 методами. + \item Получили зависимость коэффициента поверхностного натяжения + $\sigma(T)$ для воды в диапазоне от 20 до 60 \textdegree C, а + также показали линейность зависимости на данном температурном + диапазоне. + \item Вычислили зависимость теплоты образования на единицу площади + $q(T)$. + \item Сложением получили $U/\Pi(T)$, доказали, что ее можно считать + постоянной. Таким образом, внутренняя энергия единицы площади + пленки не зависит от температуры. + \end{enumerate} +\end{document} diff --git a/2.5.1/Baldin_V/data/p_max.csv b/2.5.1/Baldin_V/data/p_max.csv new file mode 100644 index 00000000..17c6c613 --- /dev/null +++ b/2.5.1/Baldin_V/data/p_max.csv @@ -0,0 +1,2 @@ +P_max +sigma_{P_max} diff --git a/2.5.1/Baldin_V/data/sigma_T.csv b/2.5.1/Baldin_V/data/sigma_T.csv new file mode 100644 index 00000000..551b72d9 --- /dev/null +++ b/2.5.1/Baldin_V/data/sigma_T.csv @@ -0,0 +1,10 @@ +T,P,sigma,dsigma,q,dq,U_F,dU_F,,dsigma/dT,dq/dT,dU/dT +18.3,186,76.7,0.4,52.4,3.1,129.2,3.6,,,, +25.0,182,75.1,0.4,53.6,3.2,128.7,3.6,,,, +30.1,180,74.3,0.4,54.6,3.3,128.8,3.7,,,, +35.0,178,73.4,0.4,55.4,3.3,128.9,3.7,,,, +40.1,176,72.6,0.4,56.4,3.4,129.0,3.8,,,, +44.4,174,71.8,0.4,57.1,3.4,128.9,3.8,,,, +49.5,172,71.0,0.4,58.1,3.5,129.0,3.9,,,, +54.5,170,70.1,0.4,58.9,3.5,129.1,3.9,,,, +59.8,167,68.9,0.4,59.9,3.6,128.8,4.0,,,, diff --git a/2.5.1/Baldin_V/data/sigma_T.pgf b/2.5.1/Baldin_V/data/sigma_T.pgf new file mode 100644 index 00000000..7e92f679 --- /dev/null +++ b/2.5.1/Baldin_V/data/sigma_T.pgf @@ -0,0 +1,1467 @@ +%% Creator: Matplotlib, PGF backend +%% +%% To include the figure in your LaTeX document, write +%% \input{.pgf} +%% +%% Make sure the required packages are loaded in your preamble +%% \usepackage{pgf} +%% +%% Also ensure that all the required font packages are loaded; for instance, +%% the lmodern package is sometimes necessary when using math font. +%% \usepackage{lmodern} +%% +%% Figures using additional raster images can only be included by \input if +%% they are in the same directory as the main LaTeX file. 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mode 100644 index 00000000..5a24e289 Binary files /dev/null and b/3.1.3/Baldin_V/data.xlsx differ diff --git a/3.1.3/Baldin_V/graph.ipynb b/3.1.3/Baldin_V/graph.ipynb new file mode 100644 index 00000000..80996c42 --- /dev/null +++ b/3.1.3/Baldin_V/graph.ipynb @@ -0,0 +1,188 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "from scipy import stats\n", + "\n", + "df = pd.read_excel('data.xlsx', skiprows=1)\n", + "T = df['T, c']\n", + "n = df['n']" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_linear(x, y, xerr, yerr, xlabel, ylabel, file):\n", + " plt.figure(figsize=(7, 4))\n", + " plt.errorbar(x, y, fmt='b.', xerr=xerr, yerr=yerr)\n", + " res = stats.linregress(x, y)\n", + " print(res.intercept, res.slope)\n", + "\n", + " z = np.linspace(min(x), max(x), 1000)\n", + " plt.plot(z, res.slope * z + res.intercept, color='blue')\n", + "\n", + " plt.grid(which='major', linestyle='-')\n", + " plt.grid(which='minor', linestyle='--')\n", + " plt.minorticks_on()\n", + " plt.xlabel(xlabel)\n", + " plt.ylabel(ylabel)\n", + " plt.savefig(file)\n", + "\n", + " return res" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.27200000000000024 0.30016666666666664\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = n[~np.isnan(n)]\n", + "T = T[~np.isnan(T)]\n", + "\n", + "# print(n)\n", + "# print(T)\n", + "\n", + "res = plot_linear(x=n, y=T, xerr=0, yerr=0.04, xlabel='$n$', ylabel='$T$, c', file='graph.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.20728173584401843)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B_par = np.pi**2 * 8.27 * 0.63**2 / (3 * res.slope**2 * 57 * 10) - 1.7 / (57 * 10)\n", + "B_par" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_excel('data.xlsx', sheet_name='task3')\n", + "\n", + "N, d, m = df['N'], df['d, шар'], df['m, г']" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "g = 981\n", + "\n", + "M = [m[i] * g * d[i] * 0.63 for i in range(len(m))]" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-176.88018599999998 70.208208\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "res = plot_linear(x=N, y=M, xerr=0, yerr=[0.1 * m for m in M], xlabel='$n$', ylabel='$M$, ед. СГС', file='moment.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "# df['M, ед. СГС'] = M\n", + "# df.to_excel('data.xlsx', sheet_name='task3')" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "B_perp = res.slope" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/3.1.3/Baldin_V/graph.pdf b/3.1.3/Baldin_V/graph.pdf new file mode 100644 index 00000000..4c780962 Binary files /dev/null and b/3.1.3/Baldin_V/graph.pdf differ diff --git a/3.1.3/Baldin_V/lab 3_1_3 .pdf b/3.1.3/Baldin_V/lab 3_1_3 .pdf new file mode 100644 index 00000000..f8133a09 Binary files /dev/null and b/3.1.3/Baldin_V/lab 3_1_3 .pdf differ diff --git a/3.1.3/Baldin_V/lab 3_1_3 .tex b/3.1.3/Baldin_V/lab 3_1_3 .tex new file mode 100644 index 00000000..2194537b --- /dev/null +++ b/3.1.3/Baldin_V/lab 3_1_3 .tex @@ -0,0 +1,277 @@ +%FOR PDFLATEX USE ONLY +\documentclass[a4paper,12pt]{article} + +\usepackage{amssymb,amsmath} %math symbols + +\usepackage[margin=2cm]{geometry} %paper geometry + +\usepackage[utf8]{inputenc} %allows unicode (including russian) source file +\usepackage[russian]{babel} %docment in russian-style +\usepackage[utf8]{inputenc} +\usepackage[unicode]{hyperref} %links inside of the text +\usepackage[pdftex]{graphicx} %includegraphics pictures +%\usepackage{cmlgc} %bold text + +\usepackage{array} %arrays + +\usepackage{wrapfig} +\usepackage{array} +\usepackage{lipsum} +%\usepackage{esvect} +\usepackage{hyperref} +\usepackage{xcolor} +\definecolor{linkcolor}{HTML}{799B03} % цвет ссылок +\definecolor{urlcolor}{HTML}{799B03} % цвет гиперссылок + +\hypersetup{pdfstartview=FitH, linkcolor=linkcolor,urlcolor=urlcolor, colorlinks=true} + +\usepackage{subfig} +\usepackage{calc} +\usepackage{pgfplots,tikz,circuitikz} +\usepackage{pgfplotstable} +\usepackage{tkz-euclide} + +\usepackage{centernot} +\usepackage{cancel} + +\begin{document} + +\begin{center} + \LARGE{Работа 3.1.3}\\[0.2cm] + \LARGE{Измерение магнитного поля Земли}\\[0.2cm] + \large{Балдин Виктор}\\[0.2cm] +\end{center} + + +\section{Аннотация} + +В работе исследуются свойства постоянных неодимовых магнитов и с их помощью находится горизонтальная и вертикальная составляющие индукции магнитного поля Земли, а так же магнитное наклонение. + +\section{Теоретические сведения} + +Простейший магнитный диполь может быть образован витком с током или постоянным магнитом. По определению, магнитный момент $m$ тонкого витка площадью $S$ с током $I$ равен $\mathfrak{m} = \frac{I\textbf{S}}{c}, $где $\textbf{S} = S\textbf{n}$ - вектор площади контура, образующий с направлением тока правовинтовую систему, $\textbf{n}$ - единичный вектор нормали к площадке. Если размеры контура с током или магнитной стрелки малы по сравнению с расстоянием до диполя, то соответствующий магнитный диполь называют \textit{элементарным}, или \textit{точечным}. + +Магнитное поле точечного диполя определяется по формуле, анологичной формуле для поля элементарного электрического диполя: + +\[ \textbf{B}_{дип} = \frac{3(\mathfrak{m} \cdot \textbf{r})\textbf{r}}{r^5} - \frac{\textbf{m}}{r^3}\] + +Во внешнем магнитном поле с индукцией $\textbf{B}$ на точеный магнитный диполь $\mathfrak{m}$ действует механический момент сил $\textbf{M}=[\mathfrak{m}, \textbf{B}] $ +При этом потенциальная энергия которой обладает диполь с постоянным $\mathfrak{m}$, равна +$W = -(\mathfrak{m} \cdot \textbf{B})$ +Когда диполь ориентирован вдоль внешнего поля, он находится в состоянии \textit{равновесия}. + +В \textit{неоднородном} внешнем поле выражение для энергии постоянного диполя сохраняется. При этом кроме момента сил на диполь действует ещё и сила + +\[\textbf{F} = -\nabla W = (\mathfrak{m} \cdot \nabla)\textbf{B}\] + +Таким образом из вышесказанного следует, что \textit{свободный} магнитный диполь в неоднородном магнитном поле ориентируется вдоль силовых линий магнитного поля и втягивается в область более сильного поля, поскольку это ведёт к уменьшению энергии диполя. + +Выражения выше, позволяют рассчитать силу взаимодействия магнитов с моментами $\mathfrak{m_1}$ и $\mathfrak{m_2}$. Kогда моменты двух небольших магнитов направлены вдоль соединяющей их прямой: $\mathfrak{m_{1,2}} \| \textbf{r}$, где $\textbf{r}$ - радиус-вектор между ними, они взаимодействуют с силой +\[F_{12}= \mathfrak{m_1} \frac{\partial{B_2}}{\partial{r}} = \mathfrak{m_1}\frac{\partial{(2\mathfrak{m_2}/r^3)}}{\partial{r}} = -\frac{6 \mathfrak{m_1}\mathfrak{m_2}}{r^4} \;(\text{ед.\; СГС}) \] + + +Если магнитные моменты направлены перпендикулярно соединяющей их прямой: $\mathfrak{m_{1,2}} \perp \textbf{r}$, то нетрудно показать, что сила их взаимодействия окажется в два раза меньшей и будет иметь противоположный знак: $$F_{12} = \frac{3\mathfrak{m_1} \mathfrak{m_2}}{r^4}\;(\text{ед. СГС}) $$. + + + + +\section{Оборудование и инструментальные погрешности} + +\noindent \textbf{В работе используются:} неодимовые магниты; тонкая нить для изготовления крутильного маятника; медная проволока; электронные весы; секундомер; измеритель магнитной индукции; штангенциркуль; брусок, линейка и штатив из немагнитных материалов; набор гирь и разновесов.\\ +\\ +1.Весы -- 0.005 г \\ +2.Секундомер -- 0.2 с \\ +3.Штангенциркуль -- 0.01 см \\ +4.Измеритель магнитной индукции -- 5\% ед. СГС \\ + + + \subsection{Экспериментальная установка} + +В работе используются неодимовые магниты шарообразной формы. Важно, чтобы вещество из которого они изготовлены, было \textit{магнитожёстким} материалом и чтобы шары были намагничены однородно. + +Магнитное поле однородного намагниченного шара радиусом $R$ может быть вычислено точно. На расстояниях $r \geq R$ от центра шара оно совпадает с полем \textit{точечного} магнитного диполя, расположенного в центре, магнитный момент $\mathfrak{m}$ которого совпадает с полным моментом шара. Внутри шара магнитное поле однородно. Hетрудно получить, что при $r < R$ \[\textbf{B}_{0} = \frac{2\mathfrak{m}}{R^3}\] + +В качестве ещё одной характеристики материала магнита используют остаточную \textit{намагниченность} $\textbf{M}$. По определению, намагниченность равна \textit{объёмной плотности магнитного момента, поэтому для однородного намагниченного шара} $\mathfrak{m}= \textbf{M}V $, где $V = \frac{4\pi}{3}R^3$ - объём магнита. Величину $B_r = 4\pi \textbf{M}$ называют остаточной индукцией материала. + +Из сказанного выше нетрудно видеть, что индукция $\textbf{B}_p$ \textit{на полсюсах} однородно намагниченного шара направлена по нормали к поверхности и совпадает поэтому с индукцией внутри шара $\textbf{B}_p = \textbf{B}_0$. Величина $B_p$ связана с остаточной индукцией $B_r$ соотношением \[B_p = B_o = \frac{2}{3}B_r \] + +\section{Результаты измерений и обработка данных} +\subsection{Определение магнитного момента, намагниченности и остаточ- ной магнитной индукции вещества магнитных шариков} + +Диаметр шариков измеряется с помощью микрометра: $d = 0,630 \pm 0,001 $ см. \\ +Масса шариков измеряется на весах, но для того, чтобы магнитное поле шариков не влияло на показания весов, сделаем толстую подложку из легкого материала -- бумаги. $m = 0,82 \pm 0,01 $ г. \\ +Магнитометр показал значение $B_{p} = 260 \pm 5 $ мТл на полюсах шарика. +\begin{center} +\begin{minipage}{0.4\textwidth} + \includegraphics[width=\linewidth]{1.jpg}\\ +Проложим между двумя магнитными шариками брусок из немагнитного материала как на рисунке сверху и, подкладывая между бруском и верхним магнитиком листы бумаги, определим, на каком максимальном расстоянии $r_{max}$ шарики удерживают друг друга в поле тяжести Земли. \[r_{max} = 2.3 \pm 0.4 \; \text{см}\] + +Величина магнитного момента магнитика $\mathfrak{m}$: + +\[ \mathfrak{m} = \sqrt{\frac{mgr^4_{max}}{6}} \] +\[ \mathfrak{m} = 70 \pm 3 \; \text{(ед. СГС)} \] + +\end{minipage} +\begin{minipage}{0.05\textwidth} +\ +\end{minipage} +\begin{minipage}{0.4\textwidth} +\begin{center} +\includegraphics[width=0.7\linewidth]{2.jpg}\\ +\end{center} +Составим цепочку из $25$ шариков, с помощью неодимовых магнитов в форме параллелепипедов, подсоединим цепочку к гире и разновесам так, чтобы общая масса системы составила приблизительно $500 \;\text{г}$. Далее подберём минимальный вес системы цепочки с гирей, при котором она отрывается от верхнего шарика. + +Взвесим оторвавшуюся цепочку с гирей. $$m_{min} = 350 \pm 1 \; \text{г}$$ + +Рассчитаем силу сцепления двух шаров и по ней определим магнитный момент шарика $\mathfrak{m}$. + +\[ F_0 = \frac{6\mathfrak{m}^2}{d^4} \] \[F = m_{min}g = F_0(1+\frac1{2^4}+\frac1{3^4} +...)\approx 1.08 F_0 \] + +\[ \mathfrak{m} = \sqrt{\frac{d^4m_{min}g}{6 \cdot 1.08}} \;\] + +\[\mathfrak{m} = 98 \pm 6 \; \text{(ед. СГС)}.\] +\end{minipage} +\end{center} + + + +Полученные значения магнитных моментов отличаются. Это может быть связано с большой погрешностью методики эксперимента, а так же неточным взаимным расположением магнитных моментов из-за силы трения. + +Величину намагниченности материала шариков рассчитаем для метода A (левая колонка) по формуле $M = \frac{\mathfrak{m}}{\frac{\pi}{6} d^3}$, остаточную индукцию магнитного поля $B_r = 4\pi M$. $$M_A = 480 \pm 18 \; \text{(ед. СГС)}, \;M_B = 750\pm45\; \text{ед. СГС},\; B_r = 330 \pm 30 \; \text{мТл}, $$ + +Табличное значение $B_r$ для соединения $Nd_2Fe_{14}B$: +$B_{r_{\text{табл}}} = 1220 \; \text{мТл},$. К табличной величине ближе результат второго эксперимента, +хотя оба не попадают. +Расчетное значение по методу A: $B_{p_1} = 220 \pm 20$ мТл, по методу Б: $B_{p_2} = 360\pm40$ мТл. +Порядок расчетных значений совпадает с порядком измеренного $B_p$. + +\subsection{Горизонтальная составляющая магнитного поля Земли} + +Оценим влияние упругости нити на период колебаний, возбудив крутильные колебания свёрнутой в кольцо "стрелки" (магнитный момент такого кольцеобразного маятника равен 0). + +\begin{minipage}{0.3\textwidth} +Соберём крутильный маятник в виде кольца из 12 магнитных шариков и подвесим его на немагнитном штативе. Используя $\Lambda$- образный подвес, установим "магнитную стрелку" в горизонтальное положение, далее свернем её в кольцо и измерим коэффициент упругости нити + +\end{minipage} +\begin{minipage}{0.05\textwidth} +\ +\end{minipage} +\begin{minipage}{0.3\textwidth} +\begin{center} +\includegraphics[width=\linewidth]{5.jpg}\\ +\end{center} + +\end{minipage} +Из эксперимента получаем, что для кольца $T = 4.8$ c. Запишем уравнение вращательного движения и формулу для периода колебаний: + + $$I \ddot \alpha + f\alpha = 0, \; T = 2\pi \sqrt{\frac{I}{f}}, f = \Big(\frac{2\pi}{T}\Big)^2 I.$$ + Момент инерции относительно нити колечка можно оценить как $I = \frac{12mR^2}{2} = (2,5 \pm 0,1) \cdot 10^{-6} \; {\text{кг} \cdot \text{м}^2} \Rightarrow f = (4,3 \pm 0,2) \cdot 10^{-6} \; \frac{\text{кг} \cdot \text{м}^2}{\text{c}^2}$. + + \newpage + + +\begin{minipage}{0.45\textwidth} +Соберём крутильный маятник из 12 магнитных шариков и подвесим его на немагнитном штативе. Используя $\Lambda$- образный подвес, установим "магнитную стрелку" в горизонтальное положение. +Возбудим крутильные колебания маятника вокруг вертикальной оси и определим их период. Исследуем зависимость периода $T$ крутильных колебаний "стрелки" от количества магнитных шариков n, составляющих "стрелку". При этом число колебаний +всегда будем брать $N = 10$. Погрешность измерения периода соответственно +$\Delta T = 0,04$ с (время человеческой реакции $\sim 0,4$ c). +\end{minipage} +\begin{minipage}{0.1\textwidth} +\ +\end{minipage} +\begin{minipage}{0.45\textwidth} +\begin{center} +\includegraphics[width=0.7\linewidth]{3.jpg}\\ +\end{center} +\end{minipage} + +\begin{table}[h] +\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|} +\hline +$n$ & 12 & 11 & 10 & 9 & 8 & 7 & 6 & 5 & 4 \\ \hline +$t$, с & 37.8 & 35.3 & 33.7 & 30 & 27.3 & 24.3 & 20.6 & 17.5 & 14.1 \\ \hline +$T$, c & 3.78 & 3.53 & 3.37 & 3 & 2.73 & 2.43 & 2.06 & 1.75 & 1.41 \\ \hline +\end{tabular} +\end{table} + +График экспериментальной зависимости $T(n)$: +\begin{figure}[h] + \centering + \includegraphics{graph.pdf} + \caption{Зависимость периода колебаний от \\ числа числа магнитов магнитной стрелки.} +\end{figure} + + \[J_n \ddot\theta + (\mathfrak{m}_n B_{\|}+f)\theta = 0, J_n \approx \frac1{12}n^2md^2 \Rightarrow T_n = 2\pi \sqrt{ \frac{md^2n^2}{12(\mathfrak{m}B_{\|}+f)}}\] +По значению углового коэффициента рассчитаем величину горизонтальной составляющей магнитного поля Земли. +$$k = 2\pi \sqrt{ \frac{md^2n^2}{12(\mathfrak{m}B_{\|}+f)}} = 0,30 \pm 0,02 \; \text{c}, B_{\|} = \frac{\pi^2md^2}{3\mathfrak{m}k^2} - \frac{f}{\mathfrak{m}} \approx 0,012 \pm 0,001 \; \text{мТл} $$ + +При это оказалось $f \ll \frac{\pi^2md^2}{3k^2}$, то есть упругость можно было не учитывать.\\ + +\begin{minipage}{0.45\textwidth} + +Изготовим магнитную "стрелку" из 10 шариков и подвесим её за середину с помощью нити на штативе. +Определим механический момент сил, действующий со стороны магнитного поля Земли на \textit{горизонтально} расположенную магнитную "стрелку". Для этого с помощью одного или нескольких кусочков проволоки, уравновесим "стрелку" в горизонтальном положении. +С помощью весов определим массу уравновешивающего груза $m_{гр}$. +Из условия равновесия рассчитаем механический момент сил $M = m_{гр}gx$, действующих на горизонтальную "стрелку" со стороны поля Земли. Измерения сил проведём для чётных значений $n = 4,6,8,10,12$. +\end{minipage} +\begin{minipage}{0.05\textwidth} +\ +\end{minipage} +\begin{minipage}{0.45\textwidth} +\begin{center} +\includegraphics[width=\linewidth]{4.jpg}\\ +\end{center} +\end{minipage} + +\begin{center} +\begin{table}[h] +\begin{tabular}{|c|c|c|c|} +\hline +N & d, шар & m, г & M, ед. СГС \\ \hline +10 & 3 & 0.284 & 526.6 \\ \hline +12 & 4 & 0.284 & 702.1 \\ \hline +8 & 2 & 0.255 & 315.2 \\ \hline +6 & 2 & 0.189 & 233.6 \\ \hline +4 & 1 & 0.237 & 146.5 \\ \hline +\end{tabular} +\end{table} +\end{center} + +\begin{figure}[h] + \centering + \includegraphics{moment.pdf} + \caption{Зависимость момента сил, уравновешивающего \\ стрелку от числа числа магнитов в ней.} +\end{figure} + +Коэффициент наклона $a = 70 \pm 7 \; \text{г}\frac{\text{см}^2}{\text{с}^2}.$ Из линейности видно, что приближение аддитивности магнитных моментов для используемых в работе магнитов применимо. По значению углового коэффициента аппроксимирующей прямой рассчитаем величину вертикальной составляющей $B_{\perp}$ магнитного поля Земли. +$$M_n = n\mathfrak{m}B_{\perp} \Rightarrow B_{\perp} = \frac{a}{\mathfrak{m}} \approx 0.07 \pm 0.01 \; \text{мТл}$$ + + + +\newpage +\section{Выводы и рассчет погрешностей} +\subsection{Погрешности} + +\[ \varepsilon_{\mathfrak{m_1}} = \sqrt{\left(\frac{\Delta m}m\right)^2 + 4\left(\frac{\Delta r_{max}}{r_{max}}\right)^2} \approx 6 \%\] + +\[ \varepsilon_{\mathfrak{m_2}} = \sqrt{\Big(\frac{\Delta d}d\Big)^2 + 4\left(\frac{\Delta r_{min}}{r_{min}}\right)^2}\approx 2 \%\] + +\[\varepsilon_{B_{\|}} = \sqrt{\Big(\frac{\Delta m}m\Big)^2 + \Big(\frac{\Delta d}d\Big)^2 + \Big(\frac{\Delta \mathfrak{m}}{\mathfrak{m}}\Big)^2 + \Big(\frac{\Delta \frac{T_n}{n}}{\frac{T_n}{n}}\Big)^2} \approx 7 \% \] + +\[\varepsilon_{B_{\perp}} = \sqrt{\Big(\frac{\Delta a}a\Big)^2 + \Big(\frac{\Delta \mathfrak{m}}{\mathfrak{m}}\Big)^2} \approx 12 \%\] + + +\subsection{Вывод} +Поскольку установка находится в железобетонном +здании, магнитное поле в нём сильно отличается от поля Земли. Так же на +показания влияет наличие электронных приспособлений связи и магнитных предметов вблизи экспериментальной установки. Магнитное +поле Земли в нашем районе около 0.05 - 0.1 ед. СГС. Полученное значение не сильно отличается действительного. + +Используя результаты измерений $B_{\perp}$ и $B_{\|}$, магнитное наклонение $\beta$ и полная величина индукции магнитного поля Земли в кабинете выполнения лабораторной работы равны: +$$\beta = \arctan{\frac{B_{\perp}}{B_{\|}}} \approx 80^{\circ} $$ +$$\Delta \beta = \frac{1}{1 + (\frac{B_{\perp}}{B_{\|}})^2} \Delta \frac{B_{\perp}}{B_{\|}}$$ + +Теоретически ($\phi$ - широта Москвы), +\[\beta = \arctan\frac{B_{\perp}}{B_{\|}} = \arctan\frac{\frac{-2\mathfrak{m}_3sin\phi}{r^3}}{\frac{-\mathfrak{m}_3cos\phi}{r^3}} = \arctan(2 tg\phi) \approx 71^{\circ}\] + +\end{document} diff --git a/3.1.3/Baldin_V/moment.pdf b/3.1.3/Baldin_V/moment.pdf new file mode 100644 index 00000000..26e115f7 Binary files /dev/null and b/3.1.3/Baldin_V/moment.pdf differ diff --git a/3.1.3/pdf/Baldin_V.pdf b/3.1.3/pdf/Baldin_V.pdf new file mode 100644 index 00000000..73bf0a37 Binary files /dev/null and b/3.1.3/pdf/Baldin_V.pdf differ diff --git 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+\usepackage{graphics} +\usepackage{graphicx} +\usepackage{wrapfig} +\usepackage{geometry} +\usepackage{float} +\usepackage{hyperref} +\geometry{ + a4paper, + total={170mm, 257mm}, + left=20mm, + top=10mm} + +\title{Лабораторная работа 3.5.1. Изучение плазмы газового разряда в неоне.} +\author{Балдин Виктор} +\date{\today} +\begin{document} + \maketitle + \section*{Теория} + \subsection*{Плазма} + + Из-за теплового движения в плазме электроны могут смещаться относительно ионов и образовывать неоднородности. В этих неоднородностях возникает электрическое поле, которое стремится восстановить баланс, из-за чего происходят колебания с частотой + \[w_p = \sqrt{\frac{4\pi n_e e^2}{m_e}}\] + За характерное время колебаний электроны за счет теплового движения смещаются на + \[r_D \sim \frac{v_e}{w_p} = \sqrt{\frac{kT_e}{4\pi n_e e^2}}\] + $r_D$ - дебаевский радиус, $k$ - константа Больцмана.\\ + Если поместить в плазму пробную (допустим, положительную) частицу, то электроны будут скапливаться около этой частицы, экранируя её поле. Потенциал точечного заряда будет иметь в плазме следующий вид: + \[\varphi(r) = \frac{q}{r}e^{-\frac{r}{r_D}}\] + где $r_D = \sqrt{\dfrac{kT_e}{4\pi n e^2}}$ -- \textit{радиус Дебая в случае равновесной плазмы}. Если температуры электронов и ионов сильно отличаются, то следует определять отдельно величину радиуса экранирования для электронов и для ионов. Итоговый радиус будет + \[r_D = (r_{De}^{-2} + r_{Di}^{-2})^{-1/2}\] + То есть если $T_i \ll T_e$, то $r_D \approx r_{Di}$ + \subsection*{Одиночный зонд} + При внесении в плазму уединённого проводника -- \textit{зонда} -- с потенциалом, изначально равным потенциалу точки плазмы, в которую его помещают, на него поступают токи электроннов и ионов: + \begin{equation} + \begin{array}{c} + I_{e0} = \dfrac{n \langle v_e \rangle}{4}eS,\\ + I_{i0} = \dfrac{n \langle v_i \rangle}{4}eS, + \end{array} + \end{equation} + где $\langle v_e \rangle$ и $\langle v_i \rangle$ -- средние скорости электронов и ионов, $S$ -- площадь зонда, $n$ -- плотность электронов и ионов. Скорости электронов много больше скорости ионов, поэтому $I_{i0} \ll I_{e0}$. Зонд будет заряжаться до некоторого равновестного напряжения $-U_f$ -- \textit{плавающего потенциала}.\\ + В равновесии ионный ток мало меняется, а электронный имеет вид + $$ + I_e = I_0 \exp\left( -\dfrac{eU_f}{kT_e} \right). + $$ + Будем подавать потенциал $U_\text{з}$ на зонд и снимать значение зондового тока $I_\text{з}$. Максимальное значение тока $I_{e\text{н}}$ -- электронный ток насыщения, а минимальное $I_{i\text{н}}$ -- ионный ток насыщения. Значение из эмпирической формулы Бомона: + \begin{equation} + I_{i\text{н}} = 0.4 neS \sqrt{\dfrac{2kT_e}{m_i}}. + \end{equation} + + Электронный ток насыщения можно определить по тепловому движению: + \[I_{e\text{н}} = \frac{n_eS}{4}\sqrt{\frac{8kT}{\pi m_e}}\] + \subsection*{Двойной зонд} + Двойной зонд -- система из двух одинаковых зондов, расположенных на небольшом расстоянии друг от друга, между которыми создаётся разность потенциалов, меньшая $U_f$. Рассчитаем ток между ними вблизи $I=0$. При небольших разностях потенциалов ионные токи на оба зонда близки к току насыщения и компенсируют друг друга, а значит величина результирующего тока полностью связана с разностью электронных токов. Пусть потенциалы на зондах + $$ + U_1 = -U_f + \Delta U_1, + $$ + $$ + U_2 = -U_f + \Delta U_2. + $$ + Между зондами $U = U_2 - U_1 = \Delta U_2 - \Delta U_1$. + Через первый электрод + \begin{equation} + I_1 = I_{i\text{н}} + I_{e1} = I_{i\text{н}} - \dfrac{1}{4}neS\langle v_e\rangle \exp\left(-\dfrac{eU_f}{kT_e}\right)\exp\left(\dfrac{e\Delta U_1}{kT_e}\right)=I_{i\text{н}}\left(1 - \exp\left( \dfrac{e\Delta U_1}{kT_e} \right)\right). + \end{equation} + Аналогично через второй получим + \begin{equation} + I_2 = I_{i\text{н}}\left(1 - \exp\left( \dfrac{e\Delta U_2}{kT_e} \right)\right) + \end{equation} + + Из $(7)$ и $(8)$ с учётом последовательного соединение зондов ($I_1 = -I_2 = I)$: + $$ + \Delta U_1= \dfrac{kT_e}{e}\text{ln}\left(1 - \dfrac{I}{I_{i\text{н}}}\right) + $$ + $$ + \Delta U_2= \dfrac{kT_e}{e}\text{ln}\left(1 + \dfrac{I}{I_{i\text{н}}}\right) + $$ + + Тогда итоговые формулы для разности потенциалов и тока + + \begin{equation} + U = \dfrac{kT_e}{e}\text{ln}\dfrac{1 - I/I_{i\text{н}}}{1 + I/I_{i\text{н}}}, \ \ + I = I_{i\text{н}} \text{th}\dfrac{eU}{2kT_e} + AU. + \label{tok} + \end{equation} + + \begin{figure}[h!] + \includegraphics[scale=0.8]{4.png} + \end{figure} + Зависимость выглядит примерно так. + С учетом \ref{tok} можно выразить асимптоты графика: + \begin{equation} + I = I_{i\text{нас}} + AU,\ I = -I_{i\text{нас}} + AU + \end{equation} + Наклон в нуле принимает вид: + \begin{equation} + \frac{dI}{dU} = I_{i\text{нас}} \frac{e}{2kT_e} + A + \end{equation} + + \section*{Описание установки} + \begin{center} + \includegraphics[scale=0.9]{1.png} + \end{center} + Стеклянная газоразрядная трубка имеет холодный (ненакаливаемый) полый катод, три анода и \textit{геттерный} узел -- стеклянный баллон, на внутреннюю повехность которого напылена газопоглощающая плёнка (\textit{геттер}). Трубка наполнена изотопом неона $^22$Ne при давлении 2 мм рт. ст. Катод и один из анодом (I и II) с помощью переключателя $\Pi_1$ подключается через балластный резистор $R_\text{б}$ ($\approx 450$ кОм) к регулируемому ВИП с выкодным напряжением до 5 кВ.\\ + При подключении к ВИП анода-I между ним и катодом возникает газовый разряд. Ток разряда измеряется миллиамперметром $A_1$, а падение напряжения на разрядной трубке -- цифровым вольтметром $V_1$, подключённым к трубке черезе высокоомный (25 МОм) делитель напряжения с коэффициентом $(R_1+R_2)/R_2 = 10$.\\ + При подключении к ВИП анода-II разряд возникает в пространстве между катодом и анодом-II, где находятся двойной зонд, используемый для диагностики плазмы положительного столба. Зонды изготовлены из молибденовой проволоки диаметром $d = 0.2$ мм и имеют длину $l = 5.2$ мм. Они подключены к источнику питания GPS через потенциометр $R$. Переключатель $\Pi_2$ позволяет изменять полярность напряжения на зондах. Величина напряжения на зондах изменяеься с помощью дискретного переключателя <<$V$>> выходного напряжения источника питания и потенциометра $R$, а измеряется цифровым вольтметром $V_2$. Для измерения зондового тока используется мультиметр $A_2$. + + \section*{Ход работы} + Измеряем напряжение зажигания в лампе: $U_{\text{заж}} = 99\pm5 $ В.\\ + Снимаем ВАХ газового разряда. Результаты представлены в таблице. + % \begin{table}[h!] + % \centering + % \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|} + % \hline + % $I$, мА & 0,506 & 0,991 & 1,505 & 2,014 & 2,506 & 3,004 & 3,507 & 3,999 & 4,518 \\ \hline + % $U$, В & 35,07 & 33,50 & 30,86 & 29,47 & 28,48 & 27,54 & 26,90 & 26,98 & 27,25 \\ \hline + % \end{tabular} + % \end{table} + + Построим ВАХ и определим максимальное дифференциальное сопротивление разряда $R_{\text{диф}} = \frac{dU}{dI}$. Оно будет соответствовать участку с минимальным (по модулю) наклоном графика $I(U)$: + + \begin{figure}[h!] + \centering + \includegraphics[width = \textwidth]{vah.pdf} + \caption{ВАХ газового разряда в неоне} + \end{figure} + В схеме напряжение снимается с делителя напряжений с коэффициентом 10, поэтому $R_{\text{диф}} = -1.7 \pm 0.6$ кОм. Наш график соответствует участку поднормального тлеющего разряда (см. приложение к лабораторной работе). + + % \newpage + С помощью вольтмертра $V_2$ и амперметра $A_2$ снимем ВАХ двойного зонда $I_2 = f(U_2)$ при фиксированного токе разряда $I_p$ в трубке в диапозоне $-25 \div 25$ В, процессе измерений меняя полярность зонда при нулевом токе. Измерения проведём для $I_p = 4.0$ мА, $I_p = 3.0$ мА и $I_p = 2.3$ мА. + + \begin{figure}[h!] + \centering + \includegraphics[width = \textwidth]{zond.pdf} + \caption{Зондовые характеристики} + \end{figure} + + Видно, что чем меньше ток, тем менее крутая кривая получается. Проанализируем графики по отдельности, чтобы найти их наклон в начале и пересечение асимптот с осью ординат. Данные будем заносить в таблицу. Ионный ток насыщения определим через асимптоты, затем по наклону кривой в точке $U = 0$ найдем концентрацию электронов в плазме. + + \begin{table}[h!] + \begin{tabular}{|c|c|c|c|c|c|c|c|} + \hline + $I_p$, мА & $T_e$, $10^3$ K & $n_e$, $10^{-14}$ м$^{-3}$ & $\omega_p$, $10^8$ рад/с & $r_{D_e}$, $10^{-4}$ м & $r_D$, $10^{-4}$ м & $N_D$ & $\alpha$, $10^{-7}$ \\ \hline + 4.0 & $ 3.1 \pm 0.8$ & $1.4 \pm 0.3$ & $6.7 \pm 0.8$ & $10 \pm 2$ & $1.0 \pm 0.1$ & $605 \pm 68$ & $5.2 \pm 1.2$ \\ \hline + 3.0 & $ 3.1 \pm 0.8$ & $1.0 \pm 0.2$ & $5.7 \pm 0.7$ & $12 \pm 3$ & $1.2 \pm 0.1$ & $707 \pm 81$ & $3.8 \pm 0.9$ \\ \hline + 2.3 & $ 3.3 \pm 0.8$ & $0.8 \pm 0.2$ & $4.9 \pm 0.6$ & $14 \pm 3$ & $1.4 \pm 0.2$ & $819 \pm 94$ & $2.8 \pm 0.7$ \\ \hline + \end{tabular} + \end{table} + + + По данным таблицы видно что $N_D \gg 1 \Rightarrow$ плазму можно с хорошей точностью считать идеальной. + + % \begin{table}[h!] + % \centering + % \begin{tabular}{|c|c|c|c|c|c|c|} + % \hline + % $I_p$, мА & $T_e$, $10^3$ К & $kT_e$, эВ & $n_e$, $10^{16}$ м$^{-3}$ & $\omega_p$, $10^9 \frac{\text{рад}}{c}$ & $r_{De}$, $10^{-3}$ cм & $r_D, 10^{-4}$ см \\ \hline + % 5.02 & $65.5\pm 4.5$ & $5.6 \pm 0.4$ & $5.17 \pm 0.37$ & $12.8\pm 0.5$ & $7.76\pm 0.28$ & $5.25 \pm 0.19$ \\ \hline + % 3.02 & $66.0\pm 3.3$ & $5.7 \pm 0.3$ &$3.95\pm 0.21$ & $11.2\pm 0.3$ & $8.92\pm 0.24$ & $6.01 \pm 0.16$ \\ \hline + % 1.52 & $50.6 \pm 1.2$ &$4.4 \pm 0.1$ &$2.08\pm 0.03$ & $8.1\pm 0.1$ & $10.75 \pm 0.07$ & $8.28 \pm 0.06$ \\ \hline + % \end{tabular} + % \caption{Результаты вычислений} + % \end{table} + + % \begin{figure} + % \centering + % \includegraphics[width = 0.87\textwidth, height = 0.45\textheight]{5mA} + % \caption{ВАХ зонда при $I_p = 5,02$ мА} + % \end{figure} + + % \begin{figure} + % \centering + % \includegraphics[width = 0.87\textwidth, height = 0.45\textheight]{3mA} + % \caption{ВАХ зонда при $I_p = 3,02$ мА} + % \end{figure} + + % \begin{figure}[h!] + % \centering + % \includegraphics[width = \textwidth]{1,5mA} + % \caption{ВАХ зонда при $I_p = 1,52$ мА} + % \end{figure} + % \newpage + % $r_D$ рассчитываем в предположении, что $T_i \ll T_e, \ T_i \approx 300 K$. + + % Степень ионизации $\alpha$ рассчитаем из условия, что $P \approx 2$ торр. $\alpha = \dfrac{n_i}{n}$, где $P = nkT_i$ + % \begin{table}[h!] + % \centering + % \begin{tabular}{|c|c|c|} + % \hline + % $I_p$, мА & $N_D$ & $\alpha, 10^{-7}$ \\ \hline + % 5.02 & $31.4 \pm 4.1 $ & $8.03 \pm 0.58$ \\ \hline + % 3.02 & $35.9 \pm 3.4$ & $6.13 \pm 0.33$ \\ \hline + % 1.52 & $49.4 \pm 1.2$ & $3.23 \pm 0.04$ \\ \hline + % \end{tabular} + % \caption{Результаты вычислений} + % \end{table} + + % Относительные погрешности вычисленных величин показаны в процентах в таблице : + % \begin{table}[h!] + % \centering + % \begin{tabular}{|c|c|c|c|c|c|c|c|c|} + % \hline + % $I_p$,мА & $T_e$ & $kT_e$ & $n_e$ & $\omega_p$ & $r_{De}$ & $r_D$ & $N_D$ & $\alpha$ \\ \hline + % 5.02 & 6.9 & 6.9 & 7.2 & 3.6 & 3.6 & 3.6 & 12.9 & 7.2 \\ \hline + % 3.02 & 5.0 & 5.0 & 5.3 & 2.7 & 2.7 & 2.7 & 9.6 & 5.3 \\ \hline + % 1.52 & 2.4 & 2.4 & 1.4 & 0.7 & 0.7 & 0.7 &2.5 & 1.4 \\ \hline + % \end{tabular} + % \caption{Относительные погрешности величин в процентах} + % \end{table} + + \newpage + Построим графики $T_e(I_p)$ и $n(I_p)$: + + \begin{figure}[h!] + \centering + \includegraphics[width = \textwidth]{t.pdf} + \caption{Зависимость $T_e(I_p)$} + \end{figure} + \begin{figure}[h!] + \centering + \includegraphics[width = \textwidth]{n.pdf} + \caption{Зависимость $n(I_p)$} + \end{figure} + + Очевидно, что из-за больших погрешностей эксперимента судить о характере $T_e(I_p)$ невозможно, + но зависимость $n(I_p)$ возрастает при повышении тока, потому что больше молекул газа ионизируется, так как выше электрическое поле, выше скорость электронов и больше столкновений. + + % \newpage + % В обоих случая в пределах погрешности зависимости можно назвать возрастающими. Концентрация электронов возрастает Температура электронов возрастает, но зависимость не близка к линейной. + + \newpage + \section*{Вывод} + В данной лабораторной работе мы исследовали состояние плазмы в тлеющем газовом разряде c помощью двойного зонда. Полученные результаты сходятся с указанными в лабораторной работе по порядку. Плазму в тлеющем разряде можно с хорошей точностью назвать идеальной, так как $N_D \gg 1$. + +\end{document} \ No newline at end of file diff --git a/3.5.1/Baldin_V/4.png b/3.5.1/Baldin_V/4.png new file mode 100644 index 00000000..cbbe42b7 Binary files /dev/null and b/3.5.1/Baldin_V/4.png differ diff --git a/3.5.1/Baldin_V/calc.ipynb b/3.5.1/Baldin_V/calc.ipynb new file mode 100644 index 00000000..5fdb78b9 --- /dev/null +++ b/3.5.1/Baldin_V/calc.ipynb @@ -0,0 +1,606 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib\n", + "from matplotlib import pyplot as plt\n", + "import pandas as pd\n", + "from scipy.optimize import minimize\n", + "from uncertainties import ufloat\n", + "\n", + "data = pd.read_csv( 'data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [], + "source": [ + "def create_spline( x, y, xmin, xmax, xerr, yerr, fmt, color, text=''):\n", + " xax = np.linspace( xmin, xmax, 1000)\n", + " p, cov = np.polyfit( x, y, deg=3, cov=True)\n", + "\n", + " cov = np.sqrt( np.diag( cov))\n", + "\n", + " plt.errorbar( x, y, xerr=xerr, yerr=yerr, fmt=fmt)\n", + " plt.plot( xax,\n", + " p[0] * xax**3 + p[1] * xax**2 + p[2] * xax + p[3],\n", + " color=color, label=text)\n", + "\n", + " return [p, cov]" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Ip = data['Ip, мА']\n", + "Up = data['Up, В']\n", + "\n", + "EPS = 0.01\n", + "\n", + "Ierr = Ip * EPS\n", + "Uerr = Up * EPS\n", + "\n", + "PEAK_IDX = 14\n", + "\n", + "plt.figure( figsize=(10, 5))\n", + "\n", + "create_spline( Up[:PEAK_IDX], Ip[:PEAK_IDX],\n", + " xmin=7.5, xmax=26,\n", + " xerr=Uerr[:PEAK_IDX], yerr=Ierr[:PEAK_IDX],\n", + " fmt='b.', color='blue', text='$I_p\\\\uparrow$')\n", + "\n", + "create_spline( Up[PEAK_IDX:], Ip[PEAK_IDX:],\n", + " xmin=7.5, xmax=26,\n", + " xerr=Uerr[PEAK_IDX:], yerr=Ierr[PEAK_IDX:],\n", + " fmt='r.', color='red', text='$I_p\\\\downarrow$')\n", + "\n", + "# касательная и дифференциальное напряжение\n", + "slope = (ufloat( Ip[PEAK_IDX - 2], Ierr[PEAK_IDX - 2])\n", + "- ufloat( Ip[PEAK_IDX - 1], Ierr[PEAK_IDX - 1])) / (ufloat( Up[PEAK_IDX - 2], Uerr[PEAK_IDX - 2]) - ufloat( Up[PEAK_IDX - 1], Uerr[PEAK_IDX - 1]))\n", + "inter = Ip[PEAK_IDX - 1] - slope.nominal_value * Up[PEAK_IDX - 1]\n", + "\n", + "xax = np.linspace( 7.5, 26, 1000)\n", + "plt.plot( xax, slope.nominal_value * xax + inter,\n", + " color='magenta', label='$R_{\\\\text{диф}}$')\n", + "\n", + "R_dif = 1 / slope\n", + "\n", + "plt.xlabel( '$U_p$, В')\n", + "plt.ylabel( '$I_p$, мА')\n", + "plt.grid( which='major', linestyle='-')\n", + "plt.grid( which='minor', linestyle='--')\n", + "plt.minorticks_on()\n", + "plt.legend()\n", + "\n", + "plt.savefig( 'vah.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-17.368421052631618+/-6.027263674163185" + ] + }, + "execution_count": 187, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "R_dif" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [], + "source": [ + "# I(U) - зондовые характеристики\n", + "U = []\n", + "I = []\n", + "\n", + "for i in range (1, 4):\n", + " U.append( list( data[f'U+{i}, В']) + list( -data[f'U-{i}, В']))\n", + " I.append( list( data[f'I+{i}, мкА']) + list( -data[f'I-{i}, мкА']))" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [], + "source": [ + "def clear_from_nans( arr):\n", + " arr = np.array( arr)\n", + " arr = arr[~np.isnan( arr)]\n", + " return arr" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "def plot_zond(x, y, xerr=0, yerr=0, color='blue', label='', fmt=''):\n", + " plt.errorbar(x, y, xerr=xerr, yerr=yerr, fmt=fmt)\n", + " volt = lambda u, a, b, c: a * np.tanh( b * u) + c * u\n", + "\n", + " xax = np.linspace( min(x), max(x), 1000)\n", + " #print( y.size)\n", + "\n", + " popt, pcov = curve_fit( volt, xdata=x, ydata=y)\n", + " plt.plot( xax, volt( xax, *popt), color=color, label=label)\n", + " return (popt, pcov)" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from uncertainties import unumpy as unp\n", + "\n", + "fig, ax = plt.subplots( figsize=(10, 5))\n", + "# Move the left and bottom spines to x = 0 and y = 0, respectively.\n", + "ax.spines[[\"left\", \"bottom\"]].set_position((\"data\", 0))\n", + "# Hide the top and right spines.\n", + "ax.spines[[\"top\", \"right\"]].set_visible(False)\n", + "\n", + "ax.plot(1, 0, \">k\", transform=ax.get_yaxis_transform(), clip_on=False)\n", + "ax.plot(0, 1, \"^k\", transform=ax.get_xaxis_transform(), clip_on=False)\n", + "\n", + "colors = ['red', 'blue', 'magenta']\n", + "fmts = ['r.' , 'b.' , 'm.']\n", + "labels = ['4.0 мкА', '3.0 мкА', '2.3 мкА']\n", + "\n", + "Inas, slopes = [], []\n", + "Te = []\n", + "ne = []\n", + "\n", + "ELECT = 1.6e-19\n", + "ME = 0.91e-30\n", + "KB = 1.38e-23\n", + "\n", + "for i in range( len(U)):\n", + " U[i] = clear_from_nans( U[i])\n", + " I[i] = clear_from_nans( I[i])\n", + " Uerr = [abs(j * EPS) for j in U[i]]\n", + " Ierr = [abs(j * EPS) for j in I[i]]\n", + " #print( I[i])\n", + " (popt, pcov) = plot_zond( U[i], I[i], xerr=Uerr, yerr=Ierr, color=colors[i], label=labels[i], fmt=fmts[i])\n", + "\n", + " pcov = np.sqrt( np.diag( pcov))\n", + " # текущий ток насыщения\n", + " Inas.append( ufloat( popt[0], pcov[0]))\n", + "\n", + " # y'(0) = ab + c\n", + " slopes.append( ufloat( popt[0], pcov[0]) * ufloat( popt[1], pcov[1])\n", + " + ufloat( popt[2], pcov[2]))\n", + "\n", + " Te.append( ELECT / (2 * KB * ufloat( popt[1], pcov[1])))\n", + "\n", + " ne.append( Inas[i] / ( 0.4 * ELECT * np.pi * 0.2 * 5.2 * unp.sqrt( 2 * KB * Te[i] / ME)))\n", + "\n", + " xax = np.linspace( 0, max(U[i]), 1000)\n", + " plt.plot( xax, popt[0] + popt[2] * xax, color=colors[i] , linestyle='--')\n", + " xax = np.linspace( min( U[i]), 0)\n", + " plt.plot( xax, -popt[0] + popt[2] * xax, color=colors[i], linestyle='--')\n", + "\n", + "ax.grid( which='major', linestyle='-')\n", + "ax.grid( which='minor', linestyle='--')\n", + "ax.minorticks_on()\n", + "ax.set_xlabel( '$U$, В' , loc='right')\n", + "ax.set_ylabel( '$I$, мкА', loc='top')\n", + "ax.legend()\n", + "plt.savefig( 'zond.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[28.142967713402186+/-5.241768786382042,\n", + " 20.968468010842667+/-3.995130308145933,\n", + " 15.895329152820741+/-3.0553708774586017]" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Inas" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[5.637515637964564+/-1.7188827770538249,\n", + " 4.059576938804412+/-1.2422578039815322,\n", + " 2.9993680273601897+/-0.9028514763570199]" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slopes" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[30503.12272992003+/-7822.575998593149,\n", + " 31488.74898534661+/-7974.512762627078,\n", + " 32626.131451087393+/-8118.737967656381]" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Te" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.3992673820585981+/-0.31641018878311,\n", + " 1.0261053856779772+/-0.2347420255702843,\n", + " 0.7641695069827572+/-0.17497371345115628]" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for i in range( len( ne)):\n", + " ne[i] /= 1e14\n", + "ne" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[6.672304050916743+/-0.754389408142605,\n", + " 5.713752926886428+/-0.6535673403464564,\n", + " 4.9308333550951176+/-0.5645123331054899]" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ELECT = 4.8e-10\n", + "ME *= 1e3\n", + "\n", + "wp = [( (4 * np.pi * n * 1e8 * ELECT**2) / ME)**0.5 for n in ne]\n", + "for i in range( len( wp)):\n", + " wp[i] /= 1e8\n", + "\n", + "wp" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.0108894076175412+/-0.11429399141447207,\n", + " 1.180478325854186+/-0.13502895376079557,\n", + " 1.3679151177367734+/-0.15660739250210598]" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "KB = 1.38e-16\n", + "\n", + "rd = [( (KB * 300) / (4 * np.pi * n * 1e8 * ELECT**2))**0.5 for n in ne]\n", + "for i in range( len( rd)):\n", + " rd[i] *= 100\n", + "rd" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[10.193308530564838+/-2.1782916886569845,\n", + " 12.094142843402606+/-2.5698728571438143,\n", + " 14.265313250030074+/-2.9946339051548794]" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rde = [( (KB * Te[i]) / (4 * np.pi * ne[i] * 1e8 * ELECT**2))**0.5\n", + " for i in range(len( ne))]\n", + "for i in range( len( rd)):\n", + " rde[i] *= 100\n", + "rde" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[605.480634770923+/-68.45733860762647,\n", + " 707.0573305897467+/-80.87671709630986,\n", + " 819.324159061088+/-93.80130280074056]" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N_d = [4 / 3 * np.pi * (rd[i]/100)**3 * ne[i] * 1e8 for i in range( len(rd))]\n", + "N_d" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[5.20173747977174+/-1.1762460549557991,\n", + " 3.8145181623716624+/-0.8726469352055178,\n", + " 2.840778836367127+/-0.6504599013054138]" + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "NL = 2.69e20\n", + "alpha = [n * 1e14 / NL for n in ne]\n", + "for i in range( len( alpha)):\n", + " alpha[i] *= 1e7\n", + "\n", + "alpha" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [], + "source": [ + "res = pd.DataFrame()\n", + "\n", + "I_p = [4, 3, 2.3]\n", + "\n", + "res['$I_p$, мА'] = I_p\n", + "res['$T_e$, $10^3$ K'] = [f'${t.nominal_value / 1e4: .1f} \\\\pm {t.std_dev / 1e4: .1f}$'\n", + " for t in Te]\n", + "res['$n_e$, $10^{-14}$ м$^{-3}$'] = [f'${n.nominal_value:.1f} \\\\pm {n.std_dev:.1f}$'\n", + " for n in ne]\n", + "res['$\\\\omega_p$, $10^8$ рад/с'] = [f'${w.nominal_value:.1f} \\\\pm {w.std_dev:.1f}$'\n", + " for w in wp]\n", + "res[r'$r_{D_e}$, $10^{-4}$ м'] = [f'${r.nominal_value:.0f} \\\\pm {r.std_dev:.0f}$'\n", + " for r in rde]\n", + "res[r'$r_D$, $10^{-4}$ м'] = [f'${r.nominal_value:.1f} \\\\pm {r.std_dev:.1f}$'\n", + " for r in rd]\n", + "res[r'$N_D$'] = [f'${n.nominal_value:.0f} \\\\pm {n.std_dev:.0f}$'\n", + " for n in N_d]\n", + "res['$\\\\alpha$, $10^{-7}$'] = [f'${a.nominal_value:.1f} \\\\pm {a.std_dev:.1f}$'\n", + " for a in alpha]" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [], + "source": [ + "def latex_tab(df):\n", + " tab = df.to_latex(index=False, float_format='%.1f')\n", + " tab = tab.replace('\\\\\\\\\\n', '\\\\\\\\ \\\\hline\\n')\n", + " tab = tab.replace('\\\\toprule', '\\\\hline')\n", + " tab = tab.replace('\\\\midrule\\n', '')\n", + " tab = tab.replace('\\\\bottomrule\\n', '')\n", + " return tab" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{tabular}{rlllllll}\n", + "\\hline\n", + "$I_p$, мА & $T_e$, $10^3$ K & $n_e$, $10^{-14}$ м$^{-3}$ & $\\omega_p$, $10^8$ рад/с & $r_{D_e}$, $10^{-4}$ м & $r_D$, $10^{-4}$ м & $N_D$ & $\\alpha$, $10^{-7}$ \\\\ \\hline\n", + "4.0 & $ 3.1 \\pm 0.8$ & $1.4 \\pm 0.3$ & $6.7 \\pm 0.8$ & $10 \\pm 2$ & $1.0 \\pm 0.1$ & $605 \\pm 68$ & $5.2 \\pm 1.2$ \\\\ \\hline\n", + "3.0 & $ 3.1 \\pm 0.8$ & $1.0 \\pm 0.2$ & $5.7 \\pm 0.7$ & $12 \\pm 3$ & $1.2 \\pm 0.1$ & $707 \\pm 81$ & $3.8 \\pm 0.9$ \\\\ \\hline\n", + "2.3 & $ 3.3 \\pm 0.8$ & $0.8 \\pm 0.2$ & $4.9 \\pm 0.6$ & $14 \\pm 3$ & $1.4 \\pm 0.2$ & $819 \\pm 94$ & $2.8 \\pm 0.7$ \\\\ \\hline\n", + "\\end{tabular}\n", + "\n" + ] + } + ], + "source": [ + "print( latex_tab( res))" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure( figsize=(10, 5))\n", + "plt.errorbar( I_p, [n.nominal_value for n in ne], yerr=[n.std_dev for n in ne], fmt='k.')\n", + "\n", + "plt.xlabel( '$I_p$, мА')\n", + "plt.ylabel( r'$n_e$, $10^{14}$ м$^{-3}$')\n", + "plt.grid( which='major', linestyle='-')\n", + "plt.grid( which='minor', linestyle='--')\n", + "plt.minorticks_on()\n", + "plt.savefig( 'n.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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DUChke84lOVlfX6/g6LY5VxTFrOsCO3OumZy06hOB/H29MOeqrwPdOueqa2PO5pyrft0I/OaMIlpaWtDS0iIlRTQaRVFREQDrlcgHBgawevVqS9XV1RXjY7GY9POf/vQnaTCyWl1dr2L88ePHUVtba7m6upamhQsXYmJiAtFo1FATYK1i/F/+8hfs2LHDtCYjn0jxU3n1eiNNZn0CZk5as5py+XTq1ClFX1rNPT1NQ0NDKC8vN60pl09yjlZzT08TgZXc0/MpGAxm5aSV3NPTBMxcHJ06dcp07hn59Je//EXB02ru6WlKpVIYGhrC0aNHc2rK5VNvby+Az86dfMYILU3AZ0VGzWgy61M0Gs1rjNDStGHDBoyPj0s+5dJk1qdoNEptLK+oqAAA9Pb2or+/P6cmKz5Fo1EqY/n8+fMxMTGB/v5+9PT05NRkxadoNEptLG9oaMgaN2j51NvbS2XOraurw/j4ODo6OqiM5U7Oueq+pOFTNBqlMpaXlZWhrq4OgPK6wEtz7qlTp7Bu3TrXz7kAbI/ldufcqampLE564EWoHQApNDc0NCQVmrOyciYIAnbu3Gn4NMLMk5TJyUksXLgQADA4OKjYrdHuUzxBENDU1IRIJEJl5SydTqO1tRUNDQ1SoUA7T/EIx8bGRhQWFlJ5ipfJZLI42l05Izx37dqFUChEZeUsFouhvb1d4mn3KZ46J2k8xYvH42hra5M40lo5EwQBzc3N8Pl8tp/iJZNJHDx40FROWnmKRziSnDLSJG/X4z49PY1oNCrxpLFyJvdcvmqcr096OWnnaav83JE/wTbSmsunWCwGQRDQ0NCAgoICKitnmUwGbW1taGxsVKxS5OvT9PS0xDEUClFbOWtvb0dTU5Nidc+OT8SfhoYGFBYWGmoy41MqlUI0GtXNyXx8SiQSEsdIJEJl5UwUxaxxQ0/TbM25JCfVHOf6nGsmJ636JIpiFkcvzLnqnKSxcjbX59yxsTGUlpaaKkLNV84cBJk45QgEAppfIpRP2sBnH6NTt8tj52pX/6z+G7/fr5jkcrWruZOfzWoyaicnkhZPM1r1uJP3MqvJqJ2cvFoc7fgEzAwqPp/PlKZc7YS3mqddn8h7WfFDr10eS/46DZ+0uOdq1+JILvjN5qTd8ylXey5Nap60fLKi1UiTFkdaPtkZI+Tt5HjykUbC3Y5PZMImH0E0w9FMu7wvaeae1vva8YlcCKrb9Y7PxV0vJ/PxiVx4kQcGpN0sR6124reb51wjjlqajNpZnHNz5aQVn4z60gtzLvmZz7nmxmyz4Ls1egRaiWcnVn19vatjOsHRCXi1L1nwhxXdrMSkDVZ0s3A+OgEWdLMSkzZY0c1CDjkBr/all/1Rg6+ceQRaTxHsxFq8eDG1eE7EdIKjE/BqX7LgDyu6WYlJG6zoZuF8dAIs6GYlJm2wopuFHHICXu1LL/uT9R6ORs8TP/rRj+Dz+fDd735XahNFEQ8//DDKy8tRWFiIW265RfqSOUE8Hsd3vvMdlJaWYt68ebjjjjsUX24GgJGREezduxclJSUoKSnB3r17cenSJcUxp0+fxu2334558+ahtLQU999/PxKJhFNyPxdY2SXGTKwDBw64OqYTHJ2AV/uSBX9Y0c1KTNpgRTcL56MTYEE3KzFpgxXdLOSQE/BqX3rZHzVcd3N29OhR/N//+3+xceNGRfu//Mu/4Oc//zmeeOIJHD16FEuXLkVDQ4NiF5jvfve7+MMf/oD9+/fjtddew8TEBG677TbFFwb37NmD7u5utLW1oa2tDd3d3di7d6/0ejqdxu7duzE5OYnXXnsN+/fvx4svvogHHnjAefEMQWv7YLfFdIKjE/BqX7LgDyu6WYlJG6zoZuF8dAIs6GYlJm2wopuFHHICXu1LL/sjh6tuziYmJnDXXXfhV7/6lbTLIDCzavbLX/4S//iP/4gvf/nLWL9+PZ599llMTU3hhRdeAACMjo7i6aefxs9+9jPceuut2Lx5M5577jn09PTg5ZdfBgD89a9/RVtbG37961+jvr4e9fX1+NWvfoWXXnpJ2l4zGo3ivffew3PPPYfNmzfj1ltvxc9+9jP86le/wtjY2OffKRwcHBwcHBwcHBwcnoCrvnP2rW99C7t378att96Kf/7nf5baP/roI5w7dw6NjY1SWzgcxs0334zOzk584xvfwFtvvYVkMqk4pry8HOvXr0dnZyeamprQ1dWFkpISXHfdddIx119/PUpKStDZ2Ymamhp0dXVh/fr1KC8vl45pampCPB7HW2+9hW3btmXxjsfjiMfj0u/kJk5ehNosyPE0lkvV207TXM5Wx3dbTBY4shKTBY5OxGSBIysxWeDISkwWODoRkwWOrMRkgaMTMVngyEpMFji6LaaV411T52z//v145JFHcPToUUQiEdxyyy3YtGkTfvnLX0pV5wcGBhQ3Tffddx8++eQTtLe344UXXsA999yjuEkCgMbGRqxatQpPPfUUHn30UTzzzDP44IMPFMdUV1fjnnvuwUMPPYT77rsPH3/8saIIHjBzM/jMM8/ga1/7Whb3hx9+GD/84Q+z2l944QWpCPVsIBaL4c477wQw07+kCDUHBwcHBwcHBwcHx+eDqakp7Nmzh506Z2fOnMHf/d3fIRqNGt5AqAuNiqKY1aaG+hit4/M5Ro6HHnoI3/ve96Tfx8bGUFlZicbGxpwGqJFMJhUFRu1gcnJS+rmxsRHz58+3FY9AFEWkUikEg8Gc/T9bMZ3gSNMbAq/2JQv+sKKbhZhePXeciMnCuQOwoZuVmHxsc28OsXDuOBGTBY6Au/yx8tUoV9ycvfXWWxgcHERtba3Ulk6n8corr+CJJ56Qvg927tw5LFu2TDpmcHAQS5YsAQAsXboUiUQCIyMjiu+rDQ4OYuvWrdIx58+fz3r/CxcuKOK88cYbitdHRkaQTCalY9QIh8MIh8NZ7XoFHs3Azt/KYxAYFWq1CpLszc3Nro3pBEcCGt4QeLUvWfCHFd2sxAS8d+44EZOFcwdgQzcrMQn42OaueHK4+dxxIiYLHOVwgz/MFaHesWMHenp60N3dLf275pprcNddd6G7uxurV6/G0qVLIQiC9DeJRAKHDx+Wbrxqa2sRCoUUx5w9exbHjx+Xjqmvr8fo6CiOHDkiHfPGG29gdHRUcczx48dx9uxZ6ZhoNIpwOKy4eeTg4ODg4ODg4ODg4KAJV6ycFRcXY/369Yq2efPm4fLLL5fav/vd7+LRRx/FmjVrsGbNGjz66KMoKirCnj17AAAlJSW499578cADD+Dyyy/HokWL8OCDD2LDhg249dZbAQBXX301du7ciX379uGpp54CMPO9tdtuuw01NTUAZj7+t3btWuzduxc/+clPcPHiRTz44IPYt2+f5Y8ocnBwcHBwcHBwcHBwmIUrbs7M4O///u8xPT2Nb37zmxgZGcF1112HaDSK4uJi6Zhf/OIXCAaD+OpXv4rp6Wns2LEDzzzzDAKBgHTM888/j/vvv1/a1fGOO+7AE088Ib0eCARw4MABfPOb38QNN9yAwsJC7NmzBz/96U8/P7EcHBwcHBwcHBwcHJ6Da3ZrnEsYGxtDSUmJqR1Z1Egmk2htbaXymdvJyUlpE5Dx8XG+IYhN0PSGwKt9yYI/rOhmIaZXzx0nYrJw7gBs6GYlJh/b3JtDLJw7TsRkgSPgLn+s3Bu44jtnHOxhenra9TGd4OgEvNqXLPjDim5WYtIGK7pZOB+dAAu6WYlJG6zoZiGHnIBX+9LL/sjBb848glQqRTXWoUOHXB3TCY5OwKt9yYI/rOhmJSZtsKKbhfPRCbCgm5WYtMGKbhZyyAl4tS+97I8azHznjEUkk0mpIrjf70cgEEA6nUYmk5GOIe2pVAqiKErHk2NIO0EgEIDf78+qNB4MBqXj5e+vxQWY2dIzk8kgnU5LbT6fD8FgULedcCdx0uk0QqFQTk1q7lrtWpy1NBm1yzWROKlUylCrHnetdi2ORprM+EReF0VRWirX06TmbqRJHtts7ulpUuekmdwzaiea5LHM5p6au7xdzstK7un5RI4zk5NmfZLHspJ7uXySx7YyRui1yz2Xx7HjkzxuPmOEWpP83DGrNZdPJE4ymcxrjNDSJB/L5cjXJzlHM5rM+ESOkR9rpMmMT3KedsZyAnKMXk7m45Oco92xnEBr3NDTNFtzLu2xnJU510xOWvVJi6MX5lx1TvI51/z1nhnwmzOKaGlpQUtLi5QU0WgURUVFAIAVK1Zg8+bNOHbsGE6fPi39TU1NDa666iocOXIEFy5ckNoHBgawevVqvPLKKxgfH5fa6+vrsXjxYkSjUUWybtu2DYWFhWhtbZXaYrGY9HNHR4dU4DsYDGL37t0YGhpCV1eXdExxcTG2b9+OM2fOoLu7W2ovKyvD1q1bcfLkSanmHAD09vaitrbWtKZNmzZh5cqVmppIbTp5KQQtTQDQ3NyM6elpHDp0SGrT09TZ2YkdO3aY1mTkU1VVVRZHI01mfQJmTt5YLGZKUy6f+vr6FDyt5p6epuHhYZSXl5vWZOTT8PCwgqPV3NPTRGAl9/R8IgOr3G8ruaenicBK7hn51NnZqeBpNff0NAEznh89ejSnplw+9fb2KjjmM0ZoaQJmzh25R0aazPokCEJeY4SWpg0bNgCYGYsmJiZyajLrkyAI1MbyiooKADNjen9/f05NVnwSBIHKWE6+Mz0wMICenp6cmqz4JAgCtbG8oaFBiplL02zNuXV1dQCQ9fTfK3MuqVFldywvKyuT+lLO0Utzbl9fH9atW8fnXBO5NzU1lcVJD3xDEAdAvvQ3NDQkfenPysqZIAjYuXMnwuGwrad4k5OT0k3P4OAgFixYIL1m9yleR0cHbr31VkQiESorZ+l0Gu3t7di+fbv0pU27T/E6OjqwY8cOFBYWUnmKl8lk0Nraqqg0T2PlrKOjA42NjdLTJz1NZn2KxWJ4+eWXpb6k8RSvo6MDDQ0NCIfDVJ7ixeNxCIIgcaS1ckYmXZ/PZ/spXjKZRDQaNZWTVp7idXR0oKmpScopI03ydj3u09PT+NOf/iTxpLVydujQIdx6662KJ9j5+BSLxaQLazIO0Vo5I+eO+kvZ+foUi8XQ0dGB7du3o6CggNrKGTkfCS89TUbcSfv09LTEkRRWpbFy1t7ejqamJsUnGeyunBGehYWFhprMrpyReUcrJ/PxKZFISBwjkQi1lTP1uKGnycqcKwgCmpqaqMy5JCe3bdum4OjGOVd9XWB35SxXTlr1SRTFrOsCL8y56utAGitnTs255LpgtufcsbExlJaWmtoQhN+cOQA37tY4MTGBefPm2YrndTix6w8HPXB/3An5ODQyMqJ4SMThDvBzx93g/rgX3Bt3w03+8N0aObIgfypAI9bg4KCrYzrB0Ql4tS9Z8IcV3azEpA1WdLNwPjoBFnSzEpM2WNHNQg45Aa/2pZf9UYPfnHkE6i96243V1dXl6phOcHQCXu1LFvxhRTcrMWmDFd0snI9OgAXdrMSkDVZ0s5BDTsCrfellf9TgN2ccHBwcHBwcHBwcHBwuAL854+Dg4ODg4ODg4ODgcAH4zZlHoN7NzG6s4uJiV8d0gqMT8GpfsuAPK7pZiUkbrOhm4Xx0AizoZiUmbbCim4UccgJe7Usv+5P1Hny3RvrguzXOTbhp1x+ObHB/3Am+W6P7wc8dd4P7415wb9wNN/lj5d6AF6F2EKTWC2Ctzhnw2e6KdmquyI+Jx+MoKCiQfrdTcyWTyWBgYACVlZUIhUJU6pwBwMcff4zly5dLNWzs1FwhHCsqKlBQUECl5opWv9qtc0Z4rly5UlF5XkuTmrteezKZxJkzZ6S+tFtzhXBcsWKFFN+MH0Y+pVIpnD59WuJIq84ZAY2aK5lMBp988ompnDTrE+nLK664QupbI03ydj3uiUQC/f39Ek8adc4ymQzOnTuH5cuXK94zH5/kIGMijTpnmUwGZ8+eRWVlZdauWfn6lEwmMTAwgOXLlyMQCFCpc+bz+TAwMIBly5YpnrTm61MikZA4+v1+anXO5P/n0mTGJ5Lry5cvl+YeO3XORFHE2bNndXMyH5/S6bTEkdSEtFvnzO/348yZM1i2bJki/+3WOQNm/KEx55KcXLp0qYKj2+Zcn8+XdV1gZ841k5NWfSKQv68X5lz1daBb51z5+9gdy+3OuerXjcBvziiipaUFLS0tUlJEo1EUFRUBsF6JfGBgAKtXr7ZUXV1dMT4Wi0k/C4KASCQCwHp1db2K8SMjI6itrbVcXV1L08KFC9HT04Oenh5DTYC1ivEfffQRduzYYVqTkU9VVVVSX5rRZNYnAFi6dClSqZQpTbl8+uCDD3Dq1CmpL63mnp6mSCSC8vJy05qMfBocHFT4bTX39DQRWMk9PZ+CwWBWTlrJPT1NAFBZWYm+vj7TuWfk06uvvoqJiQmJp9Xc09OUSqUQCoVw9OjRnJqMfKqurpZ+7+joQCQSyWuM0NIEAIsXL1ack0aazPrU09OT1xihpWnDhg3o6enBqVOnMDExkVOTWZ96enqojeUVFRUAgN7eXvT39+fUZMWnnp4eKmP5/PnzMTExgXQ6rTgnafjU09NDbSxvaGjAu+++i3fffTenJqs+9fb2Uplz6+rq0N3dLZ3nuTTN5pyrHoNp+NTT00NlLC8rK0NdXR0A5XWBl+bc6elprFu3zvVzLgDbY7ndOXdqaiqLkx74xxodAFm6HBoakpYuraycCYKAnTt3IhwO23qKNzk5iYULFwIABgcHFR8nsvsUTxAENDU1IRKJUFk5S6fTaG1tRUNDQ87K8GaepBCOjY2NKCwspPIUL5PJZHG0u3JGeO7atUt6+qSnyaxPsVgM7e3tEk+7T/HUOUnjKV48HkdbW5vEkdbKmSAIaG5uhs/ns/0UL5lM4uDBg6Zy0spTPMKR5JSRJnm7Hvfp6WlEo1GJJ42VM7nn8ifr+fgUi8WkjzWScYjGypn83FF/9j9fn2KxGARBQENDAwoKCqisnGUyGbS1taGxsVHipafJiDtpn56eljiGQiFqK2ft7e1oamqSPslgpMmMT8SfhoYGFBYWGmoy41MqlUI0GtXNyXx8SiQSEsdIJEJl5UwUxaxxQ0/TbM25JCfVHOf6nGsmJ636JIpiFkcvzLnqnKSxcjbX59yxsTGUlpbyjzXONsjEKUcgEFBMfgTySRv47ONA6nZ57Fzt6p/Vf0M+8qaGXruaO/nZrCajdnIiafE0o1WPO3kvs5qM2snJq8XRjk/AzKDi8/lMacrVTniredr1ibyXFT/02uWx5K/T8EmLe652LY7kgt9sTto9n3K159Kk5knLJyta9bjL30PrYlANqz7ZGSPk7eT4UCgkcbDrE5mwg8GgJp98fZL3Jc3c03pfOz6RC0F1u97xubjr5WQ+PpELL/IQi7Sb5ajVTvx285xrxFFLk1E7i3Nurpy04pNRX3phziU/8znX3JhtFny3Ro+A9o46ZWVlro7pBEcn4NW+ZMEfVnSzEpM2WNHNwvnoBFjQzUpM2mBFNws55AS82pde9ifrPfjHGumD79Y4N+GmXX84ssH9cSf4bo3uBz933A3uj3vBvXE33OSPlXsDvnLmEah34bIb6/3333d1TCc4OgGv9iUL/rCim5WYtMGKbhbORyfAgm5WYtIGK7pZyCEn4NW+9LI/avCbM49AvdW03VgnTpxwdUwnODoBr/YlC/6wopuVmLTBim4WzkcnwIJuVmLSBiu6WcghJ+DVvvSyP2rwmzMODg4ODg4ODg4ODg4XgO/W6CDcVIRazgVwV0FM+S45co52t/UlxxhptbKtrxZHGlvpAzPbL4uiSGUrfXXxRRrb+gKf5SSNbX3VsdxYhJocZyYnZ7MgpjqXaG2lD0BxvutpkrcbnTckLq0i1PJzx6zWXD6ROMlk0tYW7epty7W45OuTnKMZTWa30pf/n0uT2a30CU87YzkBOUYvJ/PxSc7R7lhOoDVu6GmarTmX9ljOypxrJiet+qTF0Qtzrjon+ZzLi1C7Fm4uQk2KvwL0CmK+99572LJlC5Ui1Jdffjl8Pp+ikCONgpidnZ1MFKHOZDIYHx+nUoT6ww8/VPCkVRDz4sWLWLZsGZWCmBcvXlRwdGMR6oKCAgXHXJrM+kTqKdEqiNnZ2angSasI9YoVK3Dx4kUcOXIkp6bZKkK9dOlSZDIZtLe3m9Jk1idBEKgVod64cSNWrFiBzs5OqkWoBUGgNpY7WYRaEARqRahXrFiBTz/9FMeOHcupyYpPgiBQG8ubmpqwdOlSxbhByydaRaivvfZarFixAocOHaJehJrmnLtmzRpEIhHqcy6pe+X2ItQszLkffvgh1q5d6+o5l2C251xehHqW4cYi1CMjI4rdGmk8bbX7dCgfTUbtTmvKZOgXoZ5tTXPJJ9pFqN2gSd7Oqk9OFaGeTU1zzad0mn4R6tnWNJd8ol2E2g2a1NxZ1eREEerZ1jSXfHKiCHW+mngRapfATUWo/f7swp35FvBLp9M4duwYNm7caEmTUXs6ncbx48excePGrFj5FMRUc6RRlJCcvDQLYsp5BgLaBWCt+gRAsy/z9UndlzQKYoqiqMnx8yoeaYaj1Zw045PabxqafD4fenp6snja8SmdTuOdd97Bxo0bXVuEOp1Oo7u7W5ej+vhc3P1+P0RRVPhjpMmsT/K+1NJl1Se/35/FMd+xXKudVhFqea7TKEKdKyfz8UnOkcacS3iq/dHTlKtdzT1XTpr1Sd6XWnHcMuem02nNsU1LkxnuZnPSik9OFKFmYc5Vc3TrnGtFk1nu+c65vAg1RxZo76hz+vRpV8d0gqMT8GpfsuAPK7pZiUkbrOhm4Xx0AizoZiUmbbCim4UccgJe7Usv+6MGXznj4ODg4ODg4ODg4JgzmJyclD5SPzIyggULFswuIQvgK2ccHBwcHBwcHBwcHBwuAL858wj0vpuUb6yamhpXx3SCoxPwal+y4A8rulmJSRus6GbhfHQCLOhmJSZtsKKbhRxyAl7tSy/7owb/WKNHYPQl/XxiXXXVVdTiORHTCY5OwKt9yYI/rOhmJSZtsKKbhfPRCbCgm5WYtMGKbhZyyAl4tS+97I8a7r495aAG9TakdmN1dna6OqYTHJ2AV/uSBX9Y0c1KTNpgRTcL56MTYEE3KzFpgxXdLOSQE/BqX3rZHzX4ypmDSCaT0jarZuspqCvD26kRIT8mkUgofrdbc+XChQtIpVKG9TCs1IgQRREXLlxAIpGQXrNT94JwTCaT1Gp5aPWr3boXhGcmk4EoilTqk6RSKUVf2q3lQTim02kEg0Eq9UnS6bSCI606ZwQ06pNkMhnTOWml5sqFCxcgiiK1OjIkJuFJo46M3HP5e+bjkxxkTMxnjFBrkp87ZrXm8imRSEh9SeLY9YnkUTKZpFLvR85RFEVqdc7k/+fSZMYneV6ST27YqWFExjW9nMzHJ3lfku237dZl0prL9DRZmXOBGX/I+GlnztUa24w0zdacq9WXduZcMzlp1ScC+ft6Yc5VXwe6cc5Vv8dsz7nq143Ab84ooqWlBS0tLVKyRKNRFBUVAbBeiXxgYACrV6+2VF1dXTE+FotJP3d0dCASiQCwXl1dr2J8b28vamtrLVdX19JEimXLK8NraQKsVYzv7OzEjh07TGsy8qmqqiqLo5Emsz4BMydvLBYzpSmXT319fQqeVnNPT9Pw8DDKy8tNazLyaXh4WMHRau7paSKwknt6PpGBVe63ldzT00RgJfeMfOrs7FTwtJp7epqAGc+PHj2aU5ORT9XV1dLvZBzKZ4zQ0gTMnDtyj4w0mfVJEIS8xggtTRs2bAAwMxZNTEzk1GTWJ0EQqI3lFRUVAGbG9P7+/pyarPgkCAKVsZzsujYwMICenp6cmqz4JAgCtbG8oaFBiplL02zNuXV1dQCAQ4cO2R7LWZxzSVFiu2N5WVmZ1Jdyjl6ac/v6+rBu3TpXzrnya2Bg9ufcqakpmIVPlN8WclDB2NgYSkpKMDQ0JFUBt7JyJggCdu7ciXA4bOsp3uTkpHTTMzg4qNhG1O5TPEEQ0NTUhEgkQmXlLJ1Oo7W1FQ0NDVKhPrtP8QRBQGNjIwoLC6k8xctkMlkcaaycCYKAXbt2IRQKUVk5i8ViaG9vl3jSeIonz0kaT/Hi8Tja2tokjrRWzsik6/P5bD/FSyaTOHjwoKmctLJyRjiSnDLSJG/X4z49PY1oNCrxpLVyRjyXr37l41MsFpMurMk4RGvljJw78ifYRlpz+RSLxSAIAhoaGlBQUEBt5aytrQ2NjY2Koqb5+jQ9PS1xJIVvaayctbe3o6mpKatArZ2VM8KzsLDQUJPZlbNoNKqbk/munBGOkUiE2sqZetzQ0zRbcy7JSTXHuT7nmslJqz6JopjF0Qtzrjonaayc0Z5z5dfAIyMjKC4untU5d2xsDKWlpRgdHZXuDfTAV84chJ2K8WTysVJdXd0u/zkSiWT9DfkYhxp67YR7IBDApk2bUFBQYElTrvZNmzZJE6SeplzthDvhGA6HTWky005OXi1f8/WJ8AwGg/D5fIaazLYXFBRo9mW+PhGOuarcW2kPhUKaHGn4JOdutl2LYzAYtJSTZnwifUkGcxqawuGwJk87Psk9z2eM0IP63LHjk/zc0eKiPj4Xd7/fj0gkktWXdnMvk8lIY5HW+1r1SYtjvmO5VrveuaAFI5+IP5FIRLp5zmcsl3Mzysl8fPL7/Vl9aWfOBT7zW2vcsOsT+dnuuGfEUUuTUbuTc67P59PlmY9PZnPSik/kgtzO9Z66nYU5V30d6MY516wfn9ecq/e6FvjNmUegd/GSb6yVK1dSi+dETCc4OgGv9iUL/rCim5WYtMGKbhbORyfAgm5WYtIGK7pZyCEn4NW+9LI/We/haHQO14D2jjodHR2ujukERyfg1b5kwR9WdLMSkzZY0c3C+egEWNDNSkzaYEU3CznkBLzal172Rw1+c+YR0PxqoSiKGB8fd3VMJzg6Aa/2JQv+sKKblZi0wYpuFs5HJ8CCblZi0gYrulnIISfg1b70sj9quOLm7Mknn8TGjRtx2WWX4bLLLkN9fT0OHjwove7z+TT//eQnP5GOueWWW7Jev/POOxXvMzIygr1796KkpAQlJSXYu3cvLl26pDjm9OnTuP322zFv3jyUlpbi/vvvl7ZV5uDg4ODg4ODg4ODgcAqu+M5ZRUUFfvzjH+PKK68EADz77LP40pe+hHfeeQfr1q3D2bNnFccfPHgQ9957L77yla8o2vft24f/7//7/6TfyW48BHv27EF/fz/a2toAAPfddx/27t2L//7v/wYws2PV7t27UVZWhtdeew3Dw8O4++67IYoiHn/8ceq6OTg4ODg4ODg4ODg4JIguxcKFC8Vf//rXmq996UtfErdv365ou/nmm8W/+7u/04333nvviQDE119/XWrr6uoSAYjvv/++KIqi2NraKvr9fnFgYEA65re//a0YDofF0dFR09xHR0dFAJb+hiCRSIh//OMfxUQiYflv1ZiYmBABiADEsbEx2/EI0um0eP78eTGdTrs2phMcaXpD4NW+ZMEfVnS7PaZ8HBoZGbFP7v/B7bqdisnCuSOKbOhmJSYf29ybQyycO07EZIGj2+YeK/cGrlg5kyOdTuP3v/89JicnUV9fn/X6+fPnceDAATz77LNZrz3//PN47rnnsGTJEuzatQs/+MEPUFxcDADo6upCSUkJrrvuOun466+/HiUlJejs7ERNTQ26urqwfv16lJeXS8c0NTUhHo/jrbfewrZt2zQ5x+NxxONx6fexsTEAkGq9WAE53urfGcUCoFkt3Q4WLlyIdDqtqDHhtpi049H0Rg4v9qUTMZ3whwXdbo+prk3G/XFfPFbGNha8cSImH9vcm0OsnDtOxHQ7R7fNPVbe3zU3Zz09Paivr5cKlv7hD3/A2rVrs4579tlnUVxcjC9/+cuK9rvuugurVq3C0qVLcfz4cTz00EN49913pQre586dw+LFi7PiLV68GOfOnZOOWbJkieL1hQsXoqCgQDpGCz/60Y/wwx/+MKs9Go2iqKgot3gNyCuk5wt5dfT29nZEIhHbMTnoeMPhHLg/7oJ8HOro6ODjkIvBzx13g/vjXnBv3Ae3zT1TU1Omj3XNzVlNTQ26u7tx6dIlvPjii7j77rtx+PDhrBu03/zmN7jrrruyOnnfvn3Sz+vXr8eaNWtwzTXX4O2338aWLVsAQCo6KIcoiop2M8eo8dBDD+F73/ue9PvY2BgqKyvR2NiYswq4GvIK9lYK1mlhcnJS+nn79u1YsGCBrXgENDk6FZMFjqzEZIGjEzFZ4MhCTC+PQ07EZIGjEzFZ4MhKTBY4OhGTBY6sxGSBo9vmHvKpOjNwzc1ZQUGBtCHINddcg6NHj+Kxxx7DU089JR3z6quv4sSJE/jd736XM96WLVsQCoVw8uRJbNmyBUuXLsX58+ezjrtw4YK0WrZ06VK88cYbitdHRkaQTCazVtTkCIfDCIfDWe1aFePNws7fymPQjKcV3+0xWeDISkwWODoRkwWObo7JxyFnYrLA0YmYLHBkJSYLHJ2IyQJHVmK6maPb5h4rx7piK30tiKKo+B4XADz99NOora3FF77whZx/39vbi2QyiWXLlgEA6uvrMTo6iiNHjkjHvPHGGxgdHcXWrVulY44fP67YHTIajSIcDqO2tpaGLA4ODg4ODg4ODg4ODm3ktVUJZTz00EPiK6+8In700UfisWPHxO9///ui3+8Xo9GodMzo6KhYVFQkPvnkk1l/f+rUKfGHP/yhePToUfGjjz4SDxw4IF511VXi5s2bxVQqJR23c+dOcePGjWJXV5fY1dUlbtiwQbztttuk11OplLh+/Xpxx44d4ttvvy2+/PLLYkVFhfjtb3/bkh437tY4Pj5uOx5BJpMRR0dHxUwm49qYTnB0Ylcmr/YlC/6wotvtMZ3aMcvtup2KycK5I4ps6GYlJh/b3JtDLJw7TsRkgaPb5h7mdms8f/489u7di7Nnz6KkpAQbN25EW1sbGhoapGP2798PURTxta99LevvCwoK8Kc//QmPPfYYJiYmUFlZid27d+MHP/gBAoGAdNzzzz+P+++/H42NjQCAO+64A0888YT0eiAQwIEDB/DNb34TN9xwAwoLC7Fnzx789Kc/dVA9m1DXkHNjTCc4OgGv9iUL/rCim5WYtMGKbhbORyfAgm5WYtIGK7pZyCEn4NW+9LI/crjiY41PP/00Pv74Y8TjcQwODuLll19W3JgBMwWjp6amUFJSkvX3lZWVOHz4MIaHhxGPx3Hq1Ck89thjWLRokeK4RYsW4bnnnsPY2BjGxsbw3HPPZX1BcMWKFXjppZcwNTWF4eFhPP7445rfJ2MNqVSKaqzW1lZXx3SCoxPwal+y4A8rulmJSRus6GbhfHQCLOhmJSZtsKKbhRxyAl7tSy/7o4Yrbs44ODg4ODg4ODg4ODi8Dld8rHGuQl70zu/3IxAIIJ1OI5PJSMeQ9lQqBVEUpePJMaSdIBAIwO/3ZxWzCwaD0vHy99fiAszsGpPJZBQF9Hw+H4LBoG474U7ipNNphEKhnJrU3LXatThraTJql2sicVKplKFWPe5a7VocjTSZ8Ym8LooiRFE01KTmbqRJHtts7ulpUuekmdwzaiea5LHM5p6au7xdzstK7un5RI4zk5NmfVIXhjebe7l8kse2Mkbotcs9l8fJxyc5yDiUzxih1iQ/d8xqzeWTvKBsPmOElib5WC5Hvj6pi97aGcsJyDHqgqp2fJLztDOWE5Bj9HIyH5/kHO2O5QRa44aeptmac2mP5azMuWZy0qpPWhy9MOeqc9KNc676PWZ7zmWyCPVcQEtLC1paWqRkkRehXrFiBTZv3oxjx47h9OnT0t/U1NTgqquuwpEjR3DhwgWpfWBgAKtXr8Yrr7yC8fFxqb2+vh6LFy9GNBpVJOu2bdtQWFiI1tZWqU2vAF8wGMTu3bsxNDSErq4u6Zji4mJs374dZ86cQXd3t9ReVlaGrVu34uTJkzhx4oTU3tvbi9raWtOaNm3ahJUrV2pqWrhwIQBlIUctTQDQ3NyM6elpHDp0SGrT09TZ2YkdO3aY1mTkU1VVVRZHI01mfQJmTt5YLGZKUy6f+vr6FDyt5p6epuHhYZSXl5vWZOTT8PCwgqPV3NPTRGAl9/R8IgOr3G8ruaenicBK7hn51NnZqeBpNff0NAEznh89ejSnJiOfqqurpd/JOJTPGKGlCZg5d9QFYO36JAhCXmOElqYNGzYAmBmLJiYmcmoy65MgCNTG8oqKCgAzY3p/f39OTVZ8EgSBylg+f/58ADNzY09PT05NVnwSBIHaWE6+kiHPSbfNuXV1dQCAQ4cO2R7LWZxzBUGgMpaXlZVJfSnn6KU5t6+vD+vWrXPlnCu/BgZmf861UoTaJ8pvCzmoYGxsDCUlJRgaGpKKUFtZORMEATt37kQ4HLb1FG9yclK66bl48aI0uQH2nuKRFZ6CggLDJyxWnqT4fD5MT08jGAxKT6PsPMUjHEkdChpP8TKZDFpbWxWFB+0+bSU8I5EIfD4flZWzVCqFRCIh9aXdp3iEYzgcRiAQoPIUL51OIx6PSxxprZyRSdfn81F5iheLxUzlpFmfRFFEOp1GJBJBJpOh8hSPrFIQnjRWzkRRlHJHziUfn2KxmDT2DA4OYsGCBVRWzsjr8tXiXFpz+ZRKpZBKpRAMBuH3+6msnKn70EiTEXf5yhnh6PP5qK2ctbe3o6mpSfFJBjs+kXEjGAxK46WdlTO9/rSzckY8DwaDkud2V85IbMLNSJOVlTNBENDU1IRIJGJ7ziV9SM7zXJpma84l4we5RjDSZMYnMzlp1SdRFLOuC7ww56qvA2mtnNGcc+XXwCMjIyguLp7VOXdsbAylpaUYHR2V7g30wFfOHIRWgbpAIKCY/AiIiQRkSV/dLo+dqz1XAT4y+Kmh1064kxOI7FZjVpNROznRCwsLFZOFWkeudsJdzTGXJjPt5OTV6st8fSI8yWBppMlsOxlU1H2Zr0/qvrTih147GbTVHGn4JOdutl2Pu5WcNOMT6ctIJEJV0/T0dBZPOz6Joojx8XEUFxdrcrHqk/w95O9jxyc5x3zGCK32UCgk5br8YtCOT6IoYnJyEsXFxVl5pMcxV7uaY75juVa71vvm45N83CA87fiUKyfz8UmLo505l/CMxWKaftv1ifxsd9yjmZNOzrnkgTV5eGmkyQx3szlpxSf5x4vzvd5Tt7Mw56o5unHONevH5zXnzoki1Bx0oX7qYDeW+uMQbovpBEcn4NW+ZMEfVnSzEpM2WNHNwvnoBFjQzUpM2mBFNws55AS82pde9kcNfnPGwcHBwcHBwcHBwcHhAvCbMw4ODg4ODg4ODg4ODheA35xx5AW9z+K6KaYTHJ2AV/uSBX9Y0c1KTNpgRTcL56MTYEE3KzFpgxXdLOSQE/BqX3rZHzn4bo0OgOzWaGZHFjWSySRaW1vR3Nxs6cuDWpicnJR2SZuYmMC8efNsxfM6aHrDQR/cH3dCPg6NjIxgwYIFs0uIIwv83HE3uD/uBffGvXDb3GPl3oCvnHkEWls524k1ODjo6phOcHQCXu1LFvxhRTcrMWmDFd0snI9OgAXdrMSkDVZ0s5BDTsCrfellf9TgN2cOgtRCSCaTUi0GUrVc3Z5KpaQ24LObKXl7MplU1HSS/yO1MdTtBLFYLKud1E8h/8jOM3rthHssFkNXVxcSiYRpTXLuWu3pdBpdXV0KnnqajLQS7oRjPB43pcmsT+q+t+sT4UnqaRhpMutTIpFQ9KWV3NPSRDgSPmb9MPIpmUwqOJrNvVw+EVjJPT1NqVTKdE6a9Yn0ZTqdtpx7etzj8biCp9UxQqtd7rmV3DM6b+Sc8hkj1Jrk546V3DPyicSMxWJ5jxFafMhYZDb3jHySc7SSe/mcT3Z8kvO0M5aTfyTP9XIyH01yjrTmXK1xg4ZP5HcaPpGcVHN025yrdV1gxyczOZmPT+r39cKcq74OdOucK4cb5lyzYOPDnYygpaUFLS0tkrHRaBRFRUUArFciHxgYwOrVqy1VV1dXjJdXR+/o6EAkEgFgvro6gV7F+N7eXtTW1lqurq6liRQKlFeG19IEWKsY39nZiR07dpjWZORTVVVVFkcjTWZ9Aj67UDCjKZdPfX19Cp5Wc09P0/DwMMrLy01rMvJpeHhYwdFq7ulpIrCSe3o+kc+Uy/22knt6mgis5J6RT52dnQqeVnNPTxMw4/nRo0dzajLyqbq6WvqdjEP5jBFamoCZc0fukZEmsz4JgpDXGKGlacOGDQBmxqKJiYmcmsz6JAgCtbG8oqICwMyY3t/fn1OTFZ8EQaAylpOPJw0MDKCnpyenJis+CYJAbSxvaGiQYubSNFtzbl1dHQBkbQfulTlXEAQqY3lZWZnUl3KOXppz+/r6sG7dOlfOufJrYGD259ypqSmYBf/OmQMgnysdGhqSPldqthJ5MpmEIAjYuXMnwuGw1E5gthI5AEV19MHBQcXnbc1UV1e3E+6EY1NTEyKRiOXq6lrt6XQara2taGhokD63rVcZ3kzFeMKxsbERhYWFOTWpuWu1ZzKZLI5Gmsz4RHju2rULoVDIUJNZn2KxGNrb2yWeZnNPzyd1TprJvVw+xeNxtLW1SRzN5p6RT4Rnc3MzfD6f6dzT8ymZTOLgwYOmctKsT3KOJKeMNMnb9bhPT08jGo1KPK2MEXrtcs/lRT7z8SkWi0kX1mQcymeMUGuSnzt6BWqt+hSLxSAIAhoaGlBQUGB5jNDSlMlk0NbWhsbGRsWXyPP1aXp6WuJICt/mO5YTpNNptLe3o6mpSVGM1Y5PxJ+GhgapUG0+YzlBKpVCNBrVzcl8fEokEhLHSCRiaywnEEUxa9zQ0zRbcy7JSTXHuT7nmslJqz6JopjF0Qtzrjon7YzlhDvtOVd+DTwyMoLi4uJZnXPHxsZQWlpq6jtnfOXMQdipGE8mH6vV1dWTAUFBQUHW3+Sqrq4G4e7z+VBcXCxxy6e6uhqZTAbFxcXSBZGeplzthDvhKB8sjTSZaScnr5av+fpEeBLORprMtgeDQc2+zNcnwpH8rRU/9NoDgYAmRxo+ybmbbdfrdys5acYn0pc+n4+aplAopMnTjk9yz7Xe16pP8vfQuhhUw4xP8nPHqq96PhUUFGT1pV2fUqmUNBZp8bTqkxbHfMdyrXat983HJ+JPQUGBdPOcz1hOkCsn8/WJcKQx5wKf+a01btj1ifxsd9wz4qilyajdyTlXFEVdnvn4ZDYnrfhELsjtXO+p21mYc9XXgW6cc8368XnNuVY2jOErZw6A79Y4N0HTGw764P64E27bMYsjG/zccTe4P+4F98a9cNvcw3dr5MgC7R11PvnkE1fHdIKjE/BqX7LgDyu6WYlJG6zoZuF8dAIs6GYlJm2wopuFHHICXu1LL/ujBr858wjUu6bZjdXd3e3qmE5wdAJe7UsW/GFFNysxaYMV3Sycj06ABd2sxKQNVnSzkENOwKt96WV/1OA3ZxwcHBwcHBwcHBwcHC4Avznj4ODg4ODg4ODg4OBwAfjNmUeg3mrabqyysjJXx3SCoxPwal+y4A8rulmJSRus6GbhfHQCLOhmJSZtsKKbhRxyAl7tSy/7k/UefLdG+nBjnbORkRHFbo00auPYreWRjyajdqc1OVHnbLY1zSWfaNc5c4MmeTurPjlV52w2Nc01n5yoczbbmuaST07UFp1tTWrurGpyos7ZbGuaKz45VecsX028ztksoaWlBS0tLVKyRKNRFBUVAbBeiXxgYACrV6+2VF1dXTFeXh29vb0dkUgEgPnq6gR6FeMrKyuxZcsWy9XVtTRdfvnlaGtrU5wgWpoAaxXj58+fjx07dpjWZORTVVUVAGX1eiNNZn0CgKamJiQSCVOacvl04sQJnDx50pQmKz5de+21WLZsmWlNRj6dP38eR44cMa3JrE8EVnJPz6eCggK0t7eb1mTWp6KiImzfvt1S7hn5dPjwYUxMTJjSZMWnqqoqlJSU2Papurpa+r2jowORSCSvMUJL0xVXXIGamhpHfMpnjNDStHHjRsTjcQwMDDjiEw1NFRUVAIDe3l709/fn1ERr3LM6li9fvhzhcBjHjh3LqYnWuJfPWH7ixAl8/PHHOTVZ9am3txe1tbVUxvLR0VH09fU54hOtOXfNmjU4dOgQpqamcmqarfOprq4OgPK6wEtz7po1a7B27VpXzrnya2AAsz7nyvM4F/jKmQNw48oZeWJN4LaneOl0Ouvpk50nKYRjY2MjCgsLXbtyRnju2rULoVCIyhOvWCyG9vZ2iafdp0PqnKTxxCsej6OtrU3i6MaVs2QyiYMHD5rKSbM+yTmSnDLSJG/X4z49PY1oNCrxpPFkUu65uhCwW1bO5OeO+uMl+foUi8UgCAIaGhqkAqN2nyBnMhm0tbWhsbExqyh8Pj5NT09LHEnhWzeunBF/GhoaUFhYaKjJjE+pVArRaFQ3J/PxKZFISBwjkQiVJ/2iKGaNG3qaZmvOJTmp5jjX51wzOemGlTMW5lx1TtJYZaI95zq1cpbvnMtXzlwCOxXjyeRjpbq6ul39s/pvclVXV0PNnfxstbq6Vjs5wbR4mtGqx528l1lNRu3k5NXiaMcnYGZQ8fl8pjTlaie81Tzt+kTey4ofeu3yWPLXafikxT1XuxZHcsFvNiftnk+52nNpUvOk5ZMVrXrc5e+hdTGohlWf7IwR8nZyfCgUkjjY9YlM2MFgUJNPvj7J+5Jm7mm9rx2fyIWgul3v+Fzc9XIyH5/IhRd5iEXazXLUaid+u3nONeKopcmoncU5N1dOWvHJqC+9MOeSn90455r14/Oac60UKecbgnBwcHBwcHBwcHBwcLgA/ObMI9B6umAn1ooVK1wd0wmOTsCrfcmCP6zoZiUmbbCim4Xz0QmwoJuVmLTBim4WcsgJeLUvveyPGvw7Zw6AfOfMzOdK1Ugmk2htbUVzc7OlJVAtTE5OSt/1mJiYUOzWyGEdNL3hoA/ujzshH4dGRkYU333lcAf4ueNucH/cC+6Ne+G2ucfKvYG7b085qEH+pUkasd555x1Xx3SCoxPwal+y4A8rulmJSRus6GbhfHQCLOhmJSZtsKKbhRxyAl7tSy/7owa/OfMI5DvR0Ih1+vRpV8d0gqMT8GpfsuAPK7pZiUkbrOhm4Xx0AizoZiUmbbCim4UccgJe7Usv+6MGvznj4ODg4ODg4ODg4OBwAfhW+g6C1HoBrNU5Az5b6bJTc0VdE0j+u92aK8DM0m4oFKJS50yLs92aK+QYI61Waq5ocaRR5wyYqY0jiiKVOmfymlq5NJmtuQJ8lpM0aq6oY9Gqc0ZAo+YKOc5MTlqpc0Zgt36WXJM8Nq06ZwAU57ueJnm70XlD4iaTybzGCLUm+bljVmsun0icZDJpq36WuqaUFpd8fZJzNKPJbJ0z+f+5NJmtc0Z42hnLCcgxejmZj09yjnbHcgKtcUNP02zNubTHclbmXDM5adUnLY5emHPVOenGOVf9HrM956pfNwK/OaOIlpYWtLS0SMkSjUZRVFQEwHol8oGBAaxevdpSdXV1xXh5dfSOjg5EIhEA5qurE+hVjH/vvfewZcsWy9XVtTRdfvnl8Pv9EATBUBNgrWJ8Z2cnduzYYVqTkU9VVVUAoOBopMmsT8DMIDw+Pm5KUy6fPvzwQwVPq7mnp+nixYtYtmyZaU1GPl28eFHB0Wru6WkisJJ7ej4VFBQoOObSZNanoqIi+P1+S7ln5FNnZ6eCp9Xc09NUU1ODixcv4siRIzk1GflUXV0t/U7GoXzGCC1NV1xxBTKZDNrb201pMuuTIAh5jRFamjZu3Iiamhp0dnZiYmIipyazPgmCQG0sr6ioAAD09vaiv78/pyYrPgmCQGUsnz9/PmpqavDpp5/i2LFjOTVZ8UkQBGpjeVNTE6644grFuEHLp97eXtTW1toey6+99lrU1NTg0KFDtsdyJ+fcNWvWoKioiPqcKwgClbG8rKwMdXV1UkwzmubanPvhhx9i7dq1rpxz5dfAAGZ9zp2amoJZ8N0aHQDZkWVoaEjakcXKypm8Mrydp3jq6ujy3RppPG21+3QoH01G7U5rymQyaG1tVVSvZ13TXPKJnDvNzc3w+XxzQpO8nVWfYrGYtGPW4OAgFixYwLymueZTOp1Ge3s7mpqaFJ9kYFnTXPKJjG1NTU2IRCJzQpOaO6uaRFHMui5gXdNc8Ul9DVxcXDyrmsbGxlBaWmpuJ3eRgzpGR0dFAOLo6Kjlv00kEuIf//hHMZFI2OYxMTEhAhABiJcuXbIdjyCZTIp/+ctfxGQy6dqYTnCk6Q2BV/uSBX9Y0e32mPJxaGRkxD65/we363YqJgvnjiiyoZuVmHxsc28OsXDuOBGTBY5um3us3BvwDUE8ApHiAqkoirhw4YKrYzrB0Ql4tS9Z8IcV3azEpA1WdLNwPjoBFnSzEpM2WNHNQg45Aa/2pZf9UYPfnHFwcHBwcHBwcHBwcLgArrg5e/LJJ7Fx40ZcdtlluOyyy1BfX4+DBw9Kr3/961+Hz+dT/Lv++usVMeLxOL7zne+gtLQU8+bNwx133KH4YjMw85nTvXv3oqSkBCUlJdi7dy8uXbqkOOb06dO4/fbbMW/ePJSWluL+++9HIpFwTDsHBwcHBwcHBwcHBwfgkpuziooK/PjHP8abb76JN998E9u3b8eXvvQl9Pb2Ssfs3LkTZ8+elf6pd6n57ne/iz/84Q/Yv38/XnvtNUxMTOC2225TfFlwz5496O7uRltbG9ra2tDd3Y29e/dKr6fTaezevRuTk5N47bXXsH//frz44ot44IEHnO8EhyH/kjeNWJs2bXJ1TCc4OgGv9iUL/rCim5WYtMGKbhbORyfAgm5WYtIGK7pZyCEn4NW+9LI/WTD/VbbPFwsXLhR//etfi6Ioinfffbf4pS99SffYS5cuiaFQSNy/f7/UNjAwIPr9frGtrU0URVF87733RADi66+/Lh3T1dUlAhDff/99URRFsbW1VfT7/eLAwIB0zG9/+1sxHA5b2tzDjRuCTExM2I7ndTjxxV8OeuD+uBNOfSmbgx74ueNucH/cC+6Ne+G2ucfKvYHr6pyl02n8/ve/x+TkJOrr66X2P//5z1i8eDEWLFiAm2++GY888ggWL14MAHjrrbeQTCbR2NgoHV9eXo7169ejs7MTTU1N6OrqQklJCa677jrpmOuvvx4lJSXo7OxETU0Nurq6sH79epSXl0vHNDU1IR6P46233sK2bds0OcfjccTjcen3sbExANmFn81AXWDUDuQxpqenpRoSdpFKpdDZ2YmtW7dKW4W6LaYTHGl6Q+DVvmTBH1Z0uz2munA098dd8QA2xjYWvHEqJh/b3JtDLJw7TsRkgaPb5h4r7++aOmc9PT2or6+XauK88MILaG5uBgD87ne/w/z587Fy5Up89NFH+Kd/+iekUim89dZbCIfDeOGFF3DPPfcobpAAoLGxEatWrcJTTz2FRx99FM888ww++OADxTHV1dW455578NBDD+G+++7Dxx9/jGg0qjgmHA7jmWeewde+9jVN7g8//DB++MMfZrW/8MILUhHq2UAsFsOdd94JANi/f79UhJqDg4Pj8wIfhzg4ODg4Pm+4be6ZmprCnj17TNU5c83KWU1NDbq7u3Hp0iW8+OKLuPvuu3H48GGsXbsW/+t//S/puPXr1+Oaa67BypUrceDAAXz5y1/WjSmKInw+n/S7/Gc7x6jx0EMP4Xvf+570+9jYGCorK9HY2Ji70JwKpNikvKBhvpicnJR+3r59OxYsWGArHgFNjk7FZIEjKzFZ4OhETBY4shDTy+OQEzFZ4OhETBY4shKTBY5OxGSBIysxWeDotrmHfKrODFxzc1ZQUIArr7wSAHDNNdfg6NGjeOyxx/DUU09lHbts2TKsXLkSJ0+eBAAsXboUiUQCIyMjUjVwABgcHMTWrVulY86fP58V68KFC1iyZIl0zBtvvKF4fWRkBMlkUjpGC+FwGOFwOKs9FArlnWB2/lYeg2Y8rfhuj8kCR1ZissDRiZgscHRzTD4OOROTBY5OxGSBIysxWeDoREwWOLIS080c3Tb3WDnWFbs1akEUxayPKRIMDw/jzJkzWLZsGQCgtrYWoVAIgiBIx5w9exbHjx+Xbs7q6+sxOjqKI0eOSMe88cYbGB0dVRxz/PhxnD17VjomGo0iHA6jtraWusbPE7R31Kmvr3d1TCc4OgGv9iUL/rCim5WYtMGKbhbORyfAgm5WYtIGK7pZyCEn4NW+9LI/arhi5ez73/8+du3ahcrKSoyPj2P//v3485//jLa2NkxMTODhhx/GV77yFSxbtgwff/wxvv/976O0tBT/43/8DwBASUkJ7r33XjzwwAO4/PLLsWjRIjz44IPYsGEDbr31VgDA1VdfjZ07d2Lfvn3Satx9992H2267DTU1NQBmvqO2du1a7N27Fz/5yU9w8eJFPPjgg9i3b5/ljye6DX4/vftwv98vbcbi1phOcHQCXu1LFvxhRTcrMWmDFd0snI9OgAXdrMSkDVZ0s5BDTsCrfellf7Lew9HoJnH+/Hns3bsXNTU12LFjB9544w20tbWhoaEBgUAAPT09+NKXvoTq6mrcfffdqK6uRldXF4qLi6UYv/jFL/A3f/M3+OpXv4obbrgBRUVF+O///m/Fne3zzz+PDRs2oLGxEY2Njdi4cSP+/d//XXo9EAjgwIEDiEQiuOGGG/DVr34Vf/M3f4Of/vSnn2t/OAGaOwklk0kcOHDA1TGd4OgEvNqXLPjDim5WYtIGK7pZOB+dAAu6WYlJG6zoZiGHnIBX+9LL/qjhipWzp59+Wve1wsJCtLe354wRiUTw+OOP4/HHH9c9ZtGiRXjuuecM46xYsQIvvfRSzvfzOlKplOtjOsHRCXi1L1nwhxXdrMSkDVZ0s3A+OgEWdLMSkzZY0c1CDjkBr/all/2RwxUrZxwcHBwcHBwcHBwcHF4Hvznj4ODg4ODg4ODg4OBwAVzxsca5CnlFcr/fj0AggHQ6jUwmIx1D2lOpFERRlI4nx5B2gkAgAL/fn/VZV1KlXL7UKj9GHhuY2dIzk8kgnU5LbT6fD8FgULedcBdFETfeeKNU+y2XJjV3rfZgMIgbb7xRwVNLk1G7XBPhSJBLk5q7VrtWvxppMuMT4RkIBCCKoqEmNXe9dp/Pp+hLs7mnp4lwJH1gJveM2kOhEPx+v4Kj2dxTc5e3y3lZyT09nwKBgOmcNOuTKIq46aabLOeekU8AFDytjBF67aIoYtu2bVlx8vFJDjIm5jNGqLmIoohbbrkFgUDAtNZcPpFcJ+ciDZ/8fj+2bdsm6TfSZMSdtMs5JpNJW2M5ATlGfqyRJjM+yXmSY/IZy+Uwysl8fSIcM5mMrbFcfvwtt9xCdc4lcdLpNEKhkO05l+SkmqPb5txgMIibbrpJwdPOnGsmJ636RCB/Xy/MuerrQDfOuer3mO0518p31PjNGUW0tLSgpaVFSpZoNIqioiIAM99l27x5M44dO4bTp09Lf1NTU4OrrroKR44cwYULF6T2gYEBrF69Gq+88grGx8el9vr6eixevBjRaFSRrNu2bUNhYSFaW1ultlgsJv0cjUal6ujBYBC7d+/G0NAQurq6pGOKi4uxfft2nDlzBt3d3VJ7WVkZtm7dipMnT+LEiRNSu1VNmzZtwsqVKzU1lZWVoaurK6cmAGhubsb09DQOHToktX0emqqqqgBAUbLBSJNZnwBg165diMVi1DR98MEHpjRZ8en666/HkiVLTGvK5dPrr79uSZMZnwis5J6eT5FIBK+++qolTWZ8Ki0tpXo+vfrqq7ZyT09TY2MjFZ+qq6ul3zs6OhCJRPIaI7Q03XjjjUilUjh48KApTbMx7n3hC1/A8uXLHfOJhqaKigoAQG9vL/r7+3NqojXuWdV044034syZM3j33XdzaqI5P1kdy9PptCInafnU29uL2tpaKmP5okWLXJF7Rj7V1NTgvffew9DQUE5Ns3U+1dXVAVBeF3hpzq2ursbVV1/tyjlXfg0MYNbn3KmpKZiFT5TfFnJQwdjYGEpKSjA0NCRtwW9l5UwQBOzcuRPhcNjWU7zJyUmpKPfg4KCiOrrdp3iCIKCpqQmRSITKylk6nUZra6ui4rqdp3iEY2NjIwoLC6k8xctkMlkc7a6cEZ67du1CKBSisnIWi8XQ3t4u8bT7FE+dkzSe4sXjcWlH1lAoRG3lTBAENDc3w+fz2X6Kl0wmcfDgQVM5adYnOUeSU0aa5O163KenpxGNRiWeNFbO5J7LV7/y8SkWi2H+/PkAPhuHaKycyc8d+RNsI625fIrFYhAEAQ0NDSgoKKCycpbJZNDW1obGxkaJl54mI+6kfXp6WuJIiqDSWDlrb29HU1OTYodjOz4RfxoaGlBYWGioyYxPqVQK0WhUNyfz8SmRSEgcI5EIlZUzURSzxg09TbM155KcVHOc63OumZy06pMoilkcvTDnqnOSxsoZ7TlXfg08MjKC4uLiWZ1zx8bGUFpaitHR0ZzlufjKmYPQqh4eCAQ0C9fJJ23gs48DqdvlsXO156qO7vf7sz52ZNSu5k5+NqvJqJ2cYFo8zWjV407ey6wmo3Zy8mpxtOMTMDOo+Hw+U5pytRPeap52fSLvZcUPvXZ5LPnrNHzS4p6rXYsjueA3m5N2z6dc7bk0qXnS8smKVj3u8vfQuhhUw6pPdsYIeTs5PhQKSRzs+iT/eI4Wn3x9kvclzdzTel87PpELQXW73vG5uOvlZD4+kQsv8hCLtJvlqNVO/HbznGvEUUuTUTuLc26unLTik1FfemHOJT+7cc4168fnNefqva4FviEIBwcHBwcHBwcHBweHC8Bvzjg4ODg4ODg4ODg4OFwA/p0zB0C+c2bmc6VqJJNJtLa2orm52dISqBYmJyel73qMj49LP9uFfCcz9Xc93BLTCY40vSHwal+y4A8rut0eUz4OjYyMKL77agdu1+1UTBbOHYAN3azE5GObe3OIhXPHiZgscHTb3GPl3oCvnHHkhenpadfHdIKjE/BqX7LgDyu6WYlJG6zoZuF8dAIs6GYlJm2wopuFHHICXu1LL/sjB7858wjUO93YjXXo0CFXx3SCoxPwal+y4A8rulmJSRus6GbhfHQCLOhmJSZtsKKbhRxyAl7tSy/7owa/OePg4ODg4ODg4ODg4HAB+Fb6DoLUegGs1TkDPttC1k7NFXVNIPnvdmuuADPb34dCISp1zrQ42625Qo4x0mql5ooWRxp1zoCZzzCTzzHraVJzN9Ikj02j5grwWU7SqLmijkWrzhkBjZor5DgzOWmlzhmB3fpZck3y2LTqnAFQnO96muTtRucNiZtMJqnVOQOgyNFcWnP5ROIkk0lb9bPUNaW0uOTrk5yjGU1m65zJ/8+lyWydM8LTzlhOQI7Ry8l8fJJztDuWE2iNG3qaZmvOpT2WszLnmslJqz5pcfTCnKvOSTfOuer3mO05V/26EfjNGUW0tLSgpaVFSpZoNIqioiIA1iuRDwwMYPXq1Zaqq6srxsuro3d0dCASiQAwX12dQK9ifG9vL2pray1XV9fSRAoFCoJgqAmwVjG+s7MTO3bsMK3JyKeqqqosjkaazPoEzJy8sVjMlKZcPvX19Sl4Ws09PU3Dw8MoLy83rcnIp+HhYQVHq7mnp4nASu7p+UQGVrnfVnJPTxOZ0K3knpFPnZ2dCp5Wc09PUzAYxPDwMI4ePZpTk5FP1dXV0u9kHMpnjNDSRCZQuUdGmsz6JAhCXmOElqYNGzYgGAyis7MTExMTOTWZ9UkQBGpjeUVFBYCZMb2/vz+nJis+CYJAZSyfP38+gsEgBgYG0NPTk1OTFZ8EQaA2ljc0NCAQCChy0m1zbl1dHYLBYNZHs9w45/p8PupzriAIVMbysrIy1NXVSTHNaJprc25fXx/WrVvnyjlXfg0MzP6cOzU1BbPguzU6ALIjy9DQkLQji5WVM3lleDtP8dTV0efNmye9RuNpq92nQ/loMmp3WlMmk0Fra6uiej3rmuaST+TcaW5uhs/nmxOa5O2s+hSLxaQdswYHB7FgwQLmNc01n9LpNNrb29HU1KT4JAPLmuaST2Rsa2pqQiQSmROa1NxZ1SSKYtZ1Aeua5opP6mvg4uLiWdU0NjaG0tJSU7s18pUzB2GnYjxZ0le3y2Pnapf/HAgEsv4mV3V1NQj3TCaDoaEhlJaWWtJk1J7JZDAyMoLS0tKs9zajVc1dzTGXJjPt5OTV8jVfn+Q8tSraG3HXa/f5fJp9ma9P6r604odRuxZHGj7JuZtt1+JoNSfN+CTvS1qa/H6/IoeMOJptz2QyGBwcRGlpqSWtetzl7yGPZ8enTCaDCxcu6HJUH5+LO2lT96Vdn+R9qfW+Vn0KBAJZHPMdy7Xatd43H5/kuU5Wi+34lCsn8/FJPf7m0mSmXSumnqZc7Wru5Ge74x7NnHR6zr148aImz3x8MpuTVnySf7w43+s9dTsLc66aoxvnXLN+fF5zrpVSC3xDEI9A/V0Cu7G6urpcHdMJjk7Aq33Jgj+s6GYlJm2wopuF89EJsKCblZi0wYpuFnLICXi1L73sjxr85oyDg4ODg4ODg4ODg8MF4DdnHBwcHBwcHBwcHBwcLgC/OfMI5Fu+0ohVXFzs6phOcHQCXu1LFvxhRTcrMWmDFd0snI9OgAXdrMSkDVZ0s5BDTsCrfellf7Leg+/WSB9kt0YzO7KokUwm0draiubmZktfHtTC5OSktEvaxMSEYrdGDuug6Q0HfXB/3An5ODQyMoIFCxbMLiGOLPBzx93g/rgX3Bv3wm1zj5V7A75bo4NwUxHqeDyOgoIC6Xc7W6tmMhkMDAygsrKSahHqjz/+GMuXL8/aNSufrVUJx4qKChQUFLiyICYAiefKlSulOHqa1Nz12pPJJM6cOSP1pd3tYgnHFStWSPHN+GHkUyqVwunTpyWObixCnclk8Mknn5jKSbM+kb684oorpL410iRv1+OeSCTQ398v8aSxVXEmk8G5c+ewfPlyxXu6qQh1JpPB2bNnUVlZqXhPI61milAPDAxg+fLlCAQCVLaU9vl8GBgYwLJlyxRPWvP1KZFISBzJjmQ0ttKX/59LkxmfSK4vX75cmnvsbJMtiiLOnj2rm5P5+JROpyWOoVCIytbffr8fZ86cwbJly7J2crOzlT5Arwg1ycmlS5cqOLptzvX5fFnXBXbmXDM56YYi1CzMuerrQDfOuU4Voc53zuVFqGcJbi5CLQgC9SLUIyMjVItQ9/T0KIqL0iiI+dFHHzFRhHrp0qVIpVJUilB/8MEHOHXqlNSXtApiRiIRagUxBwcHFX67tQi1OidpFC4FgMrKSvT19VEpiPnqq69iYmJC4kmrCHUqlUIoFHJ1EWoAWLx4MfUi1D09PVSLUPf09ODUqVNUi1D39PRQG8udLELd09NDrQj1xMQE0uk09SLUPT09VItQv/vuu3j33XdzaprNItTd3d3SeZ5L02zOueoxmIZPPT09TBShZmHOnZ6eplqEmuac61QR6nznXF6EepbhxiLUpPgrgdsKYqbT6axCjnae4hGOjY2NKCwspPIUL5OhX4Sa8Ny1a5f09ElPk1mfYrEY2tvbJZ52n+Kpc5LGU7x4PI62tjaJoxuLUCeTSRw8eNBUTpr1Sc6R5JSRJnm7Hvfp6WlEo1GJJ42VM7nn8ifrbipCLT931J/9z9enWCwGQRDQ0NCAgoICKitnmUwGbW1taGxsVNTNyden6elpiSOprURj5Yx2EWriT0NDAwoLCw01mfEplUohGo3q5mQ+PiUSCYljJBKhsnImimLWuKGnabbmXJKTao5zfc41k5NuKELNwpyrzkkaK2e051ynilDnO+fyItQugZuKUGtxyVXATw2nCmICn32cRounncKl5L3MajJqJycvzSLUBD6fDz6fj0oRasJbzdOuT+S9aBTElMeSv07DJy3uudq1OMqLlJrJSbvnU672XJrUPGn55NYi1AR6543e8UY+yYuFEg52fSITdjAY1OSTr0/yvqSZe7SKUMt/plGEWt5Oqwg1ufAiD7FIu1mOWu1GRYndMucacdTSZNTO4pybKyet+OREEWr5++lxtNru1JxLfnbjnGvWj89rztV7XQt8t0aPgPaOOmVlZa6O6QRHJ+DVvmTBH1Z0sxKTNljRzcL56ARY0M1KTNpgRTcLOeQEvNqXXvYn6z34xxrpg+/WODfBd2VyN7g/7oTbdsziyAY/d9wN7o97wb1xL9w291i5N+ArZx6Behcuu7Hef/99V8d0gqMT8GpfsuAPK7pZiUkbrOhm4Xx0AizoZiUmbbCim4UccgJe7Usv+6MGvznzCNRbTduNdeLECVfHdIKjE/BqX7LgDyu6WYlJG6zoZuF8dAIs6GYlJm2wopuFHHICXu1LL/ujBr854+Dg4ODg4ODg4ODgcAH4zRkHBwcHBwcHBwcHB4cLwLfSdxCk1gtgrc4Z8NnHEO3UXJEfo66WbqfmSjqdRkVFhcSLRp0zv9+PiooKxfvaqblCOBJeNGquaPWr3TpnhCepl0Kjzpkoioq+tFtzhXDU0q+lKVc7+dK0nCOtOmcENGqu+Hw+0zlp1ifSl36/33b9LMI9k8koeNKoc5ZOp7FixYqsfs3HJznImEijzlk6nUZlZSV8Pp9prWZ8In1pp36Wep+tFStWSLWBjDQZcSftco5mNanbteqcyf/PpcmMT3KedsZygkwmY5iT+fgk52h3LJfzqayszOpLu3XOgBl/QqGQ7TkXmMlJNUe3zbla1wV2fDKTk1Z9IpC/rxfmXPV1oBvnXPV7zPacq37dCPzmjCJaWlrQ0tIiGRaNRlFUVATAeiXygYEBrF692lJ1dXXFeHl19JdffhmRSASA+erqBHoV4/1+f17V1fU0nTt3Dv39/YaaAGsV40dHRy1pMvKpqqoKACAIgmlNZnwCgI0bN2J8fNyUplw+ffjhh+jv75f60mru6WmqrKy0pMnIp5GREQVHq7mnp4nAau7paZJzzKXJik+BQADvv/++6dwz8ukvf/kLxsfHJZ5Wc89I0+DgoK0xYsWKFaiurpZ+7+joQCQSyXuM0NJEisCa1WTGp/7+/rzGCD1NmzdvRkdHh60xQq2pv7+f2lhOLgR7e3sV+U7Dp/7+fipjOdH0ySef2B7L1Zr6+/upjeXNzc248sor0d7eblqTWZ96e3tRW1tLZc7dvHkzDhw4YHuMcHLOveqqqxCPxxV9ScOn/v5+amN5XV0dAOV1gZfm3Hnz5lEby2nPufJrYABUxnI7c+7U1BTMgm+l7wDIdplDQ0PSdplWVs7kleHtPMWTV0eXcwHsr5z19vZiw4YNKCgooLJyJooiuru7sW7duqyihvk+xevt7cX69esRDoepPMXLZDJobW1VVK+nsXLW29uLL3zhC1Kf6Wky61MikUBPT4/UlzSe4vX29mLjxo0IhUJUnuIlk0kcO3ZM4khr5UwQBDQ3N0srkXqazPiUTqfx7rvvmspJKytnvb292LRpEwBQeYoXj8dx/PhxiSetlbO//vWvWL9+veLYfHyKxWLSdsaDg4NYsGABtZWz9957Dxs3bsxaFcjXp0Qigd7eXqxbtw7BYJDaytnx48exdu1axUpivj7F43GJIykaTWPlrL29HU1NTYpirHZXzgjPcDhsqMnsytl7772nm5P5+JRKpSSOBQUF1FbOjh07hrVr1yr60u7KmSAIaGpqQiQSobJydvz4cVx99dUKjm6bcwFkXRfYXTnLlZNWfSIPh+TXBV6Yc9XXgTRWzmjPufJr4JGRERQXF8/qnDs2NobS0lJTW+nzlTMHYadiPBmYrFRXV7fLfyaTuPo9jKqrqyHn3t/fj40bN1rSZNSeTCalmGYqw+u1y7nLOZrRlKudnLxavtrxifD0+Xw5NZlp9/l8mn1pxyd5X1rxw6hdiyMNn9TczbRrcRRF0VJOmvVJHpOGJr/fr8nTrk+nT5/G+vXrLWnV4y5/D3k8uz6dOXMGGzZsyHuMULcHAgGpLwkHu7mXTCYN+9KqT3KO8ovBfMdydbvW++brk3xsM9Jkxqdc/ZiPT/JznMacS3jq5aVdn+Q3KHbGPZo56eSca3RdkK9PZnLSik/kgtzO9Z5WOwtzrpyjG+dcs358XnOulTp4fEMQDg4ODg4ODg4ODg4OF4DfnHkEAwMDs02Bg4ODg4ODg4ODg8MA/OZsDuPZZ5+Vft64cSOefvppKnH9fj9qamo0l5PdEtMJjk7Aq33Jgj+s6GYlJm2wopuF89EJsKCblZi0wYpuFnLICXi1L73sT9Z7OBbZAp588kls3LgRl112GS677DLU19fj4MGDAGY+z/sP//AP2LBhA+bNm4fy8nL87d/+LT799FNFjFtuuQU+n0/x784771QcMzIygr1796KkpAQlJSXYu3cvLl26pDjm9OnTuP322zFv3jyUlpbi/vvvRyKRcFS/E+jv78d3vvMd6fdMJoNvfOMbil1w8kUgEMBVV11l+N2S2Y7pBEcn4NW+ZMEfVnSzEpM2WNHNwvnoBFjQzUpM2mBFNws55AS82pde9kcNV9ycVVRU4Mc//jHefPNNvPnmm9i+fTu+9KUvobe3F1NTU3j77bfxT//0T3j77bfxH//xH/jggw9wxx13ZMXZt28fzp49K/176qmnFK/v2bMH3d3daGtrQ1tbG7q7u7F3717p9XQ6jd27d2NychKvvfYa9u/fjxdffBEPPPCA431AGydPnlTsPgPM6Dt16pTt2KlUCp2dnVm757gpphMcnYBX+5IFf1jRzUpM2mBFNwvnoxNgQTcrMWmDFd0s5JAT8GpfetkfNVyxW+Ptt9+u+P2RRx7Bk08+iddffx333nuvon4EADz++OO49tprcfr0aakoJQAUFRVh6dKlmu/x17/+FW1tbXj99ddx3XXXAQB+9atfob6+HidOnEBNTQ2i0Sjee+89nDlzBuXl5QCAn/3sZ/j617+ORx55JOfWl27CmjVr4Pf7FTdogUAAV155pe3YoijiwoULWVvzuimmExydgFf7kgV/WNHNSkzaYEU3C+ejE2BBNysxaYMV3SzkkBPwal962R81XHFzJkc6ncbvf/97TE5Oor6+XvOY0dFR+Hw+LFiwQNH+/PPP47nnnsOSJUuwa9cu/OAHP0BxcTEAoKurCyUlJdKNGQBcf/31KCkpQWdnJ2pqatDV1YX169dLN2YA0NTUhHg8jrfeegvbtm3T5BOPxxGPx6Xfx8bGAMx8JNNKRXDyN/L/88WSJUvwy1/+Evfffz+AmRuzf/3Xf8WSJUtsx6bF0cmYLHBkJSYLHJ2IyQJHFmLKY+QzJuaK61bdTsVkgaMTMVngyEpMFjg6EZMFjqzEZIkj+Xm2tVs53nQR6vHxcelGRw9HjhzBtddea/rN5ejp6UF9fb1UsPSFF15Ac3Nz1nGxWAxf/OIXcdVVV+G5556T2n/1q19h1apVWLp0KY4fP46HHnoIV155pbTq9uijj+KZZ57BBx98oIhXXV2Ne+65Bw899BDuu+8+fPzxx4hGo4pjwuEwnnnmGXzta1/T5P7www/jhz/8YVb7Cy+8gKKiIst9QQuxWEz63l1LSwuWL18+a1w4ODi8Cfk4tH//fkQikVlmxMHBwcEx1+G2uWdqagp79uyhW4S6oaEBgiBo3qClUin8n//zf/Czn/1MsYJkBTU1Neju7salS5fw4osv4u6778bhw4exdu1a6ZhkMok777wTmUwG//qv/6r4+3379kk/r1+/HmvWrME111yDt99+G1u2bAEAqeigHKIoKtrNHKPGQw89hO9973vS72NjY6isrERjY6Plj0Imk0kIgqCoNp8vJicnpZ/vvPPOnDfXZpHJZDAwMIDly5dT262GdkwnONL0hsCrfcmCP6zodntM+Ti0ffv2rE885Au363YqJgvnDsCGblZi8rHNvTnEwrnjREwWOLpt7iGfqjMD0zdnU1NTuPXWWxGNRlFSUiK1Hzt2DHv37sXFixfxX//1X6bfWI2CggLp+1DXXHMNjh49iscee0za1COZTOKrX/0qPvroI3R0dOS86dmyZQtCoRBOnjyJLVu2YOnSpTh//nzWcRcuXMCSJUsAAEuXLsUbb7yheH1kZATJZFI6RgvhcBjhcDirXativFnY+Vt5DIJwOExt4ACA1atXU4vlVEwnOAJ0vJHDq33Jgj+s6HZzTLkXXjx3nIjJwrkDsKGblZgAH9vcGI/A7eeOEzHdztFtc4+V9zd9y9fR0YFYLIZbb70Vo6OjyGQyeOSRR1BXV4cNGzagp6cHTU1NlsnqQRRFaRWO3JidPHkSL7/8Mi6//PKcf9/b24tkMolly5YBAOrr6zE6OoojR45Ix7zxxhsYHR3F1q1bpWOOHz+Os2fPSsdEo1GEw2HU1tZS0zYboL2jTkdHh6tjOsHRCXi1L1nwhxXdrMSkDVZ0s3A+OgEWdLMSkzZY0c1CDjkBr/all/1Rw/TKWWlpKTo6OrBjxw5s27YNBQUF+PDDD/Hb3/4WX/7yl22R+P73v49du3ahsrIS4+Pj2L9/P/785z+jra0NqVQK//N//k+8/fbbeOmll5BOp3Hu3DkAwKJFi1BQUIC+vj48//zzaG5uRmlpKd577z088MAD2Lx5M2644QYAwNVXX42dO3di37590mrcfffdh9tuuw01NTUAgMbGRqxduxZ79+7FT37yE1y8eBEPPvgg9u3bx9ROjVqgvaPO+Pi4q2M6wdEJeLUvWfCHFd2sxKQNVnSzcD46ARZ0sxKTNljRzUIOOQGv9qWX/VHD0m6Nl19+Of70pz/h1ltvRXd3N7q7u3HVVVfZJnH+/Hns3bsXZ8+eRUlJCTZu3Ii2tjY0NDTg448/lj4uuWnTJsXfHTp0CLfccgsKCgrwpz/9CY899hgmJiZQWVmJ3bt34wc/+IGiSNzzzz+P+++/H42NjQCAO+64A0888YT0eiAQwIEDB/DNb34TN9xwAwoLC7Fnzx789Kc/ta2Rg4ODg4ODg4ODg4PDCJa30r/88svR0dGBW2+9FXv27MGf/vQnLFy40BaJp59+Wve1K664IufdaWVlJQ4fPpzzfRYtWqTY4VELK1aswEsvvZQzFgcHBwcHBwcHBwcHB02YvjlTf3SxuLgYr7zyCq699lps2LBBav+P//gPeuw4qEG+gkgjVn19vatjOsHRCXi1L1nwhxXdrMSkDVZ0s3A+OgEWdLMSkzZY0c1CDjkBr/all/1Rw/TNmXyHRvL7qlWrqBOaS5AXvfP7/QgEAkin08hkMtIxpD2VSkEURel4cgxpJwgEAvD7/VnF7ILBoHS8/P0J0um04vdQKIRMJoN0Oi21+Xw+BINB3XY594ULF0q8cmlSc9drX7hwIdLptPTeWpqM2tWaFi5ciEwmA7/fb0qTnLtWu1a/5tJkxqeFCxfC5/NBFMWcmuTc9dpFUVT0pdncM9IkXx03o8monexYJOdoJffk3OXtcl5Wc09Lk8/nM52TVnxatGgR/H6/pdwz8imTySh4Wsk9o/bFixcjk8ko4uTjkxxkTMx3jFBrKisrk+Ka0WTGJ9KXmUzG8hihp2nx4sVIpVKK983XJznHdDpteywnMeX/m9FkxifCk5SiyXcsJ9yNcjJfnwhH8pqdsZy0l5WVZfltxyfy3sRvGnPu4sWLkUwmFe/rtjk3EAhg0aJFijHY7pybKyet+kQgf1+vzLny60A3zrnq95jtOddKEWrTN2f/9m//ZjqoV9HS0oKWlhYpWaLRqFSEesWKFdi8eTOOHTuG06dPS39TU1ODq666CkeOHMGFCxek9oGBAaxevRqvvPIKxsfHpfb6+nosXrwY0WhUkazbtm1DYWEhWltbpbZYLCb93N7eLhXgCwaD2L17N4aGhtDV1SUdU1xcjO3bt+PMmTPo7u6W2svKyrB161acPHkSJ06ckNorKipQW1trWtOmTZuwcuVKTU0LFy5UcNfTBADNzc2Ynp7GoUOHpDY9TfPnz8eOHTtMazLyqaqqCgCkwua5NJn1CZipI5hKpUxpyuXT+++/j1OnTpnSZMWnuro6lJeXm9Zk5NO5c+dw9OhR05rM+kRgJff0fAoGgwqvc2ky65PP58OuXbvQ19dnOveMfDp8+DAmJiZMabLiEwBs3rzZtk/V1dXS7x0dHYhEInmNEVqaAoEAtm/f7ohP+YwRWpo2bNiAv/71r4hEIo74RGssB2Z2Oe7v78+pida4Z3Usj8ViuPrqq9HT05NTE61xL5+xvKOjI+umh4ZPvb29VObcuro6vPPOOwDgiE8059yDBw8qLpBnI/eMfKqrqwOgvC7w0px75ZVXYt26da6cc+XXwACojRH5zrlTU1MwC5/o9m1RGMTY2BhKSkowNDQk7fJoZeVMEATs3LkT4XDY1lO8yclJ6enL4OCgogCf3ad4giCgqakJkUiEylO8dDqN1tZWRSFHO0/xCMfGxkYUFhZSeYqXyWSyONp9ikd47tq1C6FQiMrKWSwWQ3t7u8TT7lM8dU7SeIoXj8elTX9CoRC1lTNBENDc3CytROppMuNTMpnEwYMHTeWkWZ/kHElOGWmSt+txn56eRjQalXjSWDmTey5f/crHp1gshvnz5wP4bByisXImP3fkT7CNtObyKRaLSQVlCwoKqKycZTIZtLW1obGxUeKlp8mIO2mfnp5WFL2ltXLW3t6OpqYmxUd17PgkL85bWFhoqMmMT6lUCtFoVDcn8/EpkUhIHCORCJWVM1EUs8YNPU2zNeeSnFRznOtzrpmctOqTKIpZHL0w56pzksbKGe05V34NPDIyguLi4lmdc8fGxlBaWorR0dGcO8Bb3hCEwzy0it4FAgHNz6nKJ23gs48DqdvlsXO15yrA5/f7Naub67WruZOfzWoyaicnmBZPM1r1uJP3MqvJqJ2cvFoc7fgEzAwqPp/PlKZc7YS3mqddn8h7WfFDr10eS/46DZ+0uOdq1+JILvjN5qTd8ylXey5Nap60fLKiVY+7/D20LgbVsOqTnTFC3k6OD4VCEge7PpEJOxgMavLJ1yd5X9LMPa33teMTuRBUt+sdn4u7Xk7m4xO58CIPsUi7WY5a7cRvN8+5Rhy1NBm1szjn5spJKz4Z9aUX5lzysxvnXLN+fF5zriNFqDk4ODg4ODg4ODg4ODicA/9YowMgH2s0s3SpRjKZRGtrK5qbmy3dZWthcnJS+jjR+Pi49LNdkAJ8xcXFWR8ncktMJzjS9IbAq33Jgj+s6HZ7TPk4NDIyovh4tR24XbdTMVk4dwA2dLMSk49t7s0hFs4dJ2KywNFtc4+VewO+csaRF8jntd0c0wmOTsCrfcmCP6zoZiUmbbCim4Xz0QmwoJuVmLTBim4WcsgJeLUvveyPHPzmzCNQf5nSbqzW1lZXx3SCoxPwal+y4A8rulmJSRus6GbhfHQCLOhmJSZtsKKbhRxyAl7tSy/7owbVmzO/34/t27fjrbfeohmWg4ODg2MOYGBgYLYpcHBwcHBwuBpUb85+85vf4Oabb8b9999PMywHBwcHB6N49tlnpZ83btyIp59+ehbZcHBwcHBwuBtUt9L/+te/DgD4wQ9+QDMssyC1XgBrdc6Az7aQtVNzRV0TSP673ZorwMz296FQiEqdMy3OdmuukGOMtFqpuaLFkUadM2DmC6aiKFKpcyavqZVLk9maK8BnOUmj5oo6Fq06ZwQ0aq6Q48zkpJU6ZwR262fJNclj06pzBkBxvutpkreruX/66af4zne+I/2eyWTwjW98A7feeitWrlxpu84ZAEWO5tKayycSJ5lM2qqfpa4ppcUlX5/kHM1oMlvnTP5/Lk1m65wRnnbGcgJyjF5O5uOTnKPdsZxAa9zQ0zRbcy7tsZyVOddMTlr1SYujF+ZcdU66cc5Vv8dsz7nq141g++bs4sWL+PWvf41QKIT//b//t91wTKOlpQUtLS1SskSjURQVFQGwXol8YGAAq1evtlRdXV0xXl4dvaOjA5FIBID56uoEehXj//rXv+ZVXV1LU1lZWVZ1eC1NgLWK8V1dXZY0GflUVVUFAAqORprM+kQwPj5uSlMunz766CMFT6u5p6fp0qVLWLJkiWlNRj5dunRJwdFq7ulpIrCSe3o+kfNF7reV3NPTVFpaimAwiBMnTpjOPSOfyHsSnlZzT09Tc3MzLl68iNdffz2nJj2fzp8/r5j0gJkJ8vDhw/jbv/1b2z7deOONAGBrjNDSJAhCXmOElk9f+MIX0NzcjFdffdXWGKHWJAgCtbG8oqICANDb24v+/v6cmqz4JAgClbG8uLgYzc3NGBgYwLvvvptTkxWfBEGgNpbv2rULN954o2LcoOVTb28vamtrbY/l119/PZqbm6mM5U7OuTU1NSgtLaU+5wqCQGUsLysrQ11dnRTTjKa5Nud+9NFHuPrqq10558qvgQHYHsvtzrlTU1MwC9tb6W/fvh1f+cpX8K//+q/o7e3F8ePH8dvf/haPPPKInbBMg2yXOTQ0JG2XaWXlTF4Z3s5TPHl19IsXLyq20rfzFE8URUxMTOCyyy4zfMJi5UmKz+fDyMgI5s+fLz2NsvMUj3AsLi6m9hQvk8mgtbVVUb3e7tNWwnPBggXw+XxUVs5SqRTGxsakvrT7FI9wLCkpQSAQoPIUL51OY3R0VOJIa+WMTLo+n4/KU7xLly6ZykmzPomiiMnJSSxYsACZTIbKU7xkMimVyvD5fFRWzkRRRCwWw7x58xRc8lk5W716taItEAigr6/P9sqZKIqYnp5GcXFx1opPvj6lUilMTExg/vz58Pv9VFbO/H4/Jicns3b3srNyRjiSAtw0Vs7a29vR1NSk+CSDnZUzMm7Mnz9fGi/trJwBwPT0tG5O5uNTJpOROAaDQSorZ4FAAOPj4ygsLFSsrNhdORMEAU1NTYhEIrbnXJKTkUhEwdFtc67f78elS5cwb948iaedOddMTlr1SRTFrOsCL8y56utAWitnNOdc+TXwyMgIiouLZ3XOHRsbQ2lpqbkyW6JNXHPNNaIoiuKmTZukNvnPXsTo6KgIQBwdHbX8t4lEQvzjH/8oJhIJ2zwmJiZEACIAcWRkxHY8ApocnYrJAkdWYrLA0YmYLHBkIWZLS4s0DgUCAfHXv/41BYbu1+1UTBY4OhGTBY6sxGSBoxMxWeDISkwWOLrtGtjKvYHtDUEWL16MTz/9VPH0Rb2UyMHBwcHhTdx9993Sz++++y7uvffeWWTDwcHBwcHhbpi+OWtvb8/67gAA/OIXv8Ddd9+NwcFB/O53v8M999yDq666iipJDg4ODg72sXz58tmmwMHBwcHB4WqYvjlrbm7G0NBQVnt1dTUOHDiAn//85zh+/DiuueYaPP/881RJcrgP5LO0bo7pBEcn4NW+ZMEfVnSzEpM2WNHNwvnoBFjQzUpM2mBFNws55AS82pde9kcO0xuC+P1+nDt3DosXL3aU0FwA2RDE1Jf+VEgmk2htbUVzc7P05dJ8MTk5KW0CMjExgXnz5tmK53XQ9IaDPrg/7oR8HBoZGcGCBQtmlxBHFvi5425wf9wL7o174ba5x8q9AdUi1BzuhdZHUu3EGhwcdHVMJzg6Aa/2JQv+sKKblZi0wYpuFs5HJ8CCblZi0gYrulnIISfg1b70sj9qWLo5e+KJJ9De3q758UYOd0O9JbHdWF1dXa6O6QRHJ+DVvmTBH1Z0sxKTNljRzcL56ARY0M1KTNpgRTcLOeQEvNqXXvZHDUsfmnzyySfxz//8z/D5fFi+fDm2bNmC2tpabNmyBVu2bMGyZcuc4skkksmkVPfASp0z4LOVLjs1V+THyLkA9muuAJ9VhqdR50yLs52aKyROKpWiVnNFi6PdOmfkdfH/1TahUeeMtFnNPT1N6pykUXNFHYtWnTMCWjVX1HHt1jmTx7JbP0uuSR6bRp0zuefyOPn4JAcZh+zUz1JzlOdoLq25fCJxksmkrfpZcu7ysVwOO3XO5P/TqnMm/z+XJjM+yXnaGcsJyDF6OZmPT3KOdsdyAq1xQ0/TbM25tMdyVuZcMzlp1Sctjl6Yc9U56cY5V/0esz3nql83gqWbs97eXqRSKbzzzjt4++238fbbb+PXv/41zpw5A5/PhyVLluDTTz+1EnJOoaWlBS0tLVKyRKNRFBUVAbBeiXxgYACrV6+2VF1dXTFeXtKgo6NDqr5utro6gV7F+N7eXtTW1lqurq6liRQKlFeG19IEWKsY39nZiR07dpjWZORTVVVVFkcjTWZ9AmZO3lgsZkpTLp/6+voUPK3mnp6m4eFhlJeXm9Zk5NPw8LCCo9Xc09NEYCX39HwiA6vcbyu5p6eJwEruGfnU2dmp4Gk19/Q0ATOeHz16NKcmI5+qq6ul38k4lM8YoaUJmDl35B4ZaTLrkyAIeY0RWpo2bNgAYGYsmpiYyKnJrE+CIFAbyysqKgDMjOn9/f05NVnxSRAEKmM5+e7IwMAAenp6cmqy4pMgCNTG8oaGBilmLk2zNefW1dUBAA4dOmR7LGdxzhUEgcpYXlZWJvWlnKOX5ty+vj6sW7fOlXOuuqzXbM+5U1NTMAvTG4IEAgGcPXtWc0OQixcv4s0330R3dzf+/u//3vSbz1WQL/0NDQ1JX/qzsnImCAJ27tyJcDhs6ymevDr6hQsXUFJSIr1m5yleKpVCZ2cnvvjFLyIcDlNZOctkMjh8+DC2bt0qabHzFI9wvOGGGxCJRKg8xctkMmhtbUVDQ4P0xV+7T1sJz5tuugnBYJDKylk8Hsdrr70m9aXdp3iE44033oiCggIqT/ESiQReffVViSOtlTMy6fp8PttP8VKpFF555RVTOWnWJ9KXN998M3w+H5WneLFYDH/5y18knjRWzlKpFLq6uvDFL35R8XQ4H59isZh0YT04OIgFCxZQWTmTnztq5OtTPB5HZ2cntm7dilAoRGXlTBRF6XyUf0ogX59isZjEMRgMUls5a29vR1NTk4KjHZ+IP1u3bpUeDNpZOUun09K8o5WT+a6cEY7hcJjKyhmArHFDT5OVlTNBENDU1IRIJGJ7ziU5WV9fr+DotjlXFMWs6wI7c66ZnLTqkyiKWdcFXphz1deBNFbOaM+58mvgkZERFBcXz+qcOzY2htLSUlMbgvDdGh0A361xboLvyuRucH/cCbftmMWRDX7uuBvcH/eCe+NeuG3ucWS3xoMHDypWXjjYAu0ddT755BNXx3SCoxPwal+y4A8rulmJSRus6GbhfHQCLOhmJSZtsKKbhRxyAl7tSy/7o4bpm7OmpiaEw2HHiHA4C9o76nR3d7s6phMcnYBX+5IFf1jRzUpM2mBFNwvnoxNgQTcrMWmDFd0s5JAT8GpfetkfNXidMw4ODg4ODg4ODg4ODheA35xxcHBwcHBwcHBwcMxJDAwMzDYFS+A3Zx6BfHcrGrHKyspcHdMJjk7Aq33Jgj+s6GYlJm2wopuF89EJsKCblZi0wYpuFnLICXi1L2nHfPbZZ6WfN27ciKeffppK3M8jj0zv1shhHm7cSn9kZESxWyON7Zftbhebjyajdqc1ObGV/mxrmks+0d5K3w2a5O2s+uTUVvqzqWmu+eTEVvqzrWku+UR7K303aFJzZ1WTE1vpz7amueDTJ598giuvvFLBNRAI4OTJk1Jdx89bk5Wt9C0VoeYwhpuLULe3t1MvQl1ZWYktW7ZQKUJ9+eWXo62tTXGC0Cpc6vaCmMDMhjuJRIJKEeoTJ07g5MmTpjRZ8enaa6/FsmXLqBTEPH/+PI4cOWJa02wUoS4oKEB7e7tpTWZ9Kioqwvbt26kVxDx8+DDV4sZEU1VVFUpKSmz75GQR6iuuuAI1NTWO+ESrCPXGjRsRj8cxMDDgiE9uL0JNS9P8+fOxfPlyhMNhHDt2LKcmWuNePmP5iRMn8PHHH+fUNFtFqK+99lqMjo6ir6/PEZ9ozblr1qzBoUOHFMV7ZyP3ZqsINQtz7po1a7B27VrXzbnPP/981m6K6XQazz//PDZs2GCoyak515Ei1Bzm4caVM/LEmsBtT/HS6XTW0yc7T1IIx8bGRhQWFrp25Yzw3LVrF0KhEJUnXrFYDO3t7RJPu0+H1DlJ44lXPB5HW1ubxNGNK2fJZBIHDx40lZNmfZJzJDllpEnersd9enoa0WhU4knjyaTcc7//s0+/u2nlTH7uqD9ekq9PsVgMgiCgoaEBBQUFVJ4gZzIZtLW1obGxUVHwN1+fpqenJY6hUMi1K2fEn4aGBhQWFhpqMuNTKpVCNBrVzcl8fEokEhLHSCRC5Um/KIpZ44aeptmac0lOqjnO9TnXTE66YeWMhTlXnZM0VplozrlOrpzlO+fylTOXgEyccgQCAcXkRyCftAFIk4+6XR47V7v6Z/Xf+P1+xSSXq13NnfxsVpNROznBtHia0arHnbyXWU1G7eTk1eJoxydgZlDx+XymNOVqJ7zVPO36RN7Lih967fJY8tdp+KTFPVe7FkdywW82J+2eT7nac2lS86TlkxWtetzl76F1MaiGVZ/sjBHydnJ8KBSSONj1iUzYwWBQk0++Psn7kmbuab2vHZ/IhaC6Xe/4XNz1cjIfn8iFF3mIRdrNctRqJ367ec414qilyaidxTk3V05a8cmoL70w55Kf3Tbnrlq1Co8//ji+9a1vSTyfeuoprFq1KqemXNzznXOtFCnnG4JwcHBwcHBwcHBwcMwZ3H333dLP7777Lu69995ZZGMN/ObMI9B6umAn1ooVK1wd0wmOTsCrfcmCP6zoZiUmbbCim4Xz0QmwoJuVmLTBim4WcsgJeLUvnfRn+fLl1GJ9HnnEv3PmAMh3zsx8rlSNZDKJ1tZWNDc3W1oC1cLk5KT0XY+JiQnFbo0c1kHTGw764P64E/JxaGRkRPHdVw53gJ877gb3x73g3rgXbpt7rNwbuOLxwZNPPomNGzfisssuw2WXXYb6+nocPHhQel0URTz88MMoLy9HYWEhbrnlFvT29ipixONxfOc730FpaSnmzZuHO+64Q7HrFDBjzt69e1FSUoKSkhLs3bsXly5dUhxz+vRp3H777Zg3bx5KS0tx//33I5FIOKb984L8S5M0Yr3zzjuujukERyfg1b5kwR9WdLMSkzZY0c3C+egEWNDNSkzaYEU3CznkBLzal172Rw1X3JxVVFTgxz/+Md588028+eab2L59O770pS9JN2D/8i//gp///Od44okncPToUSxduhQNDQ2K7Tm/+93v4g9/+AP279+P1157DRMTE7jtttsUnbdnzx50d3ejra0NbW1t6O7uxt69e6XX0+k0du/ejcnJSbz22mvYv38/XnzxRTzwwAOfX2c4BPWWonZjnT592tUxneDoBLzalyz4w4puVmLSBiu6WTgfnQALulmJSRus6GYhh5yAV/vSy/6o4YrdGm+//XbF74888giefPJJvP7661i7di1++ctf4h//8R/x5S9/GcBM1e8lS5bghRdewDe+8Q2Mjo7i6aefxr//+7/j1ltvBQA899xzqKysxMsvv4ympib89a9/RVtbG15//XVcd911AIBf/epXqK+vx4kTJ1BTU4NoNIr33nsPZ86cQXl5OQDgZz/7Gb7+9a/jkUcesfwRRQ4ODg4ODg4ODg4ODrNwxc2ZHOl0Gr///e8xOTmJ+vp6fPTRRzh37hwaGxulY8LhMG6++WZ0dnbiG9/4Bt566y0kk0nFMeXl5Vi/fj06OzvR1NSErq4ulJSUSDdmAHD99dejpKQEnZ2dqKmpQVdXF9avXy/dmAEzBSXj8TjeeustbNu2TZNzPB5HPB6Xfh8bGwMAqdaLFZDjrf6dUax8ueSKSyueEzFZ4MhKTBY4OhGTBY4sxPTyOORETBY4OhGTBY6sxGSBoxMxWeDISkyWOJKfZ1u7leNdsyFIT08P6uvrpYKlL7zwApqbm9HZ2YkbbrgBAwMDipum++67D5988gna29vxwgsv4J577lHcIAFAY2MjVq1ahaeeegqPPvoonnnmGXzwwQeKY6qrq3HPPffgoYcewn333YePP/4Y0WhUcUw4HMYzzzyDr33ta5rcH374Yfzwhz/Man/hhRdQVFSUb5fYRiwWw5133gkA2L9/PyKRyKxx4eDg8Cb4OMTBwcHB8XnDbXPP1NQU9uzZw1YR6pqaGnR3d+PSpUt48cUXcffdd+Pw4cPS66Q4HYEoilltaqiP0To+n2PUeOihh/C9731P+n1sbAyVlZVobGzMa7dGUsGexm6NBE1NTXy3Rpug6Q0HfXB/3An5OLR9+/ZZ3zGLIxv83HE3uD/uBffGvXDb3EM+VWcGrrk5KygowJVXXgkAuOaaa3D06FE89thj+Id/+AcAwLlz57Bs2TLp+MHBQSxZsgQAsHTpUiQSCYyMjGDhwoWKY7Zu3Sodc/78+az3vXDhgiLOG2+8oXh9ZGQEyWRSOkYL4XAY4XA4q12ryrlZ2PlbeQwCn89HbeBIpVI4cuQIrr32Wt0K6rMd0wmOBDS8IfBqX7LgDyu63R5T7oXXzh0nYrJw7gBs6GYlJgEf29wVTw43nztOxGSBo9vmHivv74rdGrUgiiLi8ThWrVqFpUuXQhAE6bVEIoHDhw9LN161tbUIhUKKY86ePYvjx49Lx9TX12N0dBRHjhyRjnnjjTcwOjqqOOb48eM4e/asdEw0GkU4HEZtba2jep0GzU+viqKICxcuuDqmExydgFf7kgV/WNHNSkzaYEU3C+ejE2BBNysxaYMV3SzkkBPwal962R81XLFy9v3vfx+7du1CZWUlxsfHsX//fvz5z39GW1sbfD4fvvvd7+LRRx/FmjVrsGbNGjz66KMoKirCnj17AAAlJSW499578cADD+Dyyy/HokWL8OCDD2LDhg3S7o1XX301du7ciX379uGpp54CMPO9tdtuuw01NTUAZr6jtnbtWuzduxc/+clPcPHiRTz44IPYt28f36mRg4ODg4ODg4ODg8NRuOLm7Pz589i7dy/Onj2LkpISbNy4EW1tbWhoaAAA/P3f/z2mp6fxzW9+EyMjI7juuusQjUZRXFwsxfjFL36BYDCIr371q5iensaOHTvwzDPPIBAISMc8//zzuP/++6VdHe+44w488cQT0uuBQAAHDhzAN7/5Tdxwww0oLCzEnj178NOf/vRz6gkODg4ODg4ODg4ODq/CFTdnTz/9tOHrPp8PDz/8MB5++GHdYyKRCB5//HE8/vjjuscsWrQIzz33nOF7rVixAi+99JLhMSxCfpNKI9amTZtcHdMJjk7Aq33Jgj+s6GYlJm2wopuF89EJsKCblZi0wYpuFnLICXi1L73sjxquuDnjcB5+P72vF/r9fqxcuZJaPCdiOsHRCXi1L1nwhxXdrMSkDVZ0s3A+OgEWdLMSkzZY0c1CDjkBr/all/3Jeg9Ho3O4BqlUimqsjo4OV8d0gqMT8GpfsuAPK7pZiUkbrOhm4Xx0AizoZiUmbbCim4UccgJe7Usv+6MGXzlzEPKK5H6/H4FAAOl0GplMRjqGtKdSKYiiKB1PjiHtBIFAAH6/P6vSONnOU54s8mMSiYTi91AohEwmg3Q6LbX5fD4Eg0HddsI9mUxifHwcqVRK0a6nSc1dq10URYyPjyORSEivaWkyapdrIhyTyaQpTWruWu1a/WqkyYxPhGcmk4Eoioaa1Nz12lOplKIvzeaenibCMZ1OIxgMmso9o/ZQKIR0Oq3gaDb31Nzl7XJeVnJPz6dMJmM6J836RPpSFEVLuWfkE4lJeFoZI/Ta5Z7L3zMfn+QgY2I+Y4Rak/zcMas1l0+JRELqSxLHrk8kj5LJZN5jubxdzlEURVtjOQE5Rn6skSYzPsnzknz8J5+xnICMa3o5mY9P8r70+/22xnICrblMT5OVOReY8YeMn3bmXK2xzUjTbM25Wn1pZ841k5NWfSKQv68X5lz1daAb51z1e8z2nKt+3Qj85owiWlpa0NLSIiVLNBpFUVERgJnvsm3evBnHjh3D6dOnpb+pqanBVVddhSNHjuDChQtS+8DAAFavXo1XXnkF4+PjUnt9fT0WL16MaDSqSNZt27ahsLAQra2tUlssFpN+7ujokKqjB4NB7N69G0NDQ+jq6pKOKS4uxvbt23HmzBl0d3dL7WVlZdi6dStOnjyJEydOSO29vb2ora01rWnTpk1YuXKlpiZSn05eDkFLEwA0Nzdjenoahw4dktr0NHV2dmLHjh2mNRn5VFVVlcXRSJNZn4CZkzcWi5nSlMunvr4+BU+ruaenaXh4GOXl5aY1Gfk0PDys4Gg19/Q0EVjJPT2fyMAq99tK7ulpIrCSe0Y+dXZ2KnhazT09TcCM50ePHs2pycin6upq6XcyDuUzRmhpAmbOHblHRprM+iQIQl5jhJamDRs2AJgZiyYmJnJqMuuTIAjUxvKKigoAM2N6f39/Tk1WfBIEgcpYPn/+fAAzc2NPT09OTVZ8EgSB2lhONjKT56Tb5ty6ujoAwKFDh2yP5SzOuYIgUBnLy8rKpL6Uc/TSnNvX14d169a5cs6VXwMDsz/nTk1NwSx8otsLCjCIsbExlJSUYGhoSNqC38rKmSAI2LlzJ8LhsK2neJOTk9JNz+DgoKI6ut2neIIgoKmpCZFIhMrKWTqdRmtrKxoaGqRCfXaf4gmCgMbGRhQWFlJ5ipfJZLI40lg5EwQBu3btQigUorJyFovF0N7eLvGk8RRPnpM0nuLF43FpR9ZQKERt5YxMuj6fz/ZTvGQyiYMHD5rKSSsrZ4QjySkjTfJ2Pe7T09OIRqMST1orZ8Rz+epXPj7FYjHpwpqMQ7RWzsi5I3+CbaQ1l0+xWAyCIKChoQEFBQXUVs7a2trQ2NioKFiar0/T09MSR1JYlcbKWXt7O5qamhRfcre7ckZ4FhYWGmoyu3IWjUZ1czLflTPCMRKJUFs5U48beppma84lOanmONfnXDM5adUnURSzOHphzlXnJI2VM9pzrvwaeGRkBMXFxbM6546NjaG0tBSjo6M5y3PxmzMHQG7OzBigRjKZRGtrK5qbm21XM5+cnJQuisbGxhSlB+wgk8lgaGgIpaWlWR9bcktMJzjS9IbAq33Jgj+s6HZ7TPk4NDIyonhIZAdu1+1UTBbOHYAN3azE5GObe3OIhXPHiZgscHTb3GPl3oB/rNEjoHXykFiLFy+mFs+JmE5wdAJe7UsW/GFFNysxaYMV3Sycj06ABd2sxKQNVnSzkENOwKt96WV/st7D0egcroGVLyKaiXXgwAFXx3SCoxPwal+y4A8rulmJSRus6GbhfHQCLOhmJSZtsKKbhRxyAl7tSy/7owa/OePIC05sIUo7ptu3YyXwal+y4A8rulmJSRus6GbhfHQCLOhmJSZtsKKbhRxyAl7tSy/7Iwe/OePg4ODg4ODg4ODg4HAB+M0ZBwcHBwcHBwcHBweHC8A3BHEQZDthYPaLUMtjA/a29RVFETfeeKO0fTWNrfSDwSBuvPFGBU872/oSjgQ0tvXV6le7W+kTnqQQN42t9H0+n6Iv7W7rSziSPqCxra/f71dwdGMR6kAgYDonzfokiiJuuukmKlu0E+4AFDxpbKUviiK2bduWFcdNRahFUcQtt9yCQCBgWmsun0iuk3ORhk9+vx/btm2T9BtpMuJO2uUck8mka4tQy3mSY+xspQ/AMCfz9YlwzGQyVLbSDwQCuOWWW6jOuSQOrSLUJCfVHN025waDQdx0000KnnbmXDM56YYi1CzMuerrQDfOuer3mO0518p31PjNGUW4uQh1NBqlXoSaVqHF+vp6lJWVoaurK6cmgE4hYDcVxASAXbt2UStCffLkSXzwwQemNFnx6frrr8eSJUuoFMQcGhrC66+/bknT512EOhKJ4NVXX7WkyYxPpaWlVM+nV1991Vbu6WlqbGyk4pOTRahvvPFGpFIpHDx40JSm2Rj3vvCFL2D58uWO+eT2ItQ0Nd144404c+YM3n333ZyaaM5PVsfydDqtyEm3FaG+/vrrsWjRIlfknpFPNTU1eO+99zA0NJRT02ydT04WoWZhzq2ursbVV1/tyjnXqSLU+Y7lvAj1LIMXoeZFqHkRal6Emheh5kWoAV6Emheh1tfEi1DzItRamliYc9U5SWPljPacy3IRar5y5iDIxClHIBBQTH4E8kkb+OzjQOp2eexc7eqf1X/j9/uzPnZk1K7mTn42q8monZxgWjzNaNXjTt7LrCajdnLyanG04xMwM6j4fD5TmnK1E95qnnZ9Iu9lxQ+9dnks+es0fNLinqtdiyO54Debk3bPp1ztuTSpedLyyYpWPe7y99C6GFTDqk92xgh5Ozk+FApJHOz6JP94jhaffH2S9yXN3NN6Xzs+kQtBdbve8bm46+VkPj6RCy/yEIu0m+Wo1U78dvOca8RRS5NRO4tzbq6ctOKTUV96Yc4lP7txzjXrx+c151opUs43BOHg4ODg4ODg4ODg4HAB+M0ZBwcHBwcHBwcHBweHC8C/c+YAyHfOzHyuVI1kMonW1lY0NzdbWgLVwuTkpPRdj/Hxcelnu5DvZKb+rodbYjrBkaY3BF7tSxb8YUW322PKx6GRkRHFd1/twO26nYrJwrkDsKGblZh8bHNvDrFw7jgRkwWObpt7rNwb8JUzjrwwPT3t+phOcHQCXu1LFvxhRTcrMWmDFd0snI9OgAXdrMSkDVZ0s5BDTsCrfellf+TgN2cegXqnG7uxDh065OqYTnB0Al7tSxb8YUU3KzFpgxXdLJyPToAF3azEpA1WdLOQQ07Aq33pZX/U4DdnHBwcHBwcHBwcHBwcLgDfSt9BkFovgLU6Z8BnW8jaqbmirgkk/91uzRVgZvv7UChEpc6ZFme7NVfIMUZardRc0eJIo84ZMPMZZvI5Zj1Nau5GmuSxadRcAT7LSRo1V9SxaNU5I6BRc4UcZyYnrdQ5I7BbP0uuSR6bVp0zAIrzXU+TvN3ovCFxk8kktTpnABQ5mktrLp9InGQyaat+lrqmlBaXfH2SczSjyWydM/n/uTSZrXNGeNoZywnIMXo5mY9Pco52x3ICrXFDT9Nszbm0x3JW5lwzOWnVJy2OXphz1TnpxjlX/R6zPeeqXzcCvzmjiJaWFrS0tEjJEo1GUVRUBMB6JfKBgQGsXr3aUnV1dcV4eXX0jo4ORCIRAOarqxPoVYzv7e1FbW2t5erqWppIoUBBEAw1AdYqxnd2dmLHjh2mNRn5VFVVlcXRSJNZn4CZkzcWi5nSlMunvr4+BU+ruaenaXh4GOXl5aY1Gfk0PDys4Gg19/Q0EVjJPT2fyMAq99tK7ulpIhO6ldwz8qmzs1PB02ru6WkKBoMYHh7G0aNHc2oy8qm6ulr6nYxD+YwRWprIBCr3yEiTWZ8EQchrjNDStGHDBgSDQXR2dmJiYiKnJrM+CYJAbSyvqKgAMDOm9/f359RkxSdBEKiM5fPnz0cwGMTAwAB6enpyarLikyAI1MbyhoYGBAIBRU66bc6tq6tDMBjM+miWG+dcn89Hfc4VBIHKWF5WVoa6ujopphlNc23O7evrw7p161w558qvgYHZn3OnpqZgFny3RgdAdmQZGhqSdmSxsnImrwxv5ymeujr6vHnzpNdoPG21+3QoH01G7U5rymQyaG1tVVSvZ13TXPKJnDvNzc3w+XxzQpO8nVWfYrGYtGPW4OAgFixYwLymueZTOp1Ge3s7mpqaFJ9kYFnTXPKJjG1NTU2IRCJzQpOaO6uaRFHMui5gXdNc8Ul9DVxcXDyrmsbGxlBaWmpqt0a+cuYg7FSMJ0v66nZ57Fzt8p8DgUDW3+Sqrq4G4Z7JZDA0NITS0lJLmozaM5kMRkZGUFpamvXeZrSquas55tJkpp2cvFq+5uuTnKdWRXsj7nrtPp9Psy/z9Undl1b8MGrX4kjDJzl3s+1aHK3mpBmf5H1JS5Pf71fkkBFHs+2ZTAaDg4MoLS21pFWPu/w95PHs+JTJZHDhwgVdjurjc3Enbeq+tOuTvC+13teqT4FAIItjvmO5VrvW++bjkzzXyWqxHZ9y5WQ+PqnH31yazLRrxdTTlKtdzZ38bHfco5mTTs+5Fy9e1OSZj09mc9KKT/KPF+d7vaduZ2HOVXN045xr1o/Pa861UmqBbwjiEai/S2A3VldXl6tjOsHRCXi1L1nwhxXdrMSkDVZ0s3A+OgEWdLMSkzZY0c1CDjkBr/all/1Rg9+ccXBwcHBwcHBwcHBwuAD85oyDg4ODg4ODg4ODg8MF4DdnHoF8y1casYqLi10d0wmOTsCrfcmCP6zoZiUmbbCim4Xz0QmwoJuVmLTBim4WcsgJeLUvvexP1nvw3Rrpg+zWaGZHFjWSySRaW1vR3Nxs6cuDWpicnJR2SZuYmFDs1shhHTS94aAP7o87IR+HRkZGsGDBgtklxJEFfu64G9wf94J74164be6xcm/AV848Avk2oTRiffLJJ66O6QRHJ+DVvmTBH1Z0sxKTNljRzcL56ARY0M1KTNpgRTcLOeQEvNqXXvZHDb6VvoNIJpPSNqtW6pwBn91M2akRIT8mFotJRagB+zVXuru7sWTJEmo1V9LpNLq7u1FWViY9fbJT94JwXLx4MQoLC6nU8tDqV7t1LwjPZcuWIRQKUalPkkgkFH1pt5YH4bh06VKEw2Eq9UlITMKRVp0zAhr1SVKplOmcNOsT0V1eXg5RFKnUXInH4wqeNOrIyD2X534+PslBxkQadWTk54764yX5+hSLxaS+LCgooFLvJ5PJSGORfGvmfH2ScyTbd9utYUSOUe8+Zscn+TleWFhoqMmMT+R81MvJfHySj5WRSIRKXSZRFLPGDT1NVuZcYMafUChke84lOanm6LY5V4unnTnXTE5a9YlA/r5emHPV14FunHPV70Grdlu+c676dSPwmzOKaGlpQUtLi5Qs0WgURUVFAKxXIh8YGMDq1astVVdXV4yXV0fv6OiQbs7MVlcn0KsY39vbi9raWsvV1bU0kUKB8srwWpoAaxXjOzs7sWPHDtOajHyqqqrK4mikyaxPwMzJG4vFTGnK5VNfX5+Cp9Xc09M0PDyM8vJy05qMfBoeHlZwtJp7epoIrOSenk9kYJX7bSX39DQRWMk9I586OzsVPK3mnp4mYMbzo0eP5tRk5FN1dbX0OxmH8hkjtDQBM+eO3CMjTWZ9EgQhrzFCS9OGDRsAzIxFExMTOTWZ9UkQBGpjeUVFBYCZMb2/vz+nJis+CYJAZSwnH08aGBhAT09PTk1WfBIEgdpY3tDQIMXMpWm25ty6ujoAwKFDh2yP5SzOuYIgUBnLy8rKpL6Uc/TSnNvX14d169a5cs6VXwMDsz/nTk1NwSz4d84cAPlc6dDQkPS5UisrZ4IgYOfOnQiHw7ae4smrow8ODio+b2v3KZ4gCGhqaqK6ctba2oqGhgZqK2eCIKCxsZHqUzw1RxorZ4IgYNeuXdRWzmKxGNrb2yWeNJ7iyXOSxlO8eDyOtrY2iSOtlTMy6fp8PttP8ZLJJA4ePGgqJ62snBGOJKeMNMnb9bhPT08jGo1KPGmtnBHP7a6cxWIx6cKajEO0Vs7IuUNz5UwQBDQ0NFBdOWtra0NjYyOVlbPp6WmJI82Vs/b2djQ1NSmKsdpdOSM8aa2cRaNR3ZzMd+WMcKS5cqYeN/Q0zdacS3JSzXGuz7lmctKqT6IoZnH0wpyrzkkaK2e051z5NfDIyAiKi4tndc4dGxtDaWmpqe+c8ZUzB2GnYjyZfKxUV1e3y38uKCjI+ptc1dXVINx9Ph/Kysokblarq2u1ZzIZxUeJ9DTlaifcCUf5YGmkyUw7OXm1fM3XJ8KTcDbSZLY9GAxq9mW+PhGO5G+t+KHXHggENDnS8EnO3Wy7Xr9byUkzPpG+9Pl81DSFQiFNnnZ8knuu9b5WfZK/h9bFoBpmfJKfO1Z91fOpoKAgqy/t+pRKpaSxSIunVZ+0OOY7lmu1a71vPj4RfwoKCqSb53zGcoJcOZmvT4QjjTkX+MxvrXHDrk/kZ7vjnhFHLU1G7U7OuaIo6vLMxyezOWnFJ3JBbud6T93Owpyrvg5045xr1o/Pa861smEMXzlzAHy3xrkJviuTu8H9cSfctmMWRzb4ueNucH/cC+6Ne+G2uYfv1siRBfUXve3Gev/9910d0wmOTsCrfcmCP6zoZiUmbbCim4Xz0QmwoJuVmLTBim4WcsgJeLUvveyPGvzmzCOgvd3piRMnXB3TCY5OwKt9yYI/rOhmJSZtsKKbhfPRCbCgm5WYtMGKbhZyyAl4tS+97I8arrg5+9GPfoS6ujoUFxdj8eLF+Ju/+RvFjirAzGdwtf795Cc/kY655ZZbsl6/8847FXFGRkawd+9elJSUoKSkBHv37sWlS5cUx5w+fRq333475s2bh9LSUtx///1IJBKO6efg4ODg4ODg4ODg4HDFzdnhw4fxrW99C6+//joEQUAqlUJjYyMmJyelY86ePav495vf/AY+nw9f+cpXFLH27dunOO6pp55SvL5nzx50d3ejra0NbW1t6O7uxt69e6XX0+k0du/ejcnJSbz22mvYv38/XnzxRTzwwAPOdgIHBwcHBwcHBwcHh6fhit0a29raFL//27/9GxYvXoy33noLN910EwBg6dKlimP+8z//E9u2bcPq1asV7UVFRVnHEvz1r39FW1sbXn/9dVx33XUAgF/96leor6/HiRMnUFNTg2g0ivfeew9nzpxBeXk5AOBnP/sZvv71r+ORRx6xvMGHW6C1o42dWCtWrHB1TCc4OgGv9iUL/rCim5WYtMGKbhbORyfAgm5WYtIGK7pZyCEn4NW+9LI/arji5kyN0dFRAMCiRYs0Xz9//jwOHDiAZ599Nuu1559/Hs899xyWLFmCXbt24Qc/+AGKi4sBAF1dXSgpKZFuzADg+uuvR0lJCTo7O1FTU4Ouri6sX79eujEDgKamJsTjcbz11lvYtm1b1nvG43HE43Hp97GxMQCQar1YATne6t8ZxQIg1aWghfXr1yOTyVD9zC3tmLTj0fRGDi/2pRMxnfCHBd1uj6muTcb9cV88VsY2FrxxIiYf29ybQ6ycO07EdDtHt809Vt7fdTdnoijie9/7Hr74xS9i/fr1msc8++yzKC4uxpe//GVF+1133YVVq1Zh6dKlOH78OB566CG8++67UhXvc+fOYfHixVnxFi9ejHPnzknHLFmyRPH6woULUVBQIB2jxo9+9CP88Ic/zGqPRqMoKirKLVoD8grp+UJeHb29vR2RSMR2TA463nA4B+6PuyAfhzo6Ovg45GLwc8fd4P64F9wb98Ftc8/U1JTpY113c/btb38bx44dw2uvvaZ7zG9+8xvcddddWR29b98+6ef169djzZo1uOaaa/D2229jy5YtACAVHpRDFEVFu5lj5HjooYfwve99T/p9bGwMlZWVaGxszKvOGalgT6POGcH27dup1XigydGpmCxwZCUmCxydiMkCRxZienkcciImCxydiMkCR1ZissDRiZgscGQlJgsc3Tb3kE/VmYGrbs6+853v4L/+67/wyiuvoKKiQvOYV199FSdOnMDvfve7nPG2bNmCUCiEkydPYsuWLVi6dCnOnz+fddyFCxek1bKlS5fijTfeULw+MjKCZDKZtaJGEA6HEQ6Hs9q1KsabhZ2/lcegGU8rvttjssCRlZgscHQiJgsc3RyTj0POxGSBoxMxWeDISkwWODoRkwWOrMR0M0e3zT1WjnXFt+5EUcS3v/1t/Md//Ac6OjqwatUq3WOffvpp1NbW4gtf+ELOuL29vUgmk1i2bBkAoL6+HqOjozhy5Ih0zBtvvIHR0VFs3bpVOub48eM4e/asdEw0GkU4HEZtbW2+Ejk4ODg4ODg4ODg4OAzhipWzb33rW3jhhRfwn//5nyguLpa+21VSUoLCwkLpuLGxMfz+97/Hz372s6wYfX19eP7559Hc3IzS0lK89957eOCBB7B582bccMMNAICrr74aO3fuxL59+6Qt9u+77z7cdtttqKmpAQA0NjZi7dq12Lt3L37yk5/g4sWLePDBB7Fv3z5md2oE6O/WWFNT4+qYTnB0Al7tSxb8YUU3KzFpgxXdLJyPToAF3azEpA1WdLOQQ07Aq33pZX+yILoAADT//du//ZviuKeeekosLCwUL126lBXj9OnT4k033SQuWrRILCgoEKuqqsT7779fHB4eVhw3PDws3nXXXWJxcbFYXFws3nXXXeLIyIjimE8++UTcvXu3WFhYKC5atEj89re/LcZiMdN6RkdHRQDi6Oio6b8hSCQS4h//+EcxkUhY/ls1JiYmpL6cmJiwHc/roOkNB31wf9wJ+TikHms53AF+7rgb3B/3gnvjXrht7rFyb+CK21NRFDX/ff3rX1ccd99992FqagolJSVZMSorK3H48GEMDw8jHo/j1KlTeOyxx7K241+0aBGee+45jI2NYWxsDM8991zWlwRXrFiBl156CVNTUxgeHsbjjz+u+Z0ylpBKpajG6uzsdHVMJzg6Aa/2JQv+sKKblZi0wYpuFs5HJ8CCblZi0gYrulnIISfg1b70sj9quOLmjMN5iKJINdaFCxdcHdMJjk7Aq33Jgj+s6GYlJm2wopuF89EJsKCblZi0wYpuFnLICXi1L73sjxqu+M7ZXIW86J3f70cgEEA6nVYUrSPtqVQKoihKx5NjSDtBIBCA3+/PKmYXDAal4+Xvr8UFmNk1JpPJIJ1OS20+nw/BYFC3nXAncdLpNEKhUE5Nau5a7VqctTQZtcs1kTipVMpQqx53rXYtjkaazPhEXierxUaa1NyNNMljm809PU3qnDSTe0btRJM8ltncU3OXt8t5Wck9PZ/IcWZy0qxP8lhWci+XT/LYVsYIvXa55/I4+fgkBxmH8hkj1Jrk545Zrbl8kheUzWeM0NIkH8vlyNcnddFbO2M5ATlGfqyRJjM+yXnaGcsJyDF6OZmPT3KOdsdyAq1xQ0/TbM25tMdyVuZcMzlp1Sctjl6Yc9U56cY5V/0esz3nMl2EmmW0tLSgpaVFShZ5EeoVK1Zg8+bNOHbsGE6fPi39TU1NDa666iocOXIEFy5ckNoHBgawevVqvPLKKxgfH5fa6+vrsXjxYkSjUUWybtu2DYWFhWhtbZXa9ArwBYNB7N69G0NDQ+jq6pKOKS4uxvbt23HmzBl0d3dL7WVlZdi6dStOnjyJEydOSO29vb2ora01rWnTpk1YuXKlpqaFCxcCUBZy1NIEAM3NzZiensahQ4ekNj1NnZ2d2LFjh2lNRj5VVVVlcTTSZNYnYObkjcVipjTl8qmvr0/B02ru6WkaHh5GeXm5aU1GPg0PDys4Ws09PU0EVnJPzycysMr9tpJ7epoIrOSekU+dnZ0KnlZzT08TMOP50aNHc2oy8qm6ulr6nYxD+YwRWpqAmXNHXQDWrk+CIOQ1Rmhp2rBhA4CZsWhiYiKnJrM+CYJAbSwnpWt6e3vR39+fU5MVnwRBoDKWz58/H8DM3NjT05NTkxWfBEGgNpY3NDRIMXNpmq05t66uDgBw6NAh22M5i3OuIAhUxvKysjKpL+UcvTTn9vX1Yd26da6cc+XXwMDsz7lWilD7RLevHzKIsbExlJSUYGhoSNrh0crKmSAI2LlzJ8LhsK2neJOTk9JNz/DwMIqLi6XX7DzFy2QyGBgYQGVlJdWVs48//hjLly+XnpjZeYpHOFZUVKCgoIDKU7xMJoPW1lZF4UG7T1sJz5UrVyqeyNjxKZlM4syZM1Jf2n2KRziuWLFCim/GDyOfUqkUTp8+LXGktXJGJl2fz2f7KV4mk8Enn3xiKifN+kT68oorrpD61kiTvF2PeyKRQH9/v8STxspZJpPBuXPnsHz5csV75uNTLBaTLqwHBwexYMECKitnmUwGZ8+eRWVlpeI9jbSaWTkbGBjA8uXLEQgEqKyc+Xw+DAwMYNmyZYon7fn6lEgkJI5+v5/ayll7ezuampoUn2Sw4xPJ9eXLl6OgoMBQkxmfRFHE2bNndXMyH5/S6bTEMRQKUVk58/v9OHPmDJYtW6ZY/bG7ciYIApqamhCJRGzPuSQnly5dquDotjnX5/NlXRfYmXPN5KRVn0RRzLou8MKcq74OpLFyRnvOlV8Dj4yMoLi4eFbn3LGxMZSWlmJ0dDTn7u/85swBkJszMwaokUwm0draiubmZtsF8yYnJ6WLoomJCcybN89WPK+Dpjcc9MH9cSfk49DIyEjWBkwcsw9+7rgb3B/3gnvjXrht7rFyb8A3BPEIaO+o09HR4eqYTnB0Al7tSxb8YUU3KzFpgxXdLJyPToAF3azEpA1WdLOQQ07Aq33pZX/U4DdnHgHtHXXGx8ddHdMJjk7Aq33Jgj+s6GYlJm2wopuF89EJsKCblZi0wYpuFnLICXi1L73sjxr85oyDg4ODg4ODg4ODg8MF4DdnHBwcHBwcHBwcHBwcLgC/OfMI5Dtw0YhVX1/v6phOcHQCXu1LFvxhRTcrMWmDFd0snI9OgAXdrMSkDVZ0s5BDTsCrfellf9Tgdc48AnUxWLuxFi9eTC2eEzGd4OgEvNqXLPjDim5WYtIGK7pZOB+dAAu6WYlJG6zoZiGHnIBX+9LL/mS9h6PRPY5kMin9I7UYSNVydXsqlZLagM8qw8vbSe0YdexkMglRFKU6afJ/BFNTU1ntpH4K+SevvaXVTrhPTU3hpZdekgr8mdEk567Vnkwm8dJLLyl46mky0kpiEY7T09OmNJn1Sd33dn0iPBOJRE5NZn2KxWKKvrSSe1qaCMd4PG4p94x8isfjCo5mcy+XTwRWck9PUyKRMJ2TZn0ifUk0WMk9Pe7T09MKnlbHCK32qakpHDhwAPF43FLuGZ03ck75jBFqTfJzx0ruGflEYk5NTeU9Rqi5x+NxHDhwANPT06Zzz8gnOUcruZfP+WTHJzlPO2M5+Tc9PW2Yk/loknOkNedqjRs0fCK/0/CJ5KSao9vm3GQy+7rAjk9mcjIfn9Tv64U5V30d6NY5Vw43zLlmwVfOKKKlpQUtLS2SsdFoFEVFRQCsVyIfGBjA6tWrLVVXV1eMl1dH7+joQCQSAWC+ujqBXsX43t5e1NbWWq6urqVp4cKFSKfTisrwWpoAaxXjOzs7sWPHDtOajHyqqqoCoKxeb6TJrE/AzCAQi8VMacrlU19fn6Ivreaenqbh4WGUl5eb1mTk0/DwsIKj1dzT00RgJff0fCKFOOV+W8k9PU0EVnLPyKfOzk4FT6u5p6cplUpheHgYR48ezanJyKfq6mrpdzIO5TNGaGkCZs4duUdGmsz6JAhCXmOElqYNGzYglUqhs7MTExMTOTWZ9UkQBGpjeUVFBYCZMb2/vz+nJis+CYJAZSyfP38+UqkUBgYG0NPTk1OTFZ8EQaA2ljc0NGSNG26bc+vq6pBKpXDo0CHbY7nTc666L2n4JAgClbG8rKwMdXV1UkwzmubanNvX14d169a5cs6VXwMDsz/nTk1NwSx4EWoHQArNDQ0NSYXmzFYiTyaTEAQBO3fuRDgc1q1Qrr4D16qiLq+OPjg4qCjAZ6a6urqdcCccm5qaEIlELFdX12pPp9NobW1FQ0ODVMhRrzK8mYrxhGNjYyMKCwtzalJz12rPZDJZHI00mfGJ8Ny1axdCoZChJrM+xWIxtLe3SzzN5p6eT+qcNJN7uXyKx+Noa2uTOJrNPSOfCM/m5mb4fD7TuafnUzKZxMGDB03lpFmf5BxJThlpkrfrcZ+enkY0GpV4Whkj9Nrlnss/Ep2PT7FYTCoESsahfMYItSb5uePz+UxpzeVTLBaDIAhoaGhAQUGB5TFCS1Mmk0FbWxsaGxslXnqajLiT9unpaYljKBSyNZYTpNNptLe3o6mpSfE9Cjs+EX8aGhpQWFhoqMmMT6lUCtFoVDcn8/EpkUhIHCORiK2xnEAUxaxxQ0/TbM25JCfVHOf6nGsmJ636JIpiFkcvzLnqnLQzlhPutOdc+TXwyMgIiouLZ3XOHRsbQ2lpqaki1HzlzEGQiVOOQCCg+SVC+aQNfPYdMXW7PHaudvXP6r/x+/2a30XTa1dzJz+b1WTUTk4wLZ5mtOpxJ+9lVpNROzl5tTja8QmYGVR8Pp8pTbnaCW81T7s+kfey4odeuzyW/HUaPmlxz9WuxZFc8JvNSbvnU672XJrUPGn5ZEWrHnf5e2hdDKph1Sc7Y4S8nRwfCoUkDnZ9IhN2MBjU5JOvT/K+pJl7Wu9rxydyIahu1zs+F3e9nMzHJ3LhRR5ikXazHLXaid9unnONOGppMmpncc7NlZNWfDLqSy/MueRnN865Zv34vOZcvde1wFfOHABZOTNzd6xGMplEa2srmpubLRmphcnJSemJ9fj4uPSzXZACfMXFxVlPrN0S0wmONL0h8GpfsuAPK7rdHlM+Do2MjChW8O3A7bqdisnCuQOwoZuVmHxsc28OsXDuOBGTBY5um3us3BvwDUE48gL5SICbYzrB0Ql4tS9Z8IcV3azEpA1WdLNwPjoBFnSzEpM2WNHNQg45Aa/2pZf9kYPfnHkE6s/r2o3V2trq6phOcHQCXu1LFvxhRTcrMWmDFd0snI9OgAXdrMSkDVZ0s5BDTsCrfellf9TgN2ccHBwcHBwcHBwcHBwuAL854+Dg4ODg4ODg4ODgcAH4zRkHBwcHBwcHBwcHB4cLwHdrdABurHN28eJFxW6NdmquiKKIVCqVsw6QlboXPp8P09PTCAaD0u43dmquEI656gDNdp0zwjMSicDn81Gpc0bq95C+tFtzhXAMh8MIBAJUaq6k02nE43GJoxvrnImiiFgsZionzfokiiLS6TQikQgymQyVmiukphThSaPOmSiKUu7Iubipzhl5nbyvGa25fEqlUkilUggGg/D7/VTqnKn70EiTEXfSnkwmJY6kjIAb65yRcUNeQsBOnTO9/rRT54x4HgwGJc/t1jkjsQk3I02zVeeM9CE5z3Npmq05l4wf5BrBSJMZn8zkpBvqnLEw56qvA2nUOaM95zpV5yzfOZfXOZsltLS0oKWlRUqWaDSKoqIiANYrkQ8MDGD16tWWqqurK8bLq6NHo1FEIhEA5qurE1itGJ+rurqWprKyMnR0dChONC1NgL2K8XY0VVVVAYCier2RJrM+AcCuXbsQi8Woafrggw9MabLi0/XXX48lS5aY1pTLp9dff92SJjM+EVjJPT2fIpGIwmszmsz4tHDhQtx4443UzqdXX33VVu7pabrxxhsxPT1t26fq6mrp946ODkQikbzGCC1N1157LS6//HIcPHjQlKbZGPe+8IUvYNGiRXjzzTcd8YmGpoqKCgBAb28v+vv7c2qiNe5Z1XTNNdfg4sWLePfdd3Nqojk/WR3Lh4eHceTIEVOarPjU29uL2tpaKmN5YWEhXn311VnPPSOfampqcPToUYyMjOTUNFvnU11dHQDldYGX5tzq6mpcffXVrpxz5dfAAGZ9zp2amoJZ8JUzB+DGlTPyxJrATU/xSL+onz7ZeYpHODY2NqKwsNC1K2eE565duxAKhaisnMViMbS3t0s87T7FU+ckjad48XgcbW1tEkc3rpwlk0kcPHjQVE6a9UnOUb0CkK9P09PTiEajEk8aK2dyz+VFPt20ciY/d9S1ZvL1KRaLQRAENDQ05PxkgFmfMpkM2tra0NjYqChqmq9P09PTEsdcqxSzuXJG/GloaJC2nbazcpZKpRCNRnVzMh+fEomExDESiVBZORNFMWvc0NM0W3MuyUk1x7k+55rJSTesnLEw56pzksbKGe0516mVs3znXL5y5hLYqRhPJh8r1dXV7eqf1X+Tq7q6GnqV4fOtri4HOcHMVobXa1dzJ+9lVpNROzl5tTja8QmA9DEDM5pytRPeap52fSLvZcUPvXZ5LPnrNHzS4p6rXYsjueA3m5N2z6dc7bk0qXnS8smKVj3u8vfQuhhUw6pPdsYIeTs5PhQKSRzs+kQmbPlHqXJxNNMu70uauaf1vnZ8IheC6na943Nx18vJfHwiF17kIRZpN8tRq5347eY514ijliajdhbn3Fw5acUno770wpxLfnbjnGvWj89rztV7XQt8QxAODg4ODg4ODg4ODg4XgN+cceQFvScKborpBEcn4NW+ZMEfVnSzEpM2WNHNwvnoBFjQzUpM2mBFNws55AS82pde9kcO/p0zB0C+c2bmc6VqJJNJtLa2orm52dISqBYmJyel73pMTExg3rx5tuJ5HTS94aAP7o87IR+HRkZGFN995XAH+LnjbnB/3AvujXvhtrnHyr0BXznzCLS2crYTa3Bw0NUxneDoBLzalyz4w4puVmLSBiu6WTgfnQALulmJSRus6GYhh5yAV/vSy/6owW/OPAKtejF2YnV1dbk6phMcnYBX+5IFf1jRzUpM2mBFNwvnoxNgQTcrMWmDFd0s5JAT8GpfetkfNdj4cCejINsJA9a20gc+W+mys62vettp+e92t/UFZhKUFDe0u5W+Fme72/qSY2gVxNTiSGMrfQCKwpN6mtTcjTTJY9PY1hf4LCdpbOurjkVrK30CWgUx1XFpbKVPYHeLdrkmeWxaW+kDUJzveprk7UbnDYmbTCapbaUPQJGjubTm8onESSaTtrZoV29brsUlX5/kHM1oMruVvvz/XJrMbqVPeNoZywnIMXo5mY9Pco52x3ICrXFDT9Nszbm0x3JW5lwzOWnVJy2OXphz1TnpxjlX/R6zPeeqXzcCvzmjCDcXoSbFXwH3FcSsr6+XalHICxDSKB7Z2dmJHTv+//bOPTiqMs3/376lEyAGQrgKRLklwx1C5DrKLR0uKuyMg4xrFhwLtxzEdZ2pXXH3V6NTterUjuW6FusMilIrIsyOMosQkvQKBDEBQiACQRBQ7peQmCukk768vz+oc+acTl/O6X5Pct7086lKQd68/Zzneb/vpd/zdp9nvumTUPt8Pm5JqM+fP6/yk1dCzLq6OgwePJhLQsy6ujqVj2ZMQi1NrEq9eSQuleCVELOsrEzlJ69krMBdzSsqKqLG1FVJqIG7Y4d3snC3280tufH48eMB3J2LWlpaosakVSe3281tLjcyCbXb7eYyl0vfHbl69SpOnDgRNSY9Orndbm5zeV5enmwzWkxdteZKiZP37t3LPWGzCGuulGvS7EmoRVhzz58/j7Fjx5pyzTUqCXWsay4loe5izJiE+tatW0hLS5P/Fs9dPJ/Ph7KyMsyePRtOp5PLyVkgEEBpaSlmzpwpxxLPXTzJx1mzZiE5OZnLXbxAgH8SasnPBx98EHa7ncvJWVtbGw4cOCC3Zbx38SQff/zjHyMpKYnLXbz29nZ8+eWXso9mTELt8/mwf/9+TX1Sq05SWz700EOwWCxc7uJ5PB589dVXsp88Ts58Ph/Ky8sxe/Zs1d1hMyWhVo6dYGLVqa2tDWVlZZg5c6ac6yzeu62MMXk8BucHikUnj8cj+2i327mdnPFOQi3pM3PmTPnGYDwnZ36/X153QvXJWE/OJB+dTieXkzMAHeaNcDF1VRJqqU/OmDFD5aPZ1lzGWIf3BfGsuVr6pBmSUIuw5ga/D+RxcsZ7zTUqCXWsa66eJNS0OTMAelpj94SeymRuSB9zYrYnZhEdobFjbkgf80LamBezrT30tEaiA7yfqHPx4kVT2zTCRyNI1LYUQR9R4hbFJm9EiVuE8WgEIsQtik3eiBK3CH3ICBK1LRNZn2Boc5Yg8H6iTlVVlaltGuGjESRqW4qgjyhxi2KTN6LELcJ4NAIR4hbFJm9EiVuEPmQEidqWiaxPMLQ5IwiCIAiCIAiCMAGm2Jy9/vrryM3NRWpqKvr3749ly5apnqgCAKtWrYLFYlH9TJ8+XVWnra0Na9euRUZGBnr27IlHH31U9eQp4O7nTgsKCpCWloa0tDQUFBSgoaFBVefSpUt45JFH0LNnT2RkZOD5559He3u7IbETBEEQBEEQBEEAJtmclZaWYs2aNTh48CDcbjd8Ph9cLhdu376tqrdw4UJcv35d/gl+jOgLL7yA7du3Y+vWrThw4ABaWlrw8MMPq44en3jiCVRVVaGoqAhFRUWoqqpCQUGB/He/348lS5bg9u3bOHDgALZu3YpPP/0Uv/rVr4xtBINRPt2Kh61+/fqZ2qYRPhpBoralCPqIErcoNnkjStwijEcjECFuUWzyRpS4RehDRpCobZnI+nSAmZCamhoGgJWWlsplK1euZEuXLg37moaGBuZwONjWrVvlsqtXrzKr1cqKiooYY4ydOnWKAWAHDx6U65SXlzMA7PTp04wxxgoLC5nVamVXr16V63zyySfM6XSyxsZGTf43NjYyAJrrK2lvb2d/+ctfWHt7u+7XBtPS0sIAMACspaUlbnuJDk9tCP6QPuZEOQ/V19d3tTtECGjsmBvSx7yQNubFbGuPnr2BKZNQNzY2AgDS09NV5fv27UP//v3Ru3dvPPTQQ/i3f/s39O/fHwBQWVkJr9cLl8sl1x88eDDGjRuHsrIy5Ofno7y8HGlpaZg2bZpcZ/r06UhLS0NZWRmysrJQXl6OcePGYfDgwXKd/Px8tLW1obKyEnPnzu3gb1tbG9ra2uTfm5qaANx9xKqejODSa5T/xoPShsfjQVJSUtw2gbuni+fPn8eIESNUOXHMZNMIH3lqI5GobSmCPqLEbXabSj1imRPDYfa4jbIpwtgBxIhbFJs0t5m3D4kwdoywKYKPZlt79FzfdHnOGGNYunQp6uvr8eWXX8rl27ZtQ69evZCZmYnvv/8e/+///T/4fD5UVlbC6XRiy5YteOqpp1SbJABwuVy4//778cc//hGvvfYaNm3ahG+//VZVZ/To0Xjqqaewbt06PPPMM7hw4QJKSkpUdZxOJzZt2oSf//znHXx+5ZVX8Oqrr3Yo37JlC3r06BFPc8SFx+PBihUrAABbt26Vky4SBEF0FjQPEQRBEJ2N2daeO3fu4IknntCU58x0J2fPPfccjh8/jgMHDqjKH3/8cfn/48aNw9SpU5GZmYldu3bhJz/5SVh7jDHV50JDfUY0ljpK1q1bhxdffFH+vampCUOHDoXL5YopCbXb7VZlm48V5Xf25s2bxy0BH08fjbIpgo+i2BTBRyNsiuCjCDYTeR4ywqYIPhphUwQfRbEpgo9G2BTBR1FsiuCj2dYe6VN1WjDV5mzt2rXYsWMH9u/fjyFDhkSsO2jQIGRmZuLs2bMAgIEDB6K9vR319fXo06ePXK+mpgYzZ86U69y8ebODrVu3bmHAgAFynUOHDqn+Xl9fD6/XK9cJxul0wul0dih3OBwxd7B4Xqu0wdNeKPtmtymCj6LYFMFHI2yK4KOZbdI8ZIxNEXw0wqYIPopiUwQfjbApgo+i2DSzj2Zbe/TUNcXTGhljeO655/DZZ59hz549uP/++6O+pq6uDpcvX8agQYMAADk5OXA4HHC73XKd69ev4+TJk/LmbMaMGWhsbMThw4flOocOHUJjY6OqzsmTJ3H9+nW5TklJCZxOJ3JycrjE2xVYrfyktlqtGDZsmKltGuGjESRqW4qgjyhxi2KTN6LELcJ4NAIR4hbFJm9EiVuEPmQEidqWiaxPMKb4ztkvf/lLbNmyBf/7v/+LrKwsuTwtLQ0pKSloaWnBK6+8gp/+9KcYNGgQLly4gJdffhmXLl3CN998g9TUVADAs88+i507d2LTpk1IT0/Hr3/9a9TV1aGyslL+0t6iRYtw7do1/PGPfwQAPPPMM8jMzMTnn38O4O4X/SZNmoQBAwbg3//93/HDDz9g1apVWLZsGd555x1N8TQ1NSEtLU3T50qD8Xq9KCwsxOLFi7l8nKhXr14AgJaWFvTs2TMue4kOT20I/pA+5kQ5D9XX13P7aAnBDxo75ob0MS+kjXkx29qjZ29giu3pu+++i8bGRsyZMweDBg2Sf7Zt2wYAsNlsOHHiBJYuXYrRo0dj5cqVGD16NMrLy+WNGQC89dZbWLZsGZYvX45Zs2ahR48e+Pzzz1VPU/n4448xfvx4uFwuuFwuTJgwAR999JH8d5vNhl27diE5ORmzZs3C8uXLsWzZMvz+97/vvAYxAGWuNx62jh07ZmqbRvhoBInaliLoI0rcotjkjShxizAejUCEuEWxyRtR4hahDxlBorZlIusTjCk2Z4yxkD+rVq0CAKSkpKC4uBg1NTVob2/HxYsXsWnTJgwdOlRlJzk5Ge+88w7q6upw584dfP755x3qpKenY/PmzWhqakJTUxM2b97cYTc9bNgw7Ny5E3fu3EFdXR3eeeedkN8pE4lAIMDV1qVLl0xt0wgfjSBR21IEfUSJWxSbvBElbhHGoxGIELcoNnkjStwi9CEjSNS2TGR9gjHF5owgCIIgCIIgCCLRoc0ZQRAEQRAEQRCECTDVo/S7G8qM5FarFTabDX6/X3UUKpX7fD4wxuT6Uh2pXMJms8FqtXbING632+X6yutL+P1+1e8OhwOBQED1mVmLxQK73R62XPLd7/dj5MiRsl/RYgr2PVz5yJEjVdcNFVOkcmVMko+SX9FiCvY9VHmodo0WUzSdJD8tFgsYYxFjCvY9XDljTNWWWvteuJgkH0PFHyqmaOXSl6aVPmrte8G+K8uVfunte6FislgsmvukVp38fj9GjRoFq9Wqq+9F0ikQCKj81DNHhCv3+/3yw5mUdmLRSYk0J8Y6RwTPaaNHj4bFYtEcqxadpLaMZY4IFRMAZGVlIRAIhJw79Oqk9FFrTMHlwb4r+6eSeHRS+hnPXC4RCAQi9slYdFL6GO9crvRn9OjRHdoyHp2ka/v9fjgcjrjXXOBunwz20WxrrtVqxahRo1R24tFJS5/Uq5OE8rqJsOYGvw8045obfI2uXnOD/x4J2pxxZP369Vi/fr0sWElJCXr06AHg7vfYJk+ejOPHj+PSpUvya7KyspCdnY3Dhw/j1q1bcvnVq1cxfPhw7N+/H83NzXL5jBkz0L9/f5SUlKg669y5c5GSkoLCwkKVT+3t7WhtbcXevXvlMrvdjiVLlqC2thbl5eVyeWpqKubNm4fLly+jqqpKLu/Xrx9mzpyJs2fP4syZMyrbemKaNGkSMjMzw8Z04cIFnDt3LmpMixcv1hzTzZs3dcUUSacRI0YAgCpdQ7SYtOo0evRoNDc3c9Hpu+++w7lz5+S21Nv3wsXUr18/XTFF0qm+vl7lo96+Fy4mCb19L1xMSh+jxaRHJ5vNhtOnT2vue5F0+uqrr9Dc3Cz7qbfvRYqppqYmrjli2LBhGD16tPz7nj17kJycHPMcESomxlhcc0SomM6dOxfTHBEupuzsbOzZsyeuOSI4pnPnznGby6W8otXV1bhy5YqmmLTqdO7cOS5zuRTTxYsX457Lg2M6d+4ct7l88eLFuPfee1FcXKw5Jq06VVdXIycnh8uam52djV27dsU9Rxi55mZnZ6OhoUHVljx0OnfuHLe5PDc3F4D6fUEirbk2m43bXM57zfV4PKr24TGXx7Pm3rlzB1oxxaP0uxvS4zJra2vlx2XqOTlzu91YuHAhnE5n3HfxgLtvVKdMmSLXAeK7i+fz+VBZWYnc3Fw4nU4uJ2eBQAAHDx5ETk6O7Gc8d/EkH6dOnYrk5GQud/ECgQAKCwtVWeHjvdsq+Tlt2jTY7XYuJ2dtbW2oqKiQ2zLeu3iSjw888ACSkpK43MVrb2/H4cOHZR95nZy53W4sXrxYPokMF5MWnXw+Hw4dOqSpT2rVyefz4ejRo5g2bRosFguXu3gejwdHjhyR/eRxcubz+XDs2DFMnTpVdXc4Fp08Ho/8OOOamhr07t2by8mZ1JYPPPAAgolVp7a2NlRWVsp5M3mcnDHGcOTIEUyZMkX15OBYdfJ4PLKPdrud28lZcXEx8vPzVT7Go5M0b+Tk5CA5OTliTFp08vv9OHr0aNg+GYtOXq9X9tHpdHI5OQP4r7nS3Jafn4/k5OS411ypT06ePFnlo9nWXMYYDh06pGrLeNZcLX1Sr07SzSHl+4JEWHOD3wfyODnjvebevn0bffr0AXD3UfqpqalduuY2NTUhIyND06P06eTMQEJlD7fZbKrFT0I5QQJ//ThQcLnStpZyr9eL2tpaeREPvkaoJHrhypW+19XVyXW0xhSp3O/3o66uLqSfWmMN9r2urk72S0tM0cqlwRtK13h0qqurA3B3YokWk9byUG0Zj051dXXyGyI9eoQrt1gsIX3koVOw71rKw/mup09q1am2thaMMXlSD0ZvTDabLaSf8ep069Yt3X0ynO/KayjtxatTbW1tyPJw9YHIOtntdrktlW8G49HJ6/Xi1q1bsNlsIf3Rq5PSR+WbwVjn8uDyUNeNVSfJz1jmjlC+R+qTsejEGJN9NPuaK/0eLaZQhOuToXwMFVOkciPX3EhtGatOWvqkHp2kN+TxvN8LVS7Cmqt8H2jGNVerHp215urJg0cPBCEIgiAIgiAIgjABtDkjCIIgCIIgCIIwAbQ56+bYbDZMmjQp4keNuqNNI3w0gkRtSxH0ESVuUWzyRpS4RRiPRiBC3KLY5I0ocYvQh4wgUdsykfUJhr5z1s2xWq3IzMxMOJtG+GgEidqWIugjStyi2OSNKHGLMB6NQIS4RbHJG1HiFqEPGUGitmUi69PhGoZaJ7ocn8+HPXv2hHyiVHe2aYSPRpCobSmCPqLELYpN3ogStwjj0QhEiFsUm7wRJW4R+pARJGpbJrI+wdDJmYF0dRJq4O4jc5ubm9He3q6yE+9jfZubm+Hz+SI+clXPo1VD+RnPY30lH71eb9yPaJfKJXgmoZb8DAQC3JJQ+3w+VVvG+1hfyUcpcSePx/r6/X6Vj2ZMQh0IBDT3Sa06SW0pJRrl8Vhfyabkp945IlS5UnPlNc2UhFo5dngloW5vb5fbUrITr05SP/J6vXHN5VK50kfGmGmTUCv7pfTxn3gepS/Na+H6ZCw6KdtSesKbWddcgF8S6lBzW6SYumrNDdWW8ay5WvqkGZJQi7DmBr8PNOOaa1QS6ljXXEpC3UWYMQl1Xl4eAHWCRLvdfAkxpVwUSj95JMQsKyvD/PnzTZ+E2ufzwePxcElCff78eZWfvBJi1tXVYfDgwVwSYkrpAyQfzZiEWppYlXrzSFwqwSshZllZmcpPXkmogbuaV1RURI2pq5JQA3fHjlKjSDFp1cntdnNLQj1+/HgAd+eilpaWqDFp1cntdnOby41MQu12u7nM5VKuvKtXr+LEiRNRY9Kjk9vt5jaXi7DmSomT9+7dG/dcLuKaK+XDNHsSahHW3PPnz2Ps2LGmXHONSkId65pLSai7GDMloWaMYffu3aoEiYC5EmJK7RKcyDHeu3hutxsulwspKSmmTUIt+blo0SI4HA4uJ2cejwfFxcWynzzu4in7JI+7eG1tbSgqKpJ9NGMSaq/X22Hs8Dg5k3yU+lSkmJTl4XxvbW1FSUmJ7CevkzNJc+Xpl5mSUCvHjvIOdqRYo+nk8XjgdruRl5eHpKQkbidnRUVFcLlcqrw5serU2toq+yjlVjJjEmpJn7y8PKSkpESMSevJWUlJSdg+GevJmeRjcnIyt5Mzs6+5Up8M9rG7r7la+qQZklCLsOYG90keJ2e811yjklDHuubqSUJNmzMDkDZnWgQIxuv1orCwEIsXL9aVsC4cgUAAtbW1yMjI6PARo+5s0wgfeWsDJG5biqCPKHGb3ebt27flzVl9fT169+7NwUPzx22UTRHGDiBG3KLYpLnNvH1IhLFjhE0RfDTb2qNnb0Afa+zmWK1W9O/fP+FsGuGjESRqW4qgjyhxi2KTN6LELcJ4NAIR4hbFJm9EiVuEPmQEidqWiaxPh2sYap3ocrxeL3bt2qXri4jdwaYRPhpBoralCPqIErcoNnkjStwijEcjECFuUWzyRpS4RehDRpCobZnI+gRDm7MEwIjHfYpg0+yPY5VI1LYUQR9R4hbFJm9EiVuE8WgEIsQtik3eiBK3CH3ICBK1LRNZHyW0OSMIgiAIgiAIgjABtDkjCIIgCIIgCIIwAfS0RgMw09MapSSOqampHR433Z1tGuGjEU9lStS2FEEfUeI2u02jnphl9riNsinC2AHEiFsUmzS3mbcPiTB2jLApgo9mW3voaY0mQcr1AujLcwZArhNvzhWbzYbk5GR4vV5VJ4on5wpjTP49Uj4MPXkvpGso/Ywn54rko8/nixirnpwrEsq2jzfPmeSn9Foeec4kXaS2jDfniuRjIBCAzWbjknMlEAiofOSV50yCR84VKW4tfVKrTowxeQGPN3+WMialnzzynDHG5FxFSl9i0UmJNCfyyHPGGENycjIYY3Hlz1LGpGxLq9XKJc+Z1WpFSkpKyLk5Fp28Xq9Kb155zpT/RotJi07K8SP1+Xh0AhCxT8aik3IestvtXPKcGbHmStf2+/1wOBxxr7lSnwz20WxrrtVqhcPhUPkZz5qrpU/q1UlCed1EWHOD3weacc0NvkZXr7l6HiBCmzOOrF+/HuvXr5c7S0lJCXr06AFAfybyq1evYvjw4bqyq4fKGJ+Xl6fKtg5oz64uES5j/JAhQ5CTk6M7u3qomPr06dPBz3Ax6ckY36tXL8yfP19zTJF0GjFiBAB19vpIMenVyefzaYopmk5nzpzBuXPnNMWkR6fc3FwMHjxYc0yRdLp58yYqKio0x6RVJwk9fS+cTna7vUOf1NP3wsUk2Tl//rzmvhdNp5aWFk0x6dHJ5/MhNzeXi05HjhzBlStXUFZWpjkmLToBoec4HjrFMkeEimn8+PE4ceIEevXqZYhOvOZyAKiursaVK1eixsRr3tM7l7e0tMjtGS0mXvOemdbc6upqLmuuNK6lcR4tpq5cc0tKSlS+dEXfi6RTbm4uAPX7gkRac0eOHImxY8eacs31eDwqWzzniFjm8jt37kAr9LFGA5COLmtra+WjSz0nZ8rM8PHexWOMdci4DsR/F0+ZGZ7HyZnf70dhYaGmzPBa7qRIPrpcLvlua7x38QKBQAcf4z05k/xctGgRHA4Hl5Mzj8eD4uJi2c947+IF90ked/Ha2tpQVFQk+8jr5MztdmPx4sWwWCxx38Xzer0dxk68J2dKH6U+FSkmZXk431tbW1FSUiL7yePkTKm58vQrVp3C9cl47rYqx07wR0ti1cnj8cDtdiMvLw9JSUlcTs4CgQCKiorgcrlkv8LFFMl3qby1tVX20eFwcDs5Ky4uRn5+Pmw2W9SYtOgk6ZOXl4eUlJSIMWnRyefzoaSkJGyfjEWn9vZ22cfk5GQuJ2cirLlSnwz2sbuvuVr6pF6dGGMdfEyENTe4T/I4OeO95t6+fRt9+vQBcPdjjampqV265jY1NSEjI4M+1tjVSAunEpvNplr8JJSLNvDXjwMFlyttaymXOksoX6SPDQQTrjzYd+n/WmOKVC4NsFB+ao01lO/StbTGFKlcGryhfIxXJ+ljBlpiilYu+R3sZ7w6SdfSo0e4cqUt5d956BTK92jloXyU3vBr7ZPxjqdo5dFiCvaTl056Yo0UUygfeekUzxyhLJfqOxwO2Yd4dZLmYLvdHtKfWHVStiXPvhfquvHoJL0RDC4PVz+a7+H6ZCw6SW+8pBsGUrlWH0OVi7DmRvIxVEyRykVcc6P1ST06RWrLRFhzpf+bcc3Vqkdnrbnh/h4KelojQRAEQRAEQRCECaCPNRqA2Z7WKH15kecTdcxu0wgfjXoqUyK2pQj6iBK3CDYTdewYYVOEsQOIEbcoNmluM28fEmHsGGFTBB+NfFpjLH7q2RvQyVkC0NrampA2jfDRCBK1LUXQR5S4RbHJG1HiFmE8GoEIcYtikzeixC1CHzKCRG3LRNZHCW3OujnSEwCDv0zZ3W0a4aMRJGpbiqCPKHGLYpM3osQtwng0AhHiFsUmb0SJW4Q+ZASJ2paJrE8wtDkjCIIgCIIgCIIwAbQ5IwiCIAiCIAiCMAH0KH0DkXK9APrynAF/fYQsj5wroTLMx5tzRYrF4XBwyXMm/av0M96cK5IPkWLVk3NFQukjjzxnNpsNjDH5S6bhYgr2PVJMyrbkkXPFZrOp8olFiilauRST0kdeec4keORcCTV2eOQ5k/p7vPmzlDEp/eSV50zyXWknHp0ku1pj0pLnTBo7WmONppNk0+v1xpU/KzinlN1u7+BLrDopfdQSk9Y8Z8p/o8WkNc+Z5Gc8c7mE9AX8cH0yFp2UPsY7l0sYteYC4LbmSn2S11xu5Job3Jbx5jmL1if16iShvG4irLnB7wPNuOYGX6Or19zgv0eCNmccWb9+PdavXy93lpKSEvTo0QOA/kzkV69exfDhw3VlVw+XMf7BBx9UZV2327VlV5cIlzH+m2++iTm7eqiYLBaLys9IMWnNGF9eXq4rpkg6jRgxAgBUPkaLSatOFosFzc3NmmKKptOFCxfg9/tlP/X2vXAxNTY26u574XRqbGxU+ai374WLSUJv3wsXk9LHaDHp0cnhcOD06dOa+14kncrLy1V+xjJHhIuppqYmrjlCiqm6uhrAX8dOrHNEqJgsFktcc0SomNxud0xzRLiYlixZgj179sQ9lytjcrvd3ObyIUOGAACqq6tx5coVTTFp1cntdnOZy1NTU7FkyRJcvHgx7rk8OCa3281tLjdyza2urkZOTg6XNXfJkiXYtWtX3HOEkWtudnY20tPTua+5breb21yem5sr29QaU3dacy9cuMBtLue95no8HlX78JjL41lz79y5A63Qo/QNQHpcZm1trfy4TD0nZ8rM8PHexbNaraitrUXv3r1Vpz/x3MULBAKoq6uT32TyOjm7ceMG+vbt2yEZaCx38SQfMzIykJSUxOUuXiAQQGFhoSp7fbx3WyU/BwwYINsJF5NWnbxeL27duiW3Zbx38SQf+/fvz+1uq8/nQ01Njewjr5MzadG1WCxx38ULBAK4efOmpj6pVadAIIAffvgBAwYMkNs2UkzK8nC+t7e3o7a2VvaTx8lZIBBAY2Mj0tPTVdeMVSePx4Pi4mJ57PA4OQsEAmhoaEBGRobqmpFi1XJyVldXh759+8Jms3E5ObNYLPjhhx/Qu3dv1Z32WHVqb2+XfZQSrfI4OSsuLkZ+fr4qGWs8OknzRt++fZGUlBQxJi06McbQ0NAQtk/GopPf75d9lPqlGddcaW7Lz89HcnJy3Guu1CfT0tJCJpA2y5prsVhw8+ZNpKenq07SYtVJS5/UqxNjrMP7gkRYc4PfB/I4OeO95t6+fRt9+vQBcPdR+qmpqV265jY1NSEjI4Mepd/VSNnDHQ6H6qN7ocrtdrsq27iyYyrrB2d5l34sFgssFkuH8kAggIMHD8qLePA1lGVSRwpXLvlutVpRUVEhx6klJqXvocr9fj8qKipU1w4XU7hype+Sj9KboWgxadUpuO3j1UnyMxAIRI1Jq04AVG2pp++FiknyUZqktOoRSSfGmMpHrX0vmk4SevpeuJgCgYDmPqlVJ6vVisOHD8sfPdXT98L5brFYVH7qnSNClVutVpSXl4MxpqvvRYpJ6VMsc0SosXfw4EEEAgFdfS+STlJft1qtMc8Rwb4zxlBeXh6yjWPRSemjnr4Xy3iKRyeln/HM5cq6kfpkLDEpfTTzmiu9npdOUp8M1cZmWnP9fj8OHz7cYQ6NVSctfTIWnYKvmwhrbvD7QLOuuUrMsOZqhTZnBEEQBEEQBEEQJoA2ZwRBEARBEARBECbAFJuz119/Hbm5uUhNTUX//v2xbNky1Zf2vF4v/vmf/xnjx49Hz549MXjwYPzd3/0drl27prIzZ84c+RhR+lmxYoWqTn19PQoKCpCWloa0tDQUFBSgoaFBVefSpUt45JFH0LNnT2RkZOD5559He3u7YfEbicViQWpqquq7Dolg0wgfjSBR21IEfUSJWxSbvBElbhHGoxGIELcoNnkjStwi9CEjSNS2TGR9OlzDDA8EWbhwIVasWIHc3Fz4fD78y7/8C06cOIFTp06hZ8+eaGxsxGOPPYbVq1dj4sSJqK+vxwsvvACfz4cjR47IdubMmYPRo0fjt7/9rVyWkpKCtLQ0+fdFixbhypUr2LBhAwDgmWeewX333YfPP/8cwN0vRk+aNAn9+vXDm2++ibq6OqxcuRI/+clP8M4772iKR3ogiJYv/QXj9XpRWFiIxYsX6/p8KmE8pI25IX3MC2ljbkgfc0P6mBfSxtyYSR89ewNTnJwVFRVh1apVGDt2LCZOnIgPP/wQly5dQmVlJQAgLS0Nbrcby5cvR1ZWFqZPn4533nkHlZWVHR6j3aNHDwwcOFD+UW7MvvnmGxQVFeH999/HjBkzMGPGDLz33nvYuXOnfFJXUlKCU6dOYfPmzZg8eTIWLFiAN998E++99x6ampo6r1E4EQgEcPHixQ5PM+vuNo3w0QgStS1F0EeUuEWxyRtR4hZhPBqBCHGLYpM3osQtQh8ygkRty0TWJxhT5jlrbGwEAKSnp0esY7FY0Lt3b1X5xx9/jM2bN2PAgAFYtGgRfvOb3yA1NRXA3fwbaWlpmDZtmlx/+vTpSEtLQ1lZGbKyslBeXo5x48Zh8ODBcp38/Hy0tbWhsrISc+fO7eBLW1sb2tra5N+lTZwyCbVWpPp6XxfJXlVVlfy400SxaZSPyn952UzUtjS7PiLFbXabiTp2jLApwtiRbIkQtyg2lf/ysCdK3CL0IeW/vGwmalt2Z330+GCKjzUqYYxh6dKlqK+vx5dffhmyjsfjwezZs5GdnY3NmzfL5e+99x7uv/9+DBw4ECdPnsS6deswcuRIOVHca6+9hk2bNuHbb79V2Rs9ejSeeuoprFu3Ds888wwuXLiAkpISVR2n04lNmzbh5z//eQd/XnnlFbz66qsdyrds2SInoSYIgiAIgiAIIvG4c+cOnnjiCU0fazTdydlzzz2H48eP48CBAyH/7vV6sWLFCgQCAfzXf/2X6m+rV6+W/z9u3DiMGjUKU6dOxdGjRzFlyhQACPkFPsaYqlxLHSXr1q3Diy++KP/e1NSEoUOHwuVyxfSdM7fbrUpoGA+87YliUwQfRbEpgo9G2BTBR1FsiuCjKDZF8NEImyL4KIpNEXw0wqYIPopiUwQfzWZTz1ejTLU5W7t2LXbs2IH9+/djyJAhHf7u9XqxfPlyfP/999izZ0/Ujc+UKVPgcDhw9uxZTJkyBQMHDsTNmzc71Lt16xYGDBgAABg4cCAOHTqk+nt9fT28Xq9cJxin0wmn09mhXG/SOV6vVWKxWNCvXz8kJSWpEiZ2d5tG+CjBSxsgcdtSBH1EiVsUm0DijR0jbIowdgAx4hbFpgTNbeayp8TMY8cImyL4qMQM+ui6PjMBgUCArVmzhg0ePJh9++23Ieu0t7ezZcuWsbFjx7KamhpNdk+cOMEAsNLSUsYYY6dOnWIA2KFDh+Q6Bw8eZADY6dOnGWOMFRYWMqvVyq5duybX2bp1K3M6nayxsVHTdRsbGxkAzfWVtLe3s7/85S+svb1d92sJYyFtzA3pY15IG3ND+pgb0se8kDbmxkz66NkbmOJpjWvWrMHmzZuxZcsWpKam4saNG7hx4wZaW1sBAD6fD4899hiOHDmCjz/+GH6/X64j5R87f/48fvvb3+LIkSO4cOECCgsL8bOf/QyTJ0/GrFmzAAA/+tGPsHDhQqxevRoHDx7EwYMHsXr1ajz88MPIysoCALhcLowZMwYFBQU4duwYvvjiC/z617/G6tWrdX9E0Qz4/X6cPn0afr8/oWwa4aMRJGpbiqCPKHGLYpM3osQtwng0AhHiFsUmb0SJW4Q+ZASJ2paJrE8wpticvfvuu2hsbMScOXMwaNAg+Wfbtm0AgCtXrmDHjh24cuUKJk2apKpTVlYGAEhKSsIXX3yB/Px8ZGVl4fnnn4fL5cL//d//wWazydf6+OOPMX78eLhcLrhcLkyYMAEfffSR/HebzYZdu3YhOTkZs2bNwvLly7Fs2TL8/ve/79xG4UQgEMCZM2e4P+7U7DaN8NEIErUtRdBHlLhFsckbUeIWYTwagQhxi2KTN6LELUIfMoJEbctE1icYU3znjEV5YOR9990Xtc7QoUNRWloa9Vrp6emqJzyGYtiwYdi5c2dUWwRBEARBEARBELwwxckZQRAEQRAEQRBEokObs26O1WrFsGHDYLXyk1oEm0b4aASJ2pYi6CNK3KLY5I0ocYswHo1AhLhFsckbUeIWoQ8ZQaK2ZSLrE4zpklB3B5qampCWloba2lr5ISJWqxU2mw1+v1/1OVWp3OfzgTEm509YuHAhnE6nXC5hs9lgtVo7ZBqXHufp8/k0lTscDgQCAdUXGi0WC+x2e9jycL5HiynYd1FjCgQCKCwsVOW2ED2m7qSTNHYWL14Mi8XSLWJSlousk8fjQXFxsTx2ukNM3Uknv9+P4uJi5Ofnq76jLXJM3UknaW7Lz89HcnJyt4gp2HdRY2KMdXhfIHpM3Ukn5fsC6X1cV8XU1NSEjIwMMZNQi8z69euxfv16uVOUlJSgR48eAO5+j23y5Mk4fvw4Ll26JL8mKysL2dnZOHz4MG7duiWXX716FcOHD8f+/fvR3Nwsl8+YMQP9+/dHSUmJqrPOnTsXKSkpKCwsVPmUn5+Pr7/+Gjdu3JDL7HY7lixZgtraWpSXl8vlqampmDdvHi5fvoyqqiq5vF+/fpg5cybOnj2LM2fOyOVDhw7FlClTNMc0adIkZGZmhoypb9++2L17t6rDh4tp8eLFaG1txd69e6PG1KtXL8yfP19zTJF0GjFiBADA7XZrikmvTu3t7ZpiiqbTmTNncPbsWU0x6dHpgQcewKBBgzTHFEmnmzdv4vDhw5pj0qqThJ6+F06npKQkFBcXa45Jq07JyclYsGCBrr4XSafS0lK0tLRoikmPToMHD8bAgQO56FRdXQ3gr2MnljkiVEwDBw7ExIkTDdEpljkiVEwTJkxAQ0MDfvjhB0N04hGTlFe0uroaV65ciRoTr3lP71yenp6O3r174/jx41Fj4jXvmWnNra6uRk5ODpe5/MaNG7h27ZohOvFac0eNGoUvvvgCHo8nakxdNZ5yc3MBqN8XJNKaO2rUKIwZM8bUa65EV6+5d+7c6eBTOOjkzADMdHLGGMPu3bs7ZDI32108v9/f4e5TPHdSJB9dLhdSUlJMe3Im+blo0SI4HA4ud7zCnVLEqlNwn+Rxx6utrQ1FRUWyj2Y8OfN6vR3Gjhnv4rW2tqKkpET2k8edSaXmyo9umOnkTDl2LBaLplij6eTxeOB2u5GXlycnGI33DnIgEEBRURFcLpcqYWmsOrW2tso+SolVzXhyJumTl5eHlJSUiDFp0cnn86GkpCRsn4xFp/b2dtnH5ORkLnf6RVhzpT4Z7GN3X3O19EkznJyJsOYG90kep0zdfc2lkzOTECojuc1mUy1+EsFZxqXFJ1z28XCZxoPLpc4Syher1RryM7PhyoN9l/6vNaZI5dJACuWn1lhD+S5dS2tMkcqlwRvKx3h1slgssFgsmmKKVi75HexnvDpJ19KjR7hypS3l33noFMr3aOWhfJTe8Gvtk/GOp2jl0WIK9pOXTnpijRRTKB956RTPHKEsl+o7HA7Zh3h1kuZgu90e0p9YdVK2Jc++F+q68egkvREMLg9XP5rv4fpkLDpJb7ykGwZSuVYfQ5WLsOZG8jFUTJHKRVxzo/VJPTpFastEWHOl/9Oaq23O1gptzgxAmvCbmpp0v9br9eLOnTtoamrSJWRn2RPFpgg+imJTBB+NsCmCj6LYFMFHUWyK4KMRNkXwURSbIvhohE0RfBTFpgg+ms2mtCfQ8oFF2pwZgPT52qFDh3axJwRBEARBEARBmIHm5makpaVFrEPfOTOAQCCAa9euITU1tcN3IaLR1NSEoUOH4vLly1E/k6qV3NxcVFRUcLElkk3e9ozQBkjMtjTCJo0d89pM5LFjhE0Rxg5g/rhFsUlzm3n7kChjxwibIvhoJn0YY2hubsbgwYNDfuxSCZ2cGYDVapWffhUr99xzD7eOZLPZuHZKUWwa4SPAVxsgcdtSBH1EiVsUm4k4doywKcLYAcSIWxSbAM1tZrQnYfaxY4RNEXyUMIs+0U7MJMyd6Y3gwpo1axLSphE+GkGitqUI+ogStyg2eSNK3CKMRyMQIW5RbPJGlLhF6ENGkKhtmcj6KKGPNZoM6TH8Wh61SXQupI25IX3MC2ljbkgfc0P6mBfSxtyIqg+dnJkMp9OJ3/zmN3A6nV3tChEEaWNuSB/zQtqYG9LH3JA+5oW0MTei6kMnZwRBEARBEARBECaATs4IgiAIgiAIgiBMAG3OCIIgCIIgCIIgTABtzgiCIAiCIAiCIEwAbc4IgiAIgiAIgiBMAG3OCIIgCIIgCIIgTABtzgzi9ddfR25uLlJTU9G/f38sW7YMZ86cifiazz77DHl5eejXrx/uuecezJgxA8XFxao6mzZtgsVi6fDj8XiMDKfbEYs++/btC9n2p0+fVtX79NNPMWbMGDidTowZMwbbt283MpRuRyzarFq1KqQ2Y8eOlevQ2OHDu+++iwkTJuCee+6R56ndu3dHfE1paSlycnKQnJyM4cOH4w9/+EOHOjRu+KBXH1p3Og+92tCa07no1YfWna7j9ddfh8ViwQsvvBCxnqhrD23ODKK0tBRr1qzBwYMH4Xa74fP54HK5cPv27bCv2b9/P/Ly8lBYWIjKykrMnTsXjzzyCI4dO6aqd8899+D69euqn+TkZKND6lbEoo/EmTNnVG0/atQo+W/l5eV4/PHHUVBQgK+//hoFBQVYvnw5Dh06ZGQ43YpYtHn77bdVmly+fBnp6en42c9+pqpHYyd+hgwZgjfeeANHjhzBkSNHMG/ePCxduhTV1dUh63///fdYvHgxfvzjH+PYsWN4+eWX8fzzz+PTTz+V69C44YdefWjd6Tz0aiNBa07noFcfWne6hoqKCmzYsAETJkyIWE/otYcRnUJNTQ0DwEpLS3W9bsyYMezVV1+Vf//www9ZWloaZ+8ILfrs3buXAWD19fVh6yxfvpwtXLhQVZafn89WrFjBy9WEI5axs337dmaxWNiFCxfkMho7xtGnTx/2/vvvh/zbP/3TP7Hs7GxV2d///d+z6dOny7/TuDGWSPqEgtadziOSNrTmdD16xg6tO8bT3NzMRo0axdxuN3vooYfYP/zDP4StK/LaQydnnURjYyMAID09XfNrAoEAmpubO7ympaUFmZmZGDJkCB5++OEOdzgJ/ejRZ/LkyRg0aBDmz5+PvXv3qv5WXl4Ol8ulKsvPz0dZWRk/ZxOMWMbOxo0bsWDBAmRmZqrKaezwxe/3Y+vWrbh9+zZmzJgRsk64MXHkyBF4vd6IdWjcxIcWfYKhdadz0KMNrTmdTyxjh9Yd41mzZg2WLFmCBQsWRK0r8tpj79KrJwiMMbz44ouYPXs2xo0bp/l1b775Jm7fvo3ly5fLZdnZ2di0aRPGjx+PpqYmvP3225g1axa+/vpr1UcdCO1o1WfQoEHYsGEDcnJy0NbWho8++gjz58/Hvn378OCDDwIAbty4gQEDBqheN2DAANy4ccPQGLorsYyd69evY/fu3diyZYuqnMYOP06cOIEZM2bA4/GgV69e2L59O8aMGROybrgx4fP5UFtbi0GDBtG44YwefYKhdcdY9GhDa07nE+vYoXXHeLZu3YqjR4+ioqJCU32h154uPbdLEH75y1+yzMxMdvnyZc2v2bJlC+vRowdzu90R6/n9fjZx4kS2du3aeN1MWGLRR+Lhhx9mjzzyiPy7w+FgW7ZsUdXZvHkzczqdcfuZiMSizWuvvcb69u3L2traItajsRM7bW1t7OzZs6yiooK99NJLLCMjg1VXV4esO2rUKPbaa6+pyg4cOMAAsOvXrzPGaNzwRo8+SmjdMZ5YtZGgNcdYYtWH1h1juXTpEuvfvz+rqqqSy6J9rFHktYc+1mgwa9euxY4dO7B3714MGTJE02u2bduGp59+Gn/605+iHt1arVbk5ubi7NmzPNxNOGLRR8n06dNVbT9w4MAOd1xqamo63JkhohOLNowxfPDBBygoKEBSUlLEujR2YicpKQkjR47E1KlT8frrr2PixIl4++23Q9YNNybsdjv69u0bsQ6Nm9jQo48ErTudQyzaKKE1x1hi0YfWHeOprKxETU0NcnJyYLfbYbfbUVpaiv/8z/+E3W6H3+/v8BqR1x7anBkEYwzPPfccPvvsM+zZswf333+/ptd98sknWLVqFbZs2YIlS5Zouk5VVRUGDRoUr8sJRaz6BHPs2DFV28+YMQNut1tVp6SkBDNnzozL30QiHm1KS0tx7tw5PP3005quQ2OHD4wxtLW1hfxbuDExdepUOByOiHVo3PAhkj4ArTtdSTRtgqE1p3PRog+tO8Yzf/58nDhxAlVVVfLP1KlT8bd/+7eoqqqCzWbr8Bqh154uOa9LAJ599lmWlpbG9u3bx65fvy7/3LlzR67z0ksvsYKCAvn3LVu2MLvdztavX696TUNDg1znlVdeYUVFRez8+fPs2LFj7KmnnmJ2u50dOnSoU+MTnVj0eeutt9j27dvZt99+y06ePMleeuklBoB9+umncp2vvvqK2Ww29sYbb7BvvvmGvfHGG8xut7ODBw92anwiE4s2Ek8++SSbNm1aSLs0dviwbt06tn//fvb999+z48ePs5dffplZrVZWUlLCGOuozXfffcd69OjB/vEf/5GdOnWKbdy4kTkcDvbnP/9ZrkPjhh969aF1p/PQqw2tOZ2LXn0kaN3pGoI/1tid1h7anBkEgJA/H374oVxn5cqV7KGHHpJ/f+ihh0K+ZuXKlXKdF154gQ0bNowlJSWxfv36MZfLxcrKyjovsG5CLPr87ne/YyNGjGDJycmsT58+bPbs2WzXrl0dbP/P//wPy8rKYg6Hg2VnZ6sWUiI6sWjDGGMNDQ0sJSWFbdiwIaRdGjt8+MUvfsEyMzPldpw/f7785oWx0Nrs27ePTZ48mSUlJbH77ruPvfvuux3s0rjhg159aN3pPPRqQ2tO5xLL3EbrTtcRvDnrTmuPhTHGOuOEjiAIgiAIgiAIgggPfeeMIAiCIAiCIAjCBNDmjCAIgiAIgiAIwgTQ5owgCIIgCIIgCMIE0OaMIAiCIAiCIAjCBNDmjCAIgiAIgiAIwgTQ5owgCIIgCIIgCMIE0OaMIAiCIAiCIAjCBNDmjCAIgiAIgiAIwgTQ5owgCIIgCIIgCMIE0OaMIAiCIELw4IMP4he/+EWnX7esrAw2mw0LFy7s9GsTBEEQXQttzgiCIAgiCMYYqqqqMGXKlE6/9gcffIC1a9fiwIEDuHTpUqdfnyAIgug6aHNGEARBEEGcPXsWzc3NyMnJidvWnDlzYLFY8Nlnn6nKJ0+eDIvFgn379sllt2/fxp/+9Cc8++yzePjhh7Fp06a4r08QBEGIA23OCIIgCCKIyspK2Gw2TJw4kYu9e++9Fxs2bJB/P3z4MG7dutWh3rZt25CVlYWsrCw8+eST+PDDD8EY4+IDQRAEYX5oc0YQBEEQQRw9ehTZ2dno0aMHF3uPPvoojh07hosXLwIANmzYEPL7bBs3bsSTTz4JAFi4cCFaWlrwxRdfcPGBIAiCMD+0OSMIgiCIICorK7l+3ywpKQkFBQV4//330dzcjO3bt2PlypWqOmfOnMHhw4exYsUKAIDdbsfjjz+ODz74gJsfBEEQhLmxd7UDBEEQBGE2jh07hkcffVT+fdmyZXA6nfjuu+/Q0NCAP//5z7o/8vjMM89g3rx5GDBgAFwuF/r27av6+8aNG+Hz+XDvvffKZYwxOBwO1NfXo0+fPvEFRRAEQZgeOjkjCIIgCAXSBkz5MJDjx49j6tSpqKiowL/+67/iP/7jP3TbHT16NEaNGoWXX34Zq1evVv3N5/Phv//7v/Hmm2+iqqpK/vn666+RmZmJjz/+ON6wCIIgCAGgkzOCIAiCUFBZWQmLxYJJkyYBAFpaWtDW1oYXX3wRAPCjH/0I27dvj8n27373O3z55ZeYO3cuGhsb5fKdO3eivr4eTz/9NNLS0lSveeyxx7Bx40Y899xzsQVEEARBCAOdnBEEQRCEgqNHj2LUqFFITU0FcPfUbOzYsbDZbPLfx48fDwDYtGkTLBaLZtsPPPAAfvWrX3V4zcaNG7FgwYIOGzMA+OlPf4qqqiocPXo01pAIgiAIQbAwekYvQRAEQYTlD3/4A9566y2cPHkSDQ0NWLBgAXbs2IHMzEy88sor2LdvnypXGUEQBEHECn2skSAIgiAicPz4cfzN3/wNZs2ahdbWVrz55pvIzMwEABQXF+Ptt9/uYg8JgiCI7gKdnBEEQRBEBGbPno1PPvkEQ4cO7WpXCIIgiG4OfeeMIAiCICJw9epV2pgRBEEQnQKdnBEEQRAEQRAEQZgAOjkjCIIgCIIgCIIwAbQ5IwiCIAiCIAiCMAG0OSMIgiAIgiAIgjABtDkjCIIgCIIgCIIwAbQ5IwiCIAiCIAiCMAG0OSMIgiAIgiAIgjABtDkjCIIgCIIgCIIwAbQ5IwiCIAiCIAiCMAG0OSMIgiAIgiAIgjABtDkjCIIgCIIgCIIwAbQ5IwiCIAiCIAiCMAH/HwPif2YBXLByAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure( figsize=(10, 5))\n", + "plt.errorbar( I_p, [n.nominal_value for n in Te], yerr=[n.std_dev for n in Te], fmt='k.')\n", + "\n", + "plt.xlabel( '$I_p$, мА')\n", + "plt.ylabel( r'$T_e$, К')\n", + "plt.grid( which='major', linestyle='-')\n", + "plt.grid( which='minor', linestyle='--')\n", + "plt.minorticks_on()\n", + "plt.savefig( 't.pdf')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/3.5.1/Baldin_V/data.csv b/3.5.1/Baldin_V/data.csv new file mode 100644 index 00000000..d4a4c630 --- /dev/null +++ b/3.5.1/Baldin_V/data.csv @@ -0,0 +1,29 @@ +"Uзаж, вольт","Ip, мА","Up, В","Ip2, мА","I+1, мкА","U+1, В","I-1, мкА","U-1, В","I+2, мкА","U+2, В","I-2, мкА","U-2, В","I+3, мкА","U+3, В","I-3, мкА","U-3, В","D,мм","L, мм" +94,2.12,25.8,3.98,40.5,24.6,2.4,0.5,29.6,24,1.7,0.5,23,24,1.2,0.5,0.2,5.2 +99,2.32,25.5,3,39.4,21,4.8,1,28.9,21,3.3,1,22.4,21,2.6,1,, +,2.54,24.8,2.3,38.4,18,6.8,1.5,28.2,18,5,1.5,21.8,18,3.9,1.5,, +,2.74,24,,37.5,15.1,9.1,2,27.5,15,6.6,2,21.1,15.1,5.1,2,, +,2.94,23.1,,36.1,12,16.2,4,26.4,12,12,4,20.1,12.1,9.1,4,, +,3.12,22.4,,34.5,10,21.2,6.1,25.3,10,15.5,6,19.2,10.1,12,6.1,, +,3.33,21.3,,32,8.1,24,8,23.4,8,17.6,8,17.6,8,13.5,8,, +,3.54,20,,27.5,6,25.5,10,20.2,6,18.8,10,14.9,5.9,14.5,10,, +,3.74,18.7,,21.2,4,26.5,12,15.4,4,19.5,12,11.4,4,15.1,12.1,, +,3.93,17.3,,12.6,2,27.4,15.1,9,2,20.1,14.9,6.5,2,15.8,15.1,, +,4.12,15.9,,10.2,1.5,28.1,18,7.2,1.5,20.7,18,5.2,1.5,16.2,18,, +,4.32,14.5,,7.7,1,28.8,21,5.5,1.1,21.3,20.9,3.7,1,16.7,21,, +,4.53,11.6,,5.3,0.5,29.5,24,3.5,0.5,21.8,23.9,2.4,0.5,17.1,24,, +,4.72,8.3,,,,,,,,,,,,,,, +,4.75,8,,,,,,,,,,,,,,, +,4.53,11.6,,,,,,,,,,,,,,, +,4.34,14.3,,,,,,,,,,,,,,, +,4.13,16.2,,,,,,,,,,,,,,, +,3.93,17.5,,,,,,,,,,,,,,, +,3.68,19.4,,,,,,,,,,,,,,, +,3.52,20.1,,,,,,,,,,,,,,, +,3.35,21,,,,,,,,,,,,,,, +,3.1,22.2,,,,,,,,,,,,,,, +,2.91,23,,,,,,,,,,,,,,, +,2.71,23.8,,,,,,,,,,,,,,, +,2.53,24.5,,,,,,,,,,,,,,, +,2.32,25.4,,,,,,,,,,,,,,, +,2.15,25.6,,,,,,,,,,,,,,, diff --git a/3.5.1/Baldin_V/n.pdf b/3.5.1/Baldin_V/n.pdf new file mode 100644 index 00000000..b58280dd Binary files /dev/null and b/3.5.1/Baldin_V/n.pdf differ diff --git a/3.5.1/Baldin_V/t.pdf b/3.5.1/Baldin_V/t.pdf new file mode 100644 index 00000000..9ce29348 Binary files /dev/null and b/3.5.1/Baldin_V/t.pdf differ diff --git a/3.5.1/Baldin_V/vah.pdf b/3.5.1/Baldin_V/vah.pdf new file mode 100644 index 00000000..d7f6e886 Binary files /dev/null and b/3.5.1/Baldin_V/vah.pdf differ diff --git a/3.5.1/Baldin_V/zond.pdf b/3.5.1/Baldin_V/zond.pdf new file mode 100644 index 00000000..1b705580 Binary files /dev/null and b/3.5.1/Baldin_V/zond.pdf differ diff --git a/3.5.1/pdf/Baldin_V.pdf b/3.5.1/pdf/Baldin_V.pdf new file mode 100644 index 00000000..6b46e3e5 Binary files /dev/null and b/3.5.1/pdf/Baldin_V.pdf differ