README.md

Lab 04 - Template parameters and convergence rates

Theory and Practice of Finite Elements

Luca Heltai [email protected]

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General Instructions

For each of the point below, extend the Poisson class with functions that perform the indicated tasks, trying to minimize the amount of code you copy and paste, possibly restructuring existing code by adding arguments to existing functions, and generating wrappers similar to the run method (e.g., run_exercise_3).

Once you created a function that performs the given task, add it to the poisson-tester.cc file, and make sure all the exercises are run through the gtest executable, e.g., adding a test for each exercise, as in the following snippet:

TEST_F(PoissonTester, Exercise3) {
   run_exercise_3();
}

By the end of this laboratory, you will have a code that solves a Poisson problem in arbitrary dimensions, with Lagrangian finite elements of arbitrary degree, on different domain types, with different boundary conditions, and different functions for the definition of the right hand side, the stiffness coefficient, and the forcing term.

The problem will run on successively refined grids, and we will verify Bramble-Hilbert lemma for Lagrangian finite element spaces of different order, building manufactured solutions using python, and plotting error convergence tables using latex, tikz, and pgfplots.

The program will build on top of your implementation of Step3, drawing from step-4, step-5, and step-7.

Lab-04

step-4

1. See documentation of step-4 at https://www.dealii.org/current/doxygen/deal.II/step_4.html

2. Compile and run step-4. Examine the source and header files.

3. Copy your implementation of step-3 from lab-03 to the files source/poisson.cc, include/poisson.h, and tests/poisson-tester.cc, make sure you rename correctly all your files and classes to Poisson.

4. Add the template parameter <int dim> to your Poisson class, following step-4 as an example, and make sure that your program runs correctly both in 2D and in 3D.

5. Add the parameters

   • Number of refinement cycles
   • Exact solution expression

   and the corresponding member variables (i.e., n_cycles, exact_solution_expression, and exact_solution) and run the Poisson problem again for each refinement cycle with one global refinement, making sure you output the result for each refinement cycle separately in vtu format, i.e., if Output filename is poisson_2d, and Number of refinement cycles is 3, you should output

   • poisson_2d_0.vtu
   • poisson_2d_1.vtu
   • poisson_2d_2.vtu

   where the solution in poisson_2d_0.vtu should have Number of global refinements refinements, poisson_2d_1.vtu should have Number of global refinements +1 refinements, and poisson_2d_2.vtu should have Number of global refinements +2 refinements.

6. Add a ParsedConvergenceTable object to your Poisson class (see https://www.dealii.org/current/doxygen/deal.II/classParsedConvergenceTable.html) and add its parameters in the subsection Error table of the parameter file, i.e., in the Poisson constructor add the following lines of code:

this->prm.enter_subsection("Error table");
error_table.add_parameters(this->prm);
this->prm.leave_subsection();

7. Set the boundary conditions, forcing function, and exact expression to get the manufactured solution u(x,y)=sin(pi*x)*cos(pi*y). Add a method compute_error() to the Poisson class, that calls the ParsedConvergenceTable::error_from_exact method with the exact_solution function you created above. Make sure you output both the L2 and H1 error in text format to a file. Play with the jupyter notebook manufactured_solutions.ipynb to construct non-trivial exact solutions.

8. Add a parameter Stiffness coefficient expression and the corresponding members to the Poisson class, so that the problem you will be solving is $-div(coefficient(x)\nabla u) = f(x)$.

9. Construct a (non-singular!) manufactured solution where coefficient is a discontinuous function. Notice that the manufactured solution may need a discontinuous forcing term on the right hand side, but should not have other types of singularities, that is, you need to make sure that $coefficient(x)\nabla u$ is continuous, i.e., that $\nabla u$ has a jump depending on the jump of the coefficient. Output the error tables, and comment on the error rates you observe. Do things improve when increasing the polynomial order? Why?

10. (optional) Use the latex file provided in the latex subdirectory to generate professional convergence plots for your solutions.