CFD_from_scratch_Lignell/CFD_from_scratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CFD from scratch\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Governing equations\n",
    "* Pressure\n",
    "* Finite volume formulation\n",
    "* Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Governing Equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reynolds transport theorem (RTT)\n",
    "\n",
    "* The RTT connects a Lagrangian system with an Eulerian control volume. \n",
    "    * By Lagrangian system, we mean a system of a given specified mass, or a *marked mass*.\n",
    "* Conservation laws are written in terms of Lagrangian systems, but we solve CFD problems on Eulerian domains. The RTT connects these.\n",
    "\n",
    "<img src=\"rtt.png\" width=\"350\"/>\n",
    "\n",
    "* The RTT is written as"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\underbrace{\\frac{dB_{sys}}{dt}}_{\\text{Lagrangian system}} = \\underbrace{\\frac{d}{dt}\\int_V\\rho\\beta dV + \\int_A\\rho\\beta\\vec{v}\\cdot\\vec{n}dA}_{\\text{Eulerian control volume}}.$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* $B_{sys}$ is some extensive quantity, like mass, momentum, or energy.\n",
    "* $\\beta = B_{sys}/m$ is intensive.\n",
    "* $\\rho\\beta$ will be mass, momentum, energy, etc. per unit volume.\n",
    "* $\\rho\\beta\\vec{v}$ is $B$-flux, like mass flux, momentum flux, energy flux.\n",
    "* $\\vec{n}$ is a unit normal vector pointing *out* of the surface of a control volume. \n",
    "* In an area dA, $\\rho\\beta\\vec{v}\\cdot\\vec{n}dA$ will be rate of B flowing out of a control volume through that area dA. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mass\n",
    "\n",
    "* $B_{sys} = m$\n",
    "* $\\beta = B_{sys}/m = 1$.\n",
    "* Lagrangian conservation law: mass is conserved, or the rate of change of a given mass is zero:\n",
    "$$\\frac{dm}{dt} = 0.$$\n",
    "* Substitute into the RTT and swap the right and left sides of the equality:\n",
    "<font color=\"blue\">\n",
    "$$\\frac{d}{dt}\\int_V\\rho dV + \\int_A\\rho\\vec{v}\\cdot\\vec{n}dA = 0.$$\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Momentum\n",
    "* $B_{sys} = m\\vec{v}$\n",
    "* $\\beta = B_{sys}/m = \\vec{v}$\n",
    "* Lagrangian conservation law: the rate of change of momentum of a fixed mass (system) is the sum of the external forces on the mass (system).\n",
    "    * We have surface forces $\\vec{F}$, and body forces (denoted with external field $\\vec{g}$, nominally gravitational acceleration):\n",
    "$$\\frac{dm\\vec{v}}{dt} = \\int_A \\vec{F}dA + \\int_V\\vec{g}\\rho dV.$$\n",
    "    * Consider viscous and pressure forces, so that $\\vec{F} = -\\boldsymbol{\\tau}\\cdot\\vec{n} - P\\boldsymbol{\\delta}\\cdot{\\vec{n}}$, where $\\boldsymbol{\\tau}$ is the viscous stress tensor, and $\\boldsymbol{\\delta}$ is the unit tensor. \n",
    "        * (The negative sign is because $\\vec{n}$ points *out* of the suface, and we want the force on or into the surface.) \n",
    "    * This gives\n",
    "$$\\frac{dm\\vec{v}}{dt} = -\\int_A\\boldsymbol{\\tau}\\cdot\\vec{n}dA -\\int_AP\\boldsymbol{\\delta}\\cdot\\vec{n}dA+ \\int_V\\vec{g}\\rho dV.$$\n",
    "* Substitute into the RTT and swap the right and left sides of the equality:\n",
    "<font color=\"blue\">\n",
    "$$\\frac{d}{dt}\\int_V\\rho\\vec{v}dV + \\int_A\\rho\\vec{v}\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\boldsymbol{\\tau}\\cdot\\vec{n}dA -\\int_AP\\boldsymbol{\\delta}\\cdot\\vec{n}dA+ \\int_V\\vec{g}\\rho dV.$$\n",
    "</font>\n",
    "* Here, $\\vec{v}\\vec{v}$ is a tensor, and can be written as $\\vec{v}\\otimes\\vec{v}$, or $v_iv_j$ in index notation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy\n",
    "* $B_{sys} = E$ (where $E = mu + \\frac{1}{2}m\\vec{v}\\cdot\\vec{v}$ is internal + kinetic energy).\n",
    "* $\\beta = E/m = e$.\n",
    "* Lagrangian conservation law: the rate of change of energy of a given mass is the sum of the heat transfered to the mass and the work performed on the mass:\n",
    "$$\\frac{dE}{dt} = -\\int_A\\vec{q}\\cdot\\vec{n}dA + \\int_A\\vec{F}\\cdot\\vec{v}dA + \\int_V\\rho\\vec{g}\\cdot\\vec{v}dV.$$\n",
    "    * Here, $\\vec{q}$ is the heat flux vector. As before, $\\vec{F} = -\\boldsymbol{\\tau}\\cdot\\vec{n} - P\\boldsymbol{\\delta}\\cdot{\\vec{n}}$. \n",
    "    * The symmetry of $\\boldsymbol{\\tau}$ and $\\boldsymbol{\\delta}$ let us write $\\boldsymbol{\\tau}\\cdot\\vec{n}\\cdot\\vec{v} = \\boldsymbol{\\tau}\\cdot\\vec{v}\\cdot\\vec{n}$ and $P\\boldsymbol{\\delta}\\cdot\\vec{n}\\cdot\\vec{v} = P\\boldsymbol{\\delta}\\cdot\\vec{v}\\cdot\\vec{n}$.\n",
    "* Substitute into the RTT and swap the right and left sides of the equality:\n",
    "<font color=\"blue\">\n",
    "$$\\frac{d}{dt}\\int_V\\rho edV + \\int_A\\rho e\\vec{v}\\cdot\\vec{n}dA = \n",
    "-\\int_A\\vec{q}\\cdot\\vec{n}dA - \\int_A(\\boldsymbol{\\tau}\\cdot\\vec{v})\\cdot\\vec{n}dA - \\int_A(P\\boldsymbol{\\delta}\\cdot\\vec{v})\\cdot\\vec{n}dA+ \\int_V\\rho\\vec{g}\\cdot\\vec{v}dV.$$\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equation of state and constitutive relations\n",
    "* The above (blue) equations for mass, momentum and energy are three equations in $\\rho$, $\\vec{v}$ and $e$.\n",
    "* An equation of state is used to relate these quantities to pressure $P$.\n",
    "    * For, example, the ideal gas law:\n",
    "$$P = \\frac{\\rho R T}{M}$$\n",
    "    * Here, $R$ is the universal gas constant, $M$ is the mean molecular weight, and $T$ is temperature.\n",
    "* Thermodynamic relations will relate $T$ to $e$, $\\vec{v}$, and $P$. For ideal gases, $T$ is a function of internal energy $e-\\frac{1}{2}\\vec{v}\\cdot\\vec{v}$.\n",
    "* Consitutive relations are also required to write $\\vec{q}$, and $\\boldsymbol{\\tau}$ in terms of $\\rho$, $\\vec{v}$, $e$, $P$."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Differential form\n",
    "\n",
    "* The above equations are in integral form, which is convenient for a Finite Volume solution. (It is also convenient for derivation.)\n",
    "* We can find the differential form as follows.\n",
    "    * If the control volume is fixed in time, we can move $d/dt$ inside the volume integral.\n",
    "    * Replace integrals over the surface area with volume integrals by applying the Gauss Divergence Theorem:\n",
    "    $$\\int_A\\vec{v}\\cdot\\vec{n}dA = \\int_V\\nabla\\cdot\\vec{v},$$\n",
    "    where $\\vec{v}$ is some vector (not necessarily velocity).\n",
    "        * Hence, in the equation for mass we have $\\int_A\\rho\\vec{v}\\cdot\\vec{n}dA = \\int_V\\nabla\\cdot(\\rho\\vec{v})dV$.\n",
    "    * Combine all the volume integrals into $\\int_V(\\text{all terms})dV = 0$. Since the volume integrated over is arbitrary, this equation can only be true if the integrand $(\\text{all terms})$ itself is 0. This gives the final result. (Also, $\\nabla\\cdot(P\\boldsymbol{\\delta}) = \\nabla P$.)\n",
    "    \n",
    "\\begin{align}\n",
    "\\text{mass:} \\phantom{xxxx}& \\frac{d\\rho}{dt} + \\nabla\\cdot(\\rho\\vec{v}) = 0\\\\\n",
    "\\text{momentum:}\\phantom{xxxx} & \\frac{d\\rho\\vec{v}}{dt} + \\nabla\\cdot(\\rho\\vec{v}\\vec{v}) = -\\underbrace{\\nabla\\cdot\\boldsymbol{\\tau}}_{\\text{viscous forces}} - \\underbrace{\\nabla P}_{\\text{pressure forces}} + \\underbrace{\\rho\\vec{g}}_{\\text{gravitational forces}} \\\\\n",
    "\\text{energy:}\\phantom{xxxx} & \\frac{d\\rho e}{dt} + \\nabla\\cdot(\\rho e\\vec{v}) = -\\underbrace{\\nabla\\cdot\\vec{q}}_{\\text{heat flux}} - \\underbrace{\\nabla\\cdot(\\boldsymbol{\\tau}\\cdot\\vec{v})}_{\\text{viscous heating}} -\\underbrace{\\nabla\\cdot(P\\vec{v})}_{\\text{PV work}} + \\underbrace{\\rho\\vec{g}\\cdot\\vec{v}}_{\\text{field work}}\n",
    "\\end{align}\n",
    "\n",
    "* The terms on the left hand side (LHS) of the equation are the accumulation and in/out transport through the control volume.\n",
    "* The terms on the right hand side (RHS) are as noted. In the energy equation, field work will convert potential energy to kinetic energy (which is part of $e$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pressure and compressibility\n",
    "* The governing equations, along with equation of state and constitutive, define the system. \n",
    "    * Oh, and boundary conditions too, don't forget those.\n",
    "* These are the compressible flow equations. \n",
    "* Pressure waves will be part of the solution.\n",
    "* Those waves travel at the sound speed (347 m/s in at at 1 atm, 300 K). \n",
    "* If we are at low speed relative to the acoustic waves (low Mach), then the flow is largely unimpacted by these waves and we call the flow incompressible.\n",
    "    * From the Bernoulli equation, ignoring gravity, we have\n",
    "    $$\\Delta P = \\frac{\\rho\\Delta v^2}{2}.$$\n",
    "    * Take pressures relative to stagnation, so $\\Delta v=v$ (most flows have $v=0$ somewhere). Using $c^2=\\gamma P/\\rho$, where $\\gamma = c_p/c_v$, and $Ma = v/c$, we have\n",
    "    $$\\frac{\\Delta P}{P} = \\frac{\\gamma}{2}Ma^2.$$\n",
    "    * For v=100 m/s, $\\Delta P/P_{atm}$ is 6%. (100 m/s is 233 mph and gives Ma=0.288 for air at 1 atm.)\n",
    "    * For v=347 m/s, $\\Delta P/P_{atm}$ is 70%. (347 m/s is 776 mph and gives Ma=1 for air at 1 atm.)\n",
    "* Flows are often considered incompressible for $Ma<0.3$.\n",
    "* Flows can have large density changes and still be considered incompressible. Heat release and chemical mixing in gases can cause large density variations, but at low speed, the changes in velocity themselves only slightly compress the fluid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Low-Mach flows\n",
    "* At low speeds, acoustic waves do not alter the thermodynamic state or affect the velocity fields. \n",
    "* But for unsteady, explicit flow solvers, we are required to take timesteps based on the smallest timescales. Here, those correspond to acoustic waves, or rather, $v+c$. \n",
    "* This is a numerically *stiff* problem: we have to take timesteps for stability that are much smaller than we need for accuracy (for capturing the relevant physics, of which acoustics don't contribute.)\n",
    "    * The ratio of the number of timesteps needed for stability to the ideal number for accuracy is $(v+c)/v = 1+1/Ma$. \n",
    "        * This is based on a CFL condition: accurate timestep sizes will be smaller than the convective time across a given grid cell: $\\Delta t < \\alpha\\Delta x/v$ for accuracy, but $\\Delta t < \\alpha\\Delta x/(v+c)$ for stability, where $\\alpha$ is a constant (the CFL) determined by the method used. \n",
    "    * At Ma=0.1 (34.7 m/s or 77.6 mph in air at 1 atm, 300 K), we would need 11 times more steps for a stable solution than we need for an acurate solution. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incompressible flow\n",
    "* The numerical stiffness for low-Mach flows can be explicitly removed from the equations by assuming the flow is incompressible and using a so-called pressure projection method.\n",
    "* The density is assumed constant, and the energy equation is no longer needed.\n",
    "* The resulting equations, ignoring gravity, and taking $\\hat{\\tau}=\\tau/\\rho$, and $\\hat{P} = P/\\rho$ are\n",
    "<font color=\"blue\">\n",
    "\\begin{align}\n",
    "\\text{mass:}\\phantom{xxxx} & \\int_A\\vec{v}\\cdot\\vec{n}dA = 0 \\\\\n",
    "\\text{momentum:}\\phantom{xxxx} & \\frac{d}{dt}\\int_V\\vec{v}dV + \\int_A\\vec{v}\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\hat{\\boldsymbol{\\tau}}\\cdot\\vec{n}dA -\\int_A\\hat{P}\\boldsymbol{\\delta}\\cdot\\vec{n}dA.\n",
    "\\end{align}\n",
    "</font>\n",
    "* These are two equations in $\\vec{v}$ and $P$. \n",
    "* The momentum equation gives the equation for $\\vec{v}$, but there is no explicit pressure equation. The mass equation (continuity) provides a constraint on the velocities.\n",
    "* Pressure can be thought of as a scalar field that constrains velocity components given by the momentum equation to satisfy continuity.\n",
    "* A pressure equation is derived by effectively taking the divergence of the momentum equation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pressure equation\n",
    "* Consider the differential form of the mass and momentum equations, where we drop the vector arrows for simplicity:\n",
    "\\begin{align}\n",
    "\\text{mass:}\\phantom{xxxx} & \\nabla\\cdot v = 0 \\\\\n",
    "\\text{momentum:}\\phantom{xxxx} & \\frac{dv}{dt} = -\\nabla\\cdot vv - \\nabla\\cdot\\hat{\\boldsymbol{\\tau}} - \\nabla\\hat{P}\n",
    "\\end{align}\n",
    "* Apply a simple Explicit Euler integration over one timestep $h=\\Delta t$ to the momentum equation:\n",
    "$$v^{n+1} = \\underbrace{v^n + h\\left(-\\nabla\\cdot vv-\\nabla\\cdot\\hat{\\boldsymbol{\\tau}}\\right)^n}_{H^n}-h\\nabla\\hat{P}^{n+1}.$$\n",
    "    * Note that $\\hat{P}$ is taken at step $n+1$ (keep reading).\n",
    "* We use $\\nabla\\cdot v=0$ applied to this $v^{n+1}$ equation to get an equation for $\\hat{P}^{n+1}$:\n",
    "<font color=\"blue\">\n",
    "$$\\nabla^2\\hat{P}^{n+1} = \\frac{1}{h}\\nabla\\cdot H^n.$$\n",
    "</font>\n",
    "* **To advance a given step, we solve this equation for $\\hat{P}$, and then use this in the momentum advancement equation for $v^{n+1}$ above:**\n",
    "<font color=\"blue\">\n",
    "$$v^{n+1} = H^n - h\\nabla\\hat{P}^{n+1}.$$\n",
    "</font>\n",
    "* This velocity satisfies continuity by construction.\n",
    "\n",
    "**In practice, the pressure equation is determined from the discretized version of the momentum and continuity equations, discussed later.**\n",
    "\n",
    "#### Boundary conditions for the pressure equation\n",
    "* The pressure equation requires boundary conditions. These are best determined from the momentum equations evaluated on the boundary. \n",
    "* There appears to be ongoing discussion of the boundary condition treatment for the pressure equation. See for example \n",
    "    * [Vremen 2014](https://linkinghub.elsevier.com/retrieve/pii/S002199911400062X)\n",
    "    * [Rempfer 2003](https://www.annualreviews.org/doi/abs/10.1146/annurev.fluid.35.030602.113908?journalCode=fluid)\n",
    "    * [Sani 2006](https://onlinelibrary.wiley.com/doi/10.1002/fld.1062)\n",
    "        * Along with [some comentantary by Rempfer](https://onlinelibrary.wiley.com/doi/pdf/10.1002/fld.1750)\n",
    "        * And a [reply by Sani](https://onlinelibrary.wiley.com/doi/pdf/10.1002/fld.1762)\n",
    "* In my opinion, the pressure treatment is not so complicated. The simplicity of inserting the velocities into the continuity equation, with boundary velocities taken along for the ride, is pretty straightforward. Pressure boundary conditions are effectively built-in. See below.        \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Solution-Finite Volume Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To solve the above equations, we'll use a finite volume method. \n",
    "* Assume a 2-dimensional rectangular domain. \n",
    "    * We divide the domain into grid cells of size $\\Delta x$ by $\\Delta y$. \n",
    "    * $\\Delta x\\Delta y$ is the volume of a grid cell.\n",
    "    * Cell face areas are $\\Delta x$ and $\\Delta y$.\n",
    "<img src=\"fig_1.png\" width=\"250\"/>\n",
    "    * cell faces are labeled as e, w, n, s for east, west, north, and south, respectively.\n",
    "    * The x-direction (horizontal) is indexed with *i* and the y direction (vertical) is indexed with $j$.\n",
    "* We assume all properties are uniform inside a given cell, and we assume all properties are uniform along a given face.\n",
    "    * With these two assumptions, we integrate the mass and momentum equations over a given cell."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Momentum components\n",
    "\\begin{align}\n",
    "\\text{u-mom:}\\phantom{xxxx}&\\frac{d}{dt}\\int_VudV + \\int_Au\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\hat{\\boldsymbol{\\tau}}_x\\cdot\\vec{n}dA -\\int_A\\hat{P}\\vec{i}\\cdot\\vec{n}dA, \\\\\n",
    "\\text{v-mom:}\\phantom{xxxx}&\\frac{d}{dt}\\int_VvdV + \\int_Av\\vec{v}\\cdot\\vec{n}dA = -\\int_A\\hat{\\boldsymbol{\\tau}}_y\\cdot\\vec{n}dA -\\int_A\\hat{P}\\vec{j}\\cdot\\vec{n}dA.\n",
    "\\end{align}\n",
    "Here, $\\vec{i}$ and $\\vec{j}$ are the unit coordinate vectors in the $x$ and $y$ directions, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Integrate the u-mom equation over a given grid cell.\n",
    "* The first term is\n",
    "$$\\frac{d}{dt}\\int_VudV \\rightarrow \\frac{d}{dt}u\\int_VdV = \\frac{duV}{dt} = \\frac{du\\Delta x\\Delta y}{dt} = \\Delta x\\Delta y\\frac{du}{dt}.$$\n",
    "* The area integrals are illustrated considering the second term, where $\\vec{n}=\\pm\\vec{i}$ or $\\vec{n}=\\pm\\vec{j}$:\n",
    "\n",
    "\\begin{align}\n",
    "\\int_Au\\vec{v}\\cdot\\vec{n}dA &= \n",
    "\\int_{A_e}u\\vec{v}\\cdot\\vec{i}dA - \n",
    "\\int_{A_w}u\\vec{v}\\cdot\\vec{i}dA + \n",
    "\\int_{A_n}u\\vec{v}\\cdot\\vec{j}dA - \n",
    "\\int_{A_s}u\\vec{v}\\cdot\\vec{j}dA \\\\\n",
    "&= u_eu_e\\Delta y - u_wu_w\\Delta y + u_nv_n\\Delta x - u_sv_s\\Delta x.\n",
    "\\end{align}\n",
    "\n",
    "* Considering all terms in the u-mom equation and dividing through by $\\Delta x\\Delta y$ gives\n",
    "\n",
    "$$\\frac{du}{dt} = -\\frac{u_eu_e - u_wu_w}{\\Delta x} - \\frac{u_nv_n - u_sv_s}{\\Delta y}\n",
    "- \\frac{\\hat{\\tau}_{xx,e}-\\hat{\\tau}_{xx,w}}{\\Delta x} \n",
    "- \\frac{\\hat{\\tau}_{xy,n}-\\hat{\\tau}_{xy,s}}{\\Delta y}\n",
    "- \\frac{\\hat{P}_e-\\hat{P}_w}{\\Delta x}$$\n",
    "\n",
    "* The quantities at faces interpolated between values at neighboring cells.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checkerboarding\n",
    "* Consider in particular the face pressures:\n",
    "$$\\hat{P}_e = \\frac{\\hat{P}_{i+1,j}+\\hat{P}_{i,j}}{2},$$\n",
    "$$\\hat{P}_w = \\frac{\\hat{P}_{i,j}+\\hat{P}_{i-1,j}}{2}.$$\n",
    "* Inserting these into the u-mom equation gives:\n",
    "$$-\\frac{\\hat{P}_e-\\hat{P}_w}{\\Delta x} = -\\frac{\\hat{P}_{i+1,j}+\\hat{P}_{i,j}-\\hat{P}_{i,j}-\\hat{P}_{i-1,j}}{2\\Delta x} = -\\frac{\\hat{P}_{i+1,j}-\\hat{P}_{i-1,j}}{2\\Delta x}.$$\n",
    "* Note that for the given *i,j* cell, $u_{i,j}$ depends on the two neighboring pressures, but not on $\\hat{P}_{i,j}$.\n",
    "* This effectively decouples $u$ from $\\hat{P}$.\n",
    "* Consider a one-dimensional grid with the following pressure values\n",
    "\n",
    "```\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "```\n",
    "\n",
    "* For any given cell, pressure term will cancel since (8-8)=0 and (2-2)=0. Hence, this pressure field will not affect the velocity. \n",
    "* This is clearly unphysical since there is an alternative pressure gradient that should drive velocity.\n",
    "* In two dimensions, we could have something like:\n",
    "\n",
    "```\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  6  |  4  |  6  |  4  |  6  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  6  |  4  |  6  |  4  |  6  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "|  8  |  2  |  8  |  2  |  8  |\n",
    "|-----|-----|-----|-----|-----|\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Staggered grid\n",
    "* The decoupling of velocity and pressure can cause instability. Numerical error/noise can be preserved and amplified.\n",
    "* To remedy this, a staggered grid is used.\n",
    "    * A u-grid is formed by shifting all grid cells left by half a cell.\n",
    "        * u-velocity components are stored at the cell centers of this shifted u-grid.\n",
    "        * The u-momentum equation is integrated over the u-cells.\n",
    "    * A v-grid is formed by shifting all grid cells down by half a cell\n",
    "        * v-velocity components are stored at the cell centers of this shifted v-grid.\n",
    "        * The v-momentum equation is integrated over the u-cells.\n",
    "    * Pressure is stored on the regular grid. \n",
    "        * The continuity equation is integrated over the P-cells.\n",
    "    \n",
    "<img src=\"fig_2.png\" width=\"600\"/>\n",
    "\n",
    "Note the overlap of the cells:\n",
    "<img src=\"fig_3.png\" width=\"100\"/>\n",
    "* In the u-momentum equation, $\\hat{P}_e$ and $\\hat{P}_w$ are needed. Now, rather than interpolate from neighboring cells, $\\hat{P}_e$ is simply $\\hat{P}_{i,j}$, and $\\hat{P}_w$ is just $\\hat{P}_{i-1,j}$, where indexing on $\\hat{P}$ is with respect to the P-grid.\n",
    "* Similarly, in the v-momentum equation, $\\hat{P}_n$, and $\\hat{P}_s$ appear, and these are available directly, without interpolation. \n",
    "* This precludes any checkerboarding since momentum components are now driven by pressure differences in adjacent cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### u-mom equation\n",
    "\n",
    "* The FV equation for u above is still correct, but is now applied to the u-grid, and all face quantities are with respect to a given u-cell.\n",
    "* We will still interpolate values to faces, as needed with $u$, $v$, and $P$ cell values refering to the respective u-grid, v-grid, and P-grid.\n",
    "* Note the definition of $\\hat{\\tau}$ for incompressible Newtonian fluids below. Don't confuse $\\nu$ (kinematic viscosity) with $v$.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\frac{du}{dt} = \\underbrace{-\\frac{u_eu_e - u_wu_w}{\\Delta x} - \\frac{u_nv_n - u_sv_s}{\\Delta y}\n",
    "- \\frac{\\hat{\\tau}_{xx,e}-\\hat{\\tau}_{xx,w}}{\\Delta x} \n",
    "- \\frac{\\hat{\\tau}_{xy,n}-\\hat{\\tau}_{xy,s}}{\\Delta y}}_{H^u}\n",
    "- \\frac{\\hat{P}_e-\\hat{P}_w}{\\Delta x}.$$\n",
    "\n",
    "<font color=\"blue\">\n",
    "$$\\frac{du}{dt} = H^u - \\frac{\\hat{P}_e - \\hat{P}_w}{\\Delta x}.$$\n",
    "</font>\n",
    "\n",
    "\n",
    "<font color=\"blue\">\n",
    "\\begin{align}\n",
    "&u_e = \\frac{u_{i+1,j}+u_{i,j}}{2}, \\\\\n",
    "&u_w = \\frac{u_{i,j}+u_{i-1,j}}{2}, \\\\\n",
    "&u_n = \\frac{u_{i,j+1}+u_{i,j}}{2}, \\\\\n",
    "&u_s = \\frac{u_{i,j}+u_{i,j-1}}{2}, \\\\\n",
    "&v_n = \\frac{v_{i,j+1}+v_{i-1,j+1}}{2}, \\\\\n",
    "&v_s = \\frac{v_{i,j}+v_{i-1,j}}{2}, \\\\\n",
    "&\\hat{\\tau}_{xx,e} = -2\\nu\\left(\\frac{\\partial u}{\\partial x}\\right)_e = -2\\nu\\left(\\frac{u_{i+1,j}-u_{i,j}}{\\Delta x}\\right), \\\\\n",
    "&\\hat{\\tau}_{xx,w} = -2\\nu\\left(\\frac{\\partial u}{\\partial x}\\right)_w = -2\\nu\\left(\\frac{u_{i,j}-u_{i-1,j}}{\\Delta x}\\right), \\\\\n",
    "&\\hat{\\tau}_{xy,n} = -\\nu\\left(\\frac{\\partial u}{\\partial y}\\right)_n \n",
    "                     -\\nu\\left(\\frac{\\partial v}{\\partial x}\\right)_n =  \n",
    "-\\nu\\left(\\frac{u_{i,j+1}-u_{i,j}}{\\Delta y}\\right)\n",
    "-\\nu\\left(\\frac{v_{i,j+1}-v_{i-1,j+1}}{\\Delta x}\\right), \\\\\n",
    "&\\hat{\\tau}_{xy,s} = -\\nu\\left(\\frac{\\partial u}{\\partial y}\\right)_s \n",
    "                     -\\nu\\left(\\frac{\\partial v}{\\partial x}\\right)_s =  \n",
    "-\\nu\\left(\\frac{u_{i,j}-u_{i,j-1}}{\\Delta y}\\right)\n",
    "-\\nu\\left(\\frac{v_{i,j}-v_{i-1,j}}{\\Delta x}\\right), \\\\\n",
    "&\\hat{P}_e = \\hat{P}_{i,j}, \\\\\n",
    "&\\hat{P}_w = \\hat{P}_{i-1,j}.\n",
    "\\end{align}\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### v-mom equation\n",
    "\n",
    "* We can follow the same procedure for the v-mom equation as for the u-mom equation, but applied to the v-grid. When interpolating face values, we again write quantities with respect to their associated grids.\n",
    "* There is a direct symmetry, however, and we can use the u-mom eqution with the following replacements:\n",
    "    * $u\\rightarrow v$\n",
    "    * $e\\rightarrow n$\n",
    "    * $w\\rightarrow s$\n",
    "    * $n\\rightarrow e$\n",
    "    * $s\\rightarrow w$\n",
    "    * $x\\rightarrow y$\n",
    "    * $y\\rightarrow x$\n",
    "    * subscripts: $u_{i+1,j}\\rightarrow v_{i,j+1}$, for example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\frac{dv}{dt} = \\underbrace{-\\frac{v_nv_n - v_sv_s}{\\Delta y} - \\frac{v_eu_e - v_wu_w}{\\Delta x}\n",
    "- \\frac{\\hat{\\tau}_{yy,n}-\\hat{\\tau}_{yy,s}}{\\Delta y} \n",
    "- \\frac{\\hat{\\tau}_{yx,e}-\\hat{\\tau}_{yx,w}}{\\Delta x}}_{H^v}\n",
    "- \\frac{\\hat{P}_n-\\hat{P}_s}{\\Delta y}.$$\n",
    "\n",
    "<font color=\"red\">\n",
    "$$\\frac{dv}{dt} = H^v - \\frac{\\hat{P}_n - \\hat{P}_s}{\\Delta y}.$$\n",
    "</font>\n",
    "\n",
    "\n",
    "<font color=\"red\">\n",
    "\\begin{align}\n",
    "&v_n = \\frac{v_{i,j+1}+v_{i,j}}{2}, \\\\\n",
    "&v_s = \\frac{v_{i,j}+v_{i,j-1}}{2}, \\\\\n",
    "&v_e = \\frac{v_{i+1,j}+v_{i,j}}{2}, \\\\\n",
    "&v_w = \\frac{v_{i,j}+v_{i-1,j}}{2}, \\\\\n",
    "&u_e = \\frac{u_{i+1,j}+u_{i+1,j-1}}{2}, \\\\\n",
    "&u_w = \\frac{u_{i,j}+u_{i,j-1}}{2}, \\\\\n",
    "&\\hat{\\tau}_{yy,n} = -2\\nu\\left(\\frac{\\partial v}{\\partial y}\\right)_n = -2\\nu\\left(\\frac{v_{i,j+1}-v_{i,j}}{\\Delta y}\\right), \\\\\n",
    "&\\hat{\\tau}_{yy,s} = -2\\nu\\left(\\frac{\\partial v}{\\partial y}\\right)_s = -2\\nu\\left(\\frac{v_{i,j}-v_{i,j-1}}{\\Delta y}\\right), \\\\\n",
    "&\\hat{\\tau}_{yx,e} = -\\nu\\left(\\frac{\\partial v}{\\partial x}\\right)_e \n",
    "                     -\\nu\\left(\\frac{\\partial u}{\\partial y}\\right)_e =  \n",
    "-\\nu\\left(\\frac{v_{i+1,j}-v_{i,j}}{\\Delta x}\\right)\n",
    "-\\nu\\left(\\frac{u_{i+1,j}-u_{i+1,j-1}}{\\Delta y}\\right), \\\\\n",
    "&\\hat{\\tau}_{yx,w} = -\\nu\\left(\\frac{\\partial v}{\\partial x}\\right)_w \n",
    "                     -\\nu\\left(\\frac{\\partial u}{\\partial y}\\right)_w =  \n",
    "-\\nu\\left(\\frac{v_{i,j}-v_{i-1,j}}{\\Delta x}\\right)\n",
    "-\\nu\\left(\\frac{u_{i,j}-u_{i,j-1}}{\\Delta y}\\right), \\\\\n",
    "&\\hat{P}_n = \\hat{P}_{i,j}, \\\\\n",
    "&\\hat{P}_s = \\hat{P}_{i,j-1}.\n",
    "\\end{align}\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuity\n",
    "* The mass balance is given by\n",
    "$$\\int_A\\vec{v}\\cdot\\vec{n}dA = 0.$$\n",
    "* Integrate this over the P-cell to give\n",
    "$$(u_e-u_w)\\Delta y + (v_n-v_s)\\Delta x = 0.$$\n",
    "\\begin{align}\n",
    "&u_e = u_{i+1,j}, \\\\\n",
    "&u_w = u_{i,j}, \\\\\n",
    "&v_n = v_{i,j+1}, \\\\\n",
    "&v_s = v_{i,j}. \\\\\n",
    "\\end{align}\n",
    "Hence,\n",
    "$$(u_{i+1,j}-u_{i,j})\\Delta y + (v_{i,j+1}-v_{i,j})\\Delta x = 0.$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discrete pressure equation\n",
    "* Apply a simple Explicit Euler step of size $\\Delta t=h$ to the $u$ and $v$ momentum equations for *i,j* pairs appearing in the above continuity equation:\n",
    "\n",
    "\\begin{align}\n",
    "u_{i+1,j}^{n+1} &= u_{i+1,j}^n + hH_{i+1,j}^u - \\frac{h}{\\Delta x}(\\hat{P}_{i+1,j}-\\hat{P}_{i,j}), \\\\\n",
    "u_{i,j}^{n+1}   &= u_{i,j}^n + hH_{i,j}^u - \\frac{h}{\\Delta x}(\\hat{P}_{i,j}-\\hat{P}_{i-1,j}), \\\\\n",
    "v_{i,j+1}^{n+1} &= v_{i,j+1}^n + hH_{i,j+1}^v - \\frac{h}{\\Delta y}(\\hat{P}_{i,j+1}-\\hat{P}_{i,j}), \\\\\n",
    "v_{i,j}^{n+1}   &= v_{i,j}^n + hH_{i,j}^v - \\frac{h}{\\Delta y}(\\hat{P}_{i,j}-\\hat{P}_{i,j-1}).\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We insert these into the continuity equation, and put terms involving $\\hat{P}$ on the left hand side, and all other terms on the right hand side. \n",
    "$$\n",
    "\\hat{P}_{i,j-1}\\underbrace{\\left[-\\frac{h\\Delta x}{\\Delta y}\\right]}_{v_s} + \n",
    "\\hat{P}_{i-1,j}\\underbrace{\\left[-\\frac{h\\Delta y}{\\Delta x}\\right]}_{u_w} + \n",
    "\\hat{P}_{i,j}\\underbrace{  \\left[\\frac{2h\\Delta y}{\\Delta x} + \\frac{2h\\Delta x}{\\Delta y}\\right]}_{(u_e,u_w) + (v_n,v_s)} + \n",
    "\\hat{P}_{i+1,j}\\underbrace{\\left[-\\frac{h\\Delta y}{\\Delta x}\\right]}_{u_e} + \n",
    "\\hat{P}_{i,j+1}\\underbrace{\\left[-\\frac{h\\Delta x}{\\Delta y}\\right]}_{v_n}  = \\\\\n",
    "\\underbrace{\\Delta x(v_{i,j}^n+hH_{i,j}^v)}_{v_s}\n",
    "+\\underbrace{\\Delta y(u_{i,j}^n+hH_{i,j}^u)}_{u_w}\n",
    "-\\underbrace{\\Delta y(u_{i+1,j}^n+hH_{i+1,j}^u)}_{u_e}\n",
    "-\\underbrace{\\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)}_{v_n}\n",
    "$$\n",
    " * The terms are labeled to show which velocity components they arise from. This is convenient for dealing with boundary cells.\n",
    "* This is a linear system of equations in the form $A\\hat{P}=b$, where $A$ is a matrix and $\\hat{P}$ and $b$ are vectors.\n",
    "    * $A$ is the matrix of coefficients of $\\hat{P}$.\n",
    "        * There is a row in $A$ for each *i,j* cell in the grid.\n",
    "        * More on this below...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In the Cartesian grid shown, there are nine types of cells:\n",
    "    * **Corner** cells for the top-right, top-left, bottom-right, and bottom-left.\n",
    "    * **Edge** cells for the top, bottom, left, and right.\n",
    "    * **Interior** cells.\n",
    "    \n",
    "<img src=\"fig_4.png\" width=\"250\"/>\n",
    "\n",
    "* We have a P-equation for each type of cell. \n",
    "    * The equations differ in which velocity making up the equation is on a boundary face. \n",
    "    * For a cell with a velocity on a face, like and ET cell, the coefficient of $\\hat{P}_{i,j+1}$ is zero, the second term of the coefficient of $\\hat{P}_{i,j}$ is decreased (factor 2 $\\rightarrow 1$) and the corresponding term on the right hand side of the P-equation is replaced with the known face velocity: $-\\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)\\rightarrow -\\Delta xv_n$, where $v_n$ here is the value on the top boundary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**$\\hat{P}$ coefficients for each cell type.**\n",
    "\n",
    "|Type| $P_{i,j-1}$ | $P_{i-1,j}$ | $P_{i,j}$ | $P_{i+1,j}$ | $P_{i,j+1}$ |\n",
    "|-------|-------|-------|-------|-------|-------|\n",
    "|I | $-\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{2h\\Delta y}{\\Delta x}+\\frac{2h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|CBL | $0$ | $0$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|EB |$0$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{2h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|CBR|$0$| $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $0$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|EL | $-\\frac{h\\Delta x}{\\Delta y}$ | $0$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{2h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|ER| $-\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{2h\\Delta x}{\\Delta y}$ | $0$ | $-\\frac{h\\Delta x}{\\Delta y}$ |\n",
    "|CTL | $-\\frac{h\\Delta x}{\\Delta y}$ | $0$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $0$ |\n",
    "|ET | $-\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{2h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $0$ |\n",
    "|CTR | $-\\frac{h\\Delta x}{\\Delta y}$ | $-\\frac{h\\Delta y}{\\Delta x}$ | $\\frac{h\\Delta y}{\\Delta x}+\\frac{h\\Delta x}{\\Delta y}$ | $0$ | $0$ |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**$b$ vector for each cell type.**\n",
    "\n",
    "| Type | $b_{v_s}$ | $+b_{u_w}$ | $+b_{u_e}$ | $+b_{v_n}$ |\n",
    "|------|----------|----------|----------|----------|\n",
    "| I    | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| CBL  | $\\Delta xv_s$| $+\\Delta yu_w$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| EB   | $\\Delta xv_s$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| CBR  | $\\Delta xv_s$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta yu_e$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| EL   | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta yu_w$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| ER   | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta yu_e$ | $- \\Delta x(v_{i,j+1}^n+hH_{i,j+1}^v)$ |\n",
    "| CTL  | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta yu_w$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta xv_n$ |\n",
    "| ET   | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta y(u_{i+1,j}+hH_{i+1,j}^u)$ | $- \\Delta xv_n$ |\n",
    "| CTR  | $\\Delta x(v_{i,j}^n+hH_{i,j}^v)$| $+\\Delta y(u_{i,j}^n+hH_{i,j}^u)$ | $-\\Delta yu_e$ | $- \\Delta xv_n$ |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation\n",
    "### 2-D lid-driven cavity\n",
    "* This is a box (cavity) with sliding walls. \n",
    "* Boundary conditions are simply zero normal flow through the domain boundaries, and constant tangential wall velocities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boilerplate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.sparse import diags\n",
    "from scipy.sparse.linalg import spsolve\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "IJ = np.ix_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User specifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_user_specifications():\n",
    "    global nx, ny, Lx, Ly, ν, ubc_t, ubc_b, vbc_r, vbc_l, n_τ_run, cfl\n",
    "    \n",
    "    nx = 40          # number of grid points in x direction (P-grid)\n",
    "    ny = 60          # number of grid points in the y direction (P-grid)\n",
    "    Lx = 1.0          # domain length in x\n",
    "    Ly = 1.5          # domain length in y\n",
    "    ν  = 1.0          # kinematic viscosity\n",
    "    \n",
    "    ubc_t = 10.0      # u on top boundary\n",
    "    ubc_b = 0.0       # u on bottom boundary\n",
    "    vbc_r = 10.0      # v on right boundary\n",
    "    vbc_l = 0.0       # v on left boundary\n",
    "    \n",
    "    n_τ_run = 2       # number of box timescales to run for\n",
    "    cfl  = 0.05       # timestep size factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid and time parameters\n",
    "\n",
    "**Indexing:** \n",
    "* We use an x-y Cartesian grid. \n",
    "* For an array ```u(nx,ny)```, ```u[i,j]``` refers to x-index ```i``` and y-index ```j```.\n",
    "* As a matrix, we normally think of ```u[i,j]``` as row ```i``` and column ```j``` and visualize ```i``` varying vertically (rows change top-to-bottom) and ```j``` varying horizontally (columns change left-to-right).\n",
    "* However, in visualizing our grid, x is horizontal (increasing left-to-right), and y is vertical (increasing bottom to top).\n",
    "* This difference in the arrays versus the visual grid is only important in the tiling of the 2-D grid to a 1-D array for the pressure solve (see below), and in plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_grid_time_vars():\n",
    "    global x, y, Δx, Δy, tend, Δt, nsteps, u, v, P\n",
    "    \n",
    "    x  = np.linspace(0,Lx,nx)     # x-grid\n",
    "    y  = np.linspace(0,Ly,ny)     # y-grid\n",
    "    Δx = x[1] - x[0]              # x-grid spacing\n",
    "    Δy = y[1] - y[0]              # y-grid spacing\n",
    "    \n",
    "    τ_box  = (Lx+Ly)/2 / np.max(np.abs([ubc_t, ubc_b, vbc_r, vbc_l]))      # box timescale\n",
    "    tend   = τ_box * n_τ_run                                               # simulation run time\n",
    "    Δt     = cfl*np.min([Δx,Δy])/np.max(np.abs([ubc_t,ubc_b,vbc_r,vbc_l])) # timestep size\n",
    "    nsteps = int(tend/Δt)                                                  # number of timesteps\n",
    "    Δt     = tend/nsteps                                                   # timestep size\n",
    "    \n",
    "    #-------------------- set solution variables\n",
    "    \n",
    "    u = np.zeros((nx+1,ny))\n",
    "    v = np.zeros((nx,ny+1))               \n",
    "    P = np.zeros((nx,ny))                # P = P/ρ (P_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### u-Mom function (Hu)\n",
    "* Array slices are used to avoid loops for speed and simplicity\n",
    "* Note the use of the IJ = np.ix_ for certain array slices.\n",
    "* Solving on the u-grid:\n",
    "\n",
    "<img src=\"ugrid.png\" width=\"200\"/>\n",
    "\n",
    "\n",
    "* The u array and u-grid face quantities including $H^u$ are size ```(nx+1, ny)```\n",
    "* Values of u on the left and right boundaries are part of the matrix and initialized, but don't change.\n",
    "* Interior u-cells are solved.\n",
    "    * python: ```i=1:nx```, ```j=0:ny```, or $i\\in[1,n_x)$ $j\\in[0,n_y)$.\n",
    "    * including the boundaries in the matrix facilitates the indexing by minimizing special cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_Hu():\n",
    "    \"\"\"\n",
    "    ue, uw, un, us, vn, vs are values on u-cell faces\n",
    "    These arrays include ALL u-cells (nx+1,ny) for convenience,\n",
    "    but only interior u-cells (and corresponding face values) are set.\n",
    "    \"\"\"\n",
    "    \n",
    "    ue   = np.zeros((nx+1,ny))   \n",
    "    uw   = np.zeros((nx+1,ny))\n",
    "    un   = np.zeros((nx+1,ny))\n",
    "    us   = np.zeros((nx+1,ny))\n",
    "    vn   = np.zeros((nx+1,ny))\n",
    "    vs   = np.zeros((nx+1,ny))\n",
    "    τxxe = np.zeros((nx+1,ny))\n",
    "    τxxw = np.zeros((nx+1,ny))\n",
    "    τxyn = np.zeros((nx+1,ny))\n",
    "    τxys = np.zeros((nx+1,ny))\n",
    "    Hu   = np.zeros((nx+1,ny))\n",
    "    \n",
    "    i = np.arange(1,nx)              # u-cell centers in domain interior\n",
    "    \n",
    "    ue[i,:] = (u[i+1,:] + u[i,:])/2\n",
    "    uw[i,:] = (u[i,:]   + u[i-1,:])/2\n",
    "    \n",
    "    j = np.arange(0,ny-1)\n",
    "    un[IJ(i,j)] = (u[IJ(i,j+1)] + u[IJ(i,j)])/2\n",
    "    un[i,ny-1] = ubc_t\n",
    "    j = np.arange(1,ny)\n",
    "    us[IJ(i,j)] = (u[IJ(i,j)] + u[IJ(i,j-1)])/2\n",
    "    us[i,0] = ubc_b\n",
    "    \n",
    "    j = np.arange(0,ny)\n",
    "    vn[IJ(i,j)] = (v[IJ(i-1,j+1)]+v[IJ(i,j+1)])/2\n",
    "    vs[IJ(i,j)] = (v[IJ(i-1,j)]  +v[IJ(i,j)])  /2\n",
    "    \n",
    "    τxxe[i,:] = -2*ν*(u[i+1,:] - u[i,:])  /Δx\n",
    "    τxxw[i,:] = -2*ν*(u[i,:]   - u[i-1,:])/Δx\n",
    "    \n",
    "    j = np.arange(0,ny-1)\n",
    "    τxyn[IJ(i,j)] = -ν*(u[IJ(i,j+1)]-u[IJ(i,j)])/Δy - ν*(v[IJ(i,j+1)]-v[IJ(i-1,j+1)])/Δx\n",
    "    τxyn[i,ny-1]  = -ν*(ubc_t-u[i,ny-1])/(Δy/2)     - ν*(v[i,ny]-v[i-1,ny])/Δx \n",
    "    \n",
    "    j = np.arange(1,ny)\n",
    "    τxys[IJ(i,j)] = -ν*(u[IJ(i,j)]-u[IJ(i,j-1)])/Δy - ν*(v[IJ(i,j)]-v[IJ(i-1,j)])/Δx\n",
    "    τxys[i,0]     = -ν*(u[i,0]-ubc_b)/(Δy/2)        - ν*(v[i,0]-v[i-1,0])/Δx\n",
    "    \n",
    "    Hu[i,:] = -((ue[i,:]*ue[i,:] - uw[i,:]*uw[i,:])/Δx + (un[i,:]*vn[i,:] - us[i,:]*vs[i,:])/Δy) \\\n",
    "              -((τxxe[i,:] - τxxw[i,:])/Δx + (τxyn[i,:] - τxys[i,:])/Δy)\n",
    "    \n",
    "    return Hu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### v-Mom function (Hv)\n",
    "* Array slices are used to avoid loops for speed and simplicity\n",
    "* Note the use of the IJ = np.ix_ for certain array slices.\n",
    "* Solving on the v-grid:\n",
    "\n",
    "<img src=\"vgrid.png\" width=\"200\"/>\n",
    "\n",
    "\n",
    "* The v array and v-grid face quantities including $H^v$ are size ```(nx, ny+1)```\n",
    "* Values of v on the left and right boundaries are part of the matrix and initialized, but don't change.\n",
    "* Interior v-cells are solved.\n",
    "    * python: ```i=0:nx```, ```j=1:ny```, or $i\\in[0,n_x)$ $j\\in[1,n_y)$.\n",
    "    * including the boundaries in the matrix facilitates the indexing by minimizing special cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_Hv():\n",
    "    \"\"\"\n",
    "    vn, vs, ve, vw, ue, uw are values on v-cell faces\n",
    "    These arrays include ALL v-cells (nx,ny+1) for convenience,\n",
    "    but only interior v-cells (and corresponding face values) are set.\n",
    "    \"\"\"\n",
    "    \n",
    "    vn   = np.zeros((nx,ny+1))   \n",
    "    vs   = np.zeros((nx,ny+1))\n",
    "    ve   = np.zeros((nx,ny+1))\n",
    "    vw   = np.zeros((nx,ny+1))\n",
    "    ue   = np.zeros((nx,ny+1))\n",
    "    uw   = np.zeros((nx,ny+1))\n",
    "    τyyn = np.zeros((nx,ny+1))\n",
    "    τyys = np.zeros((nx,ny+1))\n",
    "    τyxe = np.zeros((nx,ny+1))\n",
    "    τyxw = np.zeros((nx,ny+1))\n",
    "    Hv   = np.zeros((nx,ny+1))\n",
    "    \n",
    "    j = np.arange(1,ny)              # v-cell centers in domain interior\n",
    "    \n",
    "    vn[:,j] = (v[:,j+1] + v[:,j])/2\n",
    "    vs[:,j] = (v[:,j]   + v[:,j-1])/2\n",
    "    \n",
    "    i = np.arange(0,nx-1)\n",
    "    ve[IJ(i,j)] = (v[IJ(i+1,j)] + v[IJ(i,j)])/2\n",
    "    ve[nx-1,j] = vbc_r\n",
    "    i = np.arange(1,nx)\n",
    "    vw[IJ(i,j)] = (v[IJ(i,j)] + v[IJ(i-1,j)])/2\n",
    "    vw[0,j] = vbc_l\n",
    "    \n",
    "    i = np.arange(0,nx)\n",
    "    ue[IJ(i,j)] = (u[IJ(i+1,j-1)] + u[IJ(i+1,j)])/2\n",
    "    uw[IJ(i,j)] = (u[IJ(i,j-1)]   + u[IJ(i,j)])  /2\n",
    "    \n",
    "    τyyn[:,j] = -2*ν*(v[:,j+1] - v[:,j])  /Δy\n",
    "    τyys[:,j] = -2*ν*(v[:,j]   - v[:,j-1])/Δy\n",
    "    \n",
    "    i = np.arange(0,nx-1)\n",
    "    τyxe[IJ(i,j)] = -ν*(v[IJ(i+1,j)]-v[IJ(i,j)])/Δx - ν*(u[IJ(i+1,j)]-u[IJ(i+1,j-1)])/Δy\n",
    "    τyxe[nx-1,j]  = -ν*(vbc_r-v[nx-1,j])/(Δx/2)     - ν*(u[nx,j]-u[nx,j-1])/Δy \n",
    "    \n",
    "    i = np.arange(1,nx)\n",
    "    τyxw[IJ(i,j)] = -ν*(v[IJ(i,j)]-v[IJ(i-1,j)])/Δx - ν*(u[IJ(i,j)]-u[IJ(i,j-1)])/Δy\n",
    "    τyxw[0,j]     = -ν*(v[0,j]-vbc_l)/(Δx/2)        - ν*(u[0,j]-u[0,j-1])/Δy\n",
    "    \n",
    "    Hv[:,j] = -((vn[:,j]*vn[:,j] - vs[:,j]*vs[:,j])/Δy + (ve[:,j]*ue[:,j] - vw[:,j]*uw[:,j])/Δx) \\\n",
    "              -((τyyn[:,j] - τyys[:,j])/Δy + (τyxe[:,j] - τyxw[:,j])/Δx)\n",
    "    \n",
    "    return Hv\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pressure solver\n",
    "\n",
    "* Solving $\\hat{P}$ on the P-grid:\n",
    "\n",
    "<img src=\"Pgrid2.png\" width=\"200\"/>\n",
    "\n",
    "* We have an algebraic equation for each cell. The collection of equations will be an $A\\hat{P}=b$ system.\n",
    "* We need to create $A$ and $b$, where $A$ is the matrix of coefficients of $\\hat{P}$ and $b$ is a vector.\n",
    "* The equations were shown previously.\n",
    "    * We had 9 equation types: (1) interior, (4) sides, and (4) corners.\n",
    "    * The code below parallels the tables shown with the equations.\n",
    "* Each cell has a $\\hat{P}$ coefficient for itself and four neighbors. \n",
    "    * This gives an ```(nx,ny)``` array for the base point and each neighbor.\n",
    "* Similarly, we have an ```(nx,ny)``` array for $b$.\n",
    "* Once the $\\hat{P}$ coefficient and $b$ arrays are set, we need to reorder them from 2-D arrays to a 1-D array.\n",
    "    * We tile the points as indicated in the grid above: that is, counting cells in the x-direction from low to high y.\n",
    "    * This is done with numpy's reshape function.\n",
    "        * Since we are numbering along x, and x varies along columns in our arrays, we are using a column-major ordering, as in Fortran (as opposed to row-major ordering as in C and here in Python). Specify ```order='F'``` in the reshape functions.\n",
    "* For incompressible flows, pressure is only determined up to an additive constant: only pressure differences matter. This is evident in pressure only appearing in a gradient; adding any constant to $\\hat{P}$ doesn't change the term.\n",
    "    * To keep pressure from drifting, at the end of the pressure solve, we subtract the mean pressure so that the average is always pinned at zero.\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve_P(h):\n",
    "    \"\"\"\n",
    "    Set up and solve the AP=b system, where A is a matrix, P (=Phat) and b are vectors.\n",
    "    \"\"\"\n",
    "    \n",
    "    \n",
    "    nP = nx*ny     # total grid points solved (all P-grid cells)\n",
    "    \n",
    "    b    = np.zeros((nx,ny))           # set below\n",
    "    cP   = np.zeros((nx,ny))           # coefficient of P_i,j; set below\n",
    "    cPjm = np.full((nx,ny),-h*Δx/Δy)   # coefficient of P_i,j-1; initialized here, specialized below\n",
    "    cPim = np.full((nx,ny),-h*Δy/Δx)   # coefficient of P_i-1,j; initialized here, specialized below\n",
    "    cPip = np.full((nx,ny),-h*Δy/Δx)   # coefficient of P_i+1,j; initialized here, specialized below\n",
    "    cPjp = np.full((nx,ny),-h*Δx/Δy)   # coefficient of P_i,j+1; initialized here, specialized below\n",
    "    \n",
    "    #-------------------- \n",
    "    \n",
    "    # Interior\n",
    "    i = np.arange(1,nx-1); j = np.arange(1,ny-1)\n",
    "    b[IJ(i,j)]    = -Δy*(u[IJ(i+1,j)]+h*Hu[IJ(i+1,j)]) + Δy*(u[IJ(i,j)]+h*Hu[IJ(i,j)]) - Δx*(v[IJ(i,j+1)]+h*Hv[IJ(i,j+1)]) + Δx*(v[IJ(i,j)]+h*Hv[IJ(i,j)])\n",
    "    cP[IJ(i,j)]   =  2*h*Δy/Δx + 2*h*Δx/Δy\n",
    "    \n",
    "    # Corner bottom left\n",
    "    i = 0; j = 0\n",
    "    b[i,j]    = -Δy*(u[i+1,j]+h*Hu[i+1,j])            + Δy*u[i,j]                     - Δx*(v[i,j+1]+h*Hv[i,j+1])          + Δx*v[i,j]\n",
    "    cP[i,j]   =  h*Δy/Δx + h*Δx/Δy\n",
    "    cPjm[i,j] =  0.0\n",
    "    cPim[i,j] =  0.0\n",
    "    \n",
    "    # Side bottom\n",
    "    i = np.arange(1,nx-1); j = 0\n",
    "    b[i,j]    = -Δy*(u[i+1,j]+h*Hu[i+1,j])            + Δy*(u[i,j]+h*Hu[i,j])         - Δx*(v[i,j+1]+h*Hv[i,j+1])          + Δx*v[i,j]\n",
    "    cP[i,j]   =  2*h*Δy/Δx + h*Δx/Δy\n",
    "    cPjm[i,j] =  0.0\n",
    "    \n",
    "    # Corner bottom right\n",
    "    i = nx-1; j = 0\n",
    "    b[i,j]    = -Δy*u[i+1,j]                          + Δy*(u[i,j]+h*Hu[i,j])         - Δx*(v[i,j+1]+h*Hv[i,j+1])          + Δx*v[i,j]\n",
    "    cP[i,j]   =  h*Δy/Δx + h*Δx/Δy\n",
    "    cPjm[i,j] =  0.0\n",
    "    cPip[i,j] =  0.0\n",
    "    \n",
    "    # Side left\n",
    "    i = 0; j = np.arange(1,ny-1)\n",
    "    b[i,j]    = -Δy*(u[i+1,j]+h*Hu[i+1,j])            + Δy*u[i,j]                     - Δx*(v[i,j+1]+h*Hv[i,j+1])          + Δx*(v[i,j]+h*Hv[i,j])\n",
    "    cP[i,j]   =  h*Δy/Δx + 2*h*Δx/Δy\n",
    "    cPim[i,j] =  0.0\n",
    "    \n",
    "    # Side right\n",
    "    i = nx-1; j = np.arange(1,ny-1)\n",
    "    b[i,j]    = -Δy*u[i+1,j]                          + Δy*(u[i,j]+h*Hu[i,j])         - Δx*(v[i,j+1]+h*Hv[i,j+1])          + Δx*(v[i,j]+h*Hv[i,j])\n",
    "    cP[i,j]   =  h*Δy/Δx + 2*h*Δx/Δy\n",
    "    cPip[i,j] =  0.0\n",
    "    \n",
    "    # Corner top left\n",
    "    i = 0; j = ny-1\n",
    "    b[i,j]    = -Δy*(u[i+1,j]+h*Hu[i+1,j])            + Δy*u[i,j]                     - Δx*v[i,j+1]                        + Δx*(v[i,j]+h*Hv[i,j])\n",
    "    cP[i,j]   =  h*Δy/Δx + h*Δx/Δy\n",
    "    cPim[i,j] =  0.0\n",
    "    cPjp[i,j] =  0.0\n",
    "    \n",
    "    # Side top\n",
    "    i = np.arange(1,nx-1); j = ny-1\n",
    "    b[i,j]    = -Δy*(u[i+1,j]+h*Hu[i+1,j])            + Δy*(u[i,j]+h*Hu[i,j])         - Δx*v[i,j+1]                        + Δx*(v[i,j]+h*Hv[i,j])\n",
    "    cP[i,j]   =  2*h*Δy/Δx + h*Δx/Δy\n",
    "    cPjp[i,j] =  0.0\n",
    "    \n",
    "    # Corner top right\n",
    "    i = nx-1; j = ny-1\n",
    "    b[i,j]    = -Δy*u[i+1,j]                          + Δy*(u[i,j]+h*Hu[i,j])         - Δx*v[i,j+1]                        + Δx*(v[i,j]+h*Hv[i,j])\n",
    "    cP[i,j]   =  h*Δy/Δx + h*Δx/Δy\n",
    "    cPip[i,j] =  0.0\n",
    "    cPjp[i,j] =  0.0\n",
    "    \n",
    "    #---------------------------------\n",
    "    \n",
    "    b    = np.reshape(b,    nP, order='F')\n",
    "    \n",
    "    cP   = np.reshape(cP,   nP, order='F')\n",
    "    cPjm = np.reshape(cPjm, nP, order='F')\n",
    "    cPim = np.reshape(cPim, nP, order='F')\n",
    "    cPip = np.reshape(cPip, nP, order='F')\n",
    "    cPjp = np.reshape(cPjp, nP, order='F')\n",
    "    \n",
    "    A = diags([cPjm[nx:], cPim[1:], cP, cPip[:-1], cPjp[:-nx]], [-nx, -1, 0, 1, nx], format='csr')\n",
    "    \n",
    "    #---------------------------------\n",
    "    \n",
    "    P = spsolve(A,b)\n",
    "    \n",
    "    P -= np.average(P)\n",
    "    \n",
    "    P = np.reshape(P, (nx,ny),order='F')\n",
    "    \n",
    "    return P\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Driver and integrator\n",
    "\n",
    "* Solve the problem with a simple Explicit Euler integration.\n",
    "    * Higher order integrators are possible, with minor adjustments to the pressure solve.\n",
    "* To monitor the approach to steady state, record the average kinetic energy on the domain at each timestep.\n",
    "* Velocities are interpolated to the P-grid for convenient plotting, and evaluating kinetic energy.\n",
    "* The velocity magnitude is stored for convenient plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12.5 s, sys: 43.7 ms, total: 12.5 s\n",
      "Wall time: 12.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "set_user_specifications()\n",
    "set_grid_time_vars()\n",
    "\n",
    "ke = np.zeros(nsteps+1)\n",
    "times = np.linspace(0,tend,nsteps+1)\n",
    "\n",
    "for k in range(nsteps):\n",
    "    \n",
    "    Hu = get_Hu()\n",
    "    Hv = get_Hv()\n",
    "    \n",
    "    P = solve_P(Δt)\n",
    "    \n",
    "    i = np.arange(1,nx)\n",
    "    u[i,:] = u[i,:] + Δt*Hu[i,:] - Δt*(P[i,:]-P[i-1,:])/Δx\n",
    "    \n",
    "    j = np.arange(1,ny)\n",
    "    v[:,j] = v[:,j] + Δt*Hv[:,j] - Δt*(P[:,j]-P[:,j-1])/Δy\n",
    "    \n",
    "    #-----------------------------\n",
    "    \n",
    "    U = (u[:-1,:] + u[1:,:])/2\n",
    "    V = (v[:,:-1] + v[:,1:])/2\n",
    "    velmag = np.sqrt(U*U + V*V)\n",
    "    ke[k+1] = 0.5*(np.average(velmag**2))\n",
    "    \n",
    "    \n",
    "#----------- interpolate velocities to the P-grid\n",
    "\n",
    "U = (u[:-1,:] + u[1:,:])/2    # u-velocity on the P-grid\n",
    "V = (v[:,:-1] + v[:,1:])/2    # v-velocity on the P-grid\n",
    "velmag = np.sqrt(U*U + V*V)   # velocity magnitude."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 252x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X,Y = np.meshgrid(x,y)\n",
    "\n",
    "plt.rc('font', size=14)\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(10,10))\n",
    "\n",
    "ax1.set_aspect('equal', adjustable='box')\n",
    "ax1.streamplot(x,y,U.T,V.T, density=2.5, linewidth=1, arrowsize=0.001, color=velmag.T)\n",
    "ax1.set_title(r'$|\\vec{v}|$')\n",
    "ax1.set_xlim([0,Lx])\n",
    "ax1.set_ylim([0,Ly])\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([]);\n",
    "\n",
    "ax2.set_aspect('equal', adjustable='box')\n",
    "ax2.contourf(X,Y,P.T,40)\n",
    "ax2.set_title(r'$P/\\rho$')\n",
    "ax2.set_xlim([0,Lx])\n",
    "ax2.set_ylim([0,Ly])\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([]);\n",
    "\n",
    "plt.figure(figsize=(3.5,3))\n",
    "plt.plot(times,ke)\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('KE');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}