diff --git a/Makefile b/Makefile
index f71a95d..1779770 100644
--- a/Makefile
+++ b/Makefile
@@ -21,5 +21,6 @@ get-dakota-src:
 		git submodule update --init packages/pecos && \
 		git submodule update --init packages/surfpack && \
 		git apply ../src_patches/dakota-src.patch && \
+		git apply --whitespace=nowarn ../src_patches/adaptsampl_batch.patch && \
 	    find . \( -name \*.cpp -o -name \*.hpp -o -name \*.c -o -name \*.h \) -exec \
 			sed -i -E -f ../src_patches/replace_old_macros_numpy.sed {} +
diff --git a/src_patches/adaptsampl_batch.patch b/src_patches/adaptsampl_batch.patch
new file mode 100644
index 0000000..ea14ca8
--- /dev/null
+++ b/src_patches/adaptsampl_batch.patch
@@ -0,0 +1,43 @@
+diff --git a/src/NonDAdaptiveSampling.cpp b/src/NonDAdaptiveSampling.cpp
+index 5a43bdf31..3d22b7418 100644
+--- a/src/NonDAdaptiveSampling.cpp
++++ b/src/NonDAdaptiveSampling.cpp
+@@ -298,14 +298,30 @@ namespace Dakota
+ 			// add new_X to the build points and append approximation
+ 			VariablesArray points_to_add;
+ 			IntResponseMap responses_to_add;
+-			for(int i = 0; i < new_Xs.size(); i++) 
+-			{
+-				iteratedModel.continuous_variables(new_Xs[i]);
+-				iteratedModel.evaluate();
+-				responses_to_add.insert(IntResponsePair(iteratedModel.evaluation_id(),
+-										iteratedModel.current_response()));
+-				points_to_add.push_back(iteratedModel.current_variables());
+-			}
++            if (iteratedModel.asynch_flag())
++            {
++                fout << "\nUsing async evaluation" << std::endl;
++            
++                for(int i = 0; i < new_Xs.size(); i++) 
++                {
++                    iteratedModel.continuous_variables(new_Xs[i]);
++                    iteratedModel.evaluate_nowait();
++                    points_to_add.push_back(iteratedModel.current_variables());
++                }
++
++                // Wait for the responses.
++                responses_to_add = iteratedModel.synchronize(); 
++            }
++            else {
++                for(int i = 0; i < new_Xs.size(); i++) 
++                {
++                    iteratedModel.continuous_variables(new_Xs[i]);
++                    iteratedModel.evaluate();
++                    responses_to_add.insert(IntResponsePair(iteratedModel.evaluation_id(),
++                                            iteratedModel.current_response()));
++                    points_to_add.push_back(iteratedModel.current_variables());
++                }
++            }
+ 
+ 			gpModel.append_approximation(points_to_add,responses_to_add, true);
+