diff --git a/ProcessLib/SmallDeformation/Tests.cmake b/ProcessLib/SmallDeformation/Tests.cmake
index bda11a3ee9d..7e707e37064 100644
--- a/ProcessLib/SmallDeformation/Tests.cmake
+++ b/ProcessLib/SmallDeformation/Tests.cmake
@@ -289,6 +289,7 @@ AddTest(
)
if(NOT OGS_USE_PETSC)
+ NotebookTest(NOTEBOOKFILE Mechanics/CooksMembrane/CooksMembraneBbar.ipynb RUNTIME 1)
NotebookTest(NOTEBOOKFILE Mechanics/Linear/SimpleMechanics.ipynb RUNTIME 5)
NotebookTest(NOTEBOOKFILE Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole.md RUNTIME 15)
if(NOT WIN32)
diff --git a/Tests/Data/Mechanics/CooksMembrane/CooksMembraneBbar.ipynb b/Tests/Data/Mechanics/CooksMembrane/CooksMembraneBbar.ipynb
new file mode 100644
index 00000000000..d4821ec5123
--- /dev/null
+++ b/Tests/Data/Mechanics/CooksMembrane/CooksMembraneBbar.ipynb
@@ -0,0 +1,563 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "id": "73c13b4b-fee8-44b4-8a30-427afac95d32",
+ "metadata": {},
+ "source": [
+ "+++\n",
+ "title = \"Cook's membrane example\"\n",
+ "date = \"2024-06-11\"\n",
+ "author = \"Wenqing Wang\"\n",
+ "image = \"figure/cooks_membrane.png\"\n",
+ "web_subsection = \"small-deformations\"\n",
+ "weight = 3\n",
+ "+++"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "286c4e5e-eb58-409e-ab9b-f2c42fd388b4",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\newcommand{\\B}{\\text{B}}\n",
+ "\\newcommand{\\F}{\\text{F}}\n",
+ "\\newcommand{\\I}{\\mathbf I}\n",
+ "\\newcommand{\\intD}[1]{\\int_{\\Omega_e}#1\\mathrm{d}\\Omega}\n",
+ "$$\n",
+ "\n",
+ "# Cook's membrane example for nearly icompressible solid\n",
+ "\n",
+ "## B bar method\n",
+ " Considering a strain decomposition: $\\mathbf\\epsilon = \\underbrace{\\mathbf\\epsilon- \\frac{1}{3}(\\epsilon:\\mathbf I)}_{\\text{deviatoric}}\\I + \\underbrace{\\frac{1}{3}(\\epsilon:\\mathbf I)}_{\\text{dilatational}} \\I$.\n",
+ "\t The idea of the B bar method is to use another quadrature rule to interpolate the dilatational part, which leads to a modified B matrix [1]:\t \n",
+ "$$\n",
+ "\t \\bar\\B = \\underbrace{\\B - \\B^{\\text{dil}}}_{\\text{original B elements}}+ \\underbrace{{\\bar\\B}^{\\text{dil}}}_{\\text{by another quadrature rule} }\n",
+ "$$\n",
+ "There are several methods to form ${\\bar\\B}^{\\text{dil}}$ such as selective integration, generalization of the mean-dilatation formulation. In the current OGS, we use the latter, which reads\n",
+ "$$\n",
+ "\t\t {\\bar\\B}^{\\text{dil}} = \\frac{\\intD{\\B^{\\text{dil}}(\\xi)}}{\\intD{}}\n",
+ "$$\n",
+ "\n",
+ "## Example\n",
+ "To verify the implementation of the B bar method, the so called Cook's membrane is used as a benchmark.\n",
+ "Illustrated in the following figure, this example simulates a tapered\n",
+ "and swept panel of unit thickness. The left edge is clamped and the right edge is applied with a distributed shearing load $F$ = 100 N/mm. The plane strain condition is considered. This numerical model is exactly the same as that is presented in the paper by T. Elguedj et al [1,2]. \n",
+ "\n",
+ "\n",
+ "\n",
+ "## Reference\n",
+ "\n",
+ "[1] T.J.R. Hughes (1980). Generalization of selective integration procedures to anisotropic and nonlinear media. International Journal for Numerical Methods in Engineering, 15(9), 1413-1418.\n",
+ "\n",
+ "[2] T. Elguedj, Y. Bazilevs, V.M. Calo, T.J.R. Hughes (2008),\n",
+ " $\\bar\\B$ and $\\bar\\F$ projection methods for nearly incompressible linear and non-linear elasticity and plasticity using higher-order NURBS elements, Computer Methods in Applied Mechanics and Engineering, 197(33--40), 2732-2762.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "f0ae03a5-4cb3-43aa-91f1-5f25e2de61bc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from pathlib import Path\n",
+ "\n",
+ "from ogs6py.ogs import OGS\n",
+ "\n",
+ "out_dir = Path(os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\"))\n",
+ "if not out_dir.exists():\n",
+ " out_dir.mkdir(parents=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "35a460be-3080-4f1f-9285-9e682fe20d38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xml.etree.ElementTree as ET\n",
+ "\n",
+ "import pyvista as pv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "2bbde8c5-9907-43c2-a26c-61a5ab512a6d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_last_vtu_file_name(pvd_file_name):\n",
+ " tree = ET.parse(Path(out_dir) / pvd_file_name)\n",
+ " root = tree.getroot()\n",
+ " # Get the last DataSet tag\n",
+ " last_dataset = root.findall(\".//DataSet\")[-1]\n",
+ "\n",
+ " # Get the 'file' attribute of the last DataSet tag\n",
+ " file_attribute = last_dataset.attrib[\"file\"]\n",
+ " return f\"{out_dir}/\" + file_attribute\n",
+ "\n",
+ "\n",
+ "def get_top_uy(pvd_file_name):\n",
+ " top_point = (48.0e-3, 60.0e-3, 0)\n",
+ " file_name = get_last_vtu_file_name(pvd_file_name)\n",
+ " mesh = pv.read(file_name)\n",
+ " p_id = mesh.find_closest_point(top_point)\n",
+ " u = mesh.point_data[\"displacement\"][p_id]\n",
+ " return u[1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "a6d91f6e-b0b7-4ed0-a673-4cc3581a19bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def run_single_test(mesh_name, output_prefix, use_bbar=\"false\"):\n",
+ " model = OGS(INPUT_FILE=\"CooksMembrane.prj\", PROJECT_FILE=f\"{out_dir}/modified.prj\")\n",
+ " model.replace_text(mesh_name, xpath=\"./mesh\")\n",
+ " model.replace_text(use_bbar, xpath=\"./processes/process/use_b_bar\")\n",
+ " model.replace_text(output_prefix, xpath=\"./time_loop/output/prefix\")\n",
+ " model.replace_text(\n",
+ " \"BiCGSTAB\", xpath=\"./linear_solvers/linear_solver/eigen/solver_type\"\n",
+ " )\n",
+ " model.replace_text(\"ILUT\", xpath=\"./linear_solvers/linear_solver/eigen/precon_type\")\n",
+ " vtu_file_name = output_prefix + \"_ts_1_t_1.000000.vtu\"\n",
+ " model.replace_text(vtu_file_name, xpath=\"./test_definition/vtkdiff[1]/file\")\n",
+ " model.replace_text(vtu_file_name, xpath=\"./test_definition/vtkdiff[2]/file\")\n",
+ " model.replace_text(vtu_file_name, xpath=\"./test_definition/vtkdiff[3]/file\")\n",
+ "\n",
+ " model.write_input()\n",
+ "\n",
+ " # Run OGS\n",
+ " model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m .\")\n",
+ "\n",
+ " # Get uy at the top\n",
+ " return get_top_uy(output_prefix + \".pvd\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "37127e0d-10dd-4c7a-b263-0fe3e42b6b64",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.21584200859069824 s\n",
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.08944129943847656 s\n",
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.09354853630065918 s\n",
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.10343599319458008 s\n",
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.1191704273223877 s\n",
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.1564652919769287 s\n",
+ "[0.0021645867841231024, 0.0022603329644579387, 0.0023752958560671676, 0.002519725590136147, 0.00265152941337909, 0.0028682896170252165]\n"
+ ]
+ }
+ ],
+ "source": [
+ "mesh_names = [\n",
+ " \"mesh.vtu\",\n",
+ " \"mesh_n10.vtu\",\n",
+ " \"mesh_n15.vtu\",\n",
+ " \"mesh_n20.vtu\",\n",
+ " \"mesh_n25.vtu\",\n",
+ " \"mesh_n30.vtu\",\n",
+ "]\n",
+ "output_prefices_non_bbar = [\n",
+ " \"cooks_membrane_sd_edge_div_4_non_bbar\",\n",
+ " \"cooks_membrane_sd_refined_mesh_10_non_bbar\",\n",
+ " \"cooks_membrane_sd_refined_mesh_15_non_bbar\",\n",
+ " \"cooks_membrane_sd_refined_mesh_20_non_bbar\",\n",
+ " \"cooks_membrane_sd_refined_mesh_25_non_bbar\",\n",
+ " \"cooks_membrane_sd_refined_mesh_30_non_bbar\",\n",
+ "]\n",
+ "\n",
+ "uys_at_top_non_bbar = []\n",
+ "for mesh_name, output_prefix in zip(mesh_names, output_prefices_non_bbar):\n",
+ " uy_at_top = run_single_test(mesh_name, output_prefix)\n",
+ " uys_at_top_non_bbar.append(uy_at_top)\n",
+ "\n",
+ "print(uys_at_top_non_bbar)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "99c34461-1d0a-42f7-a716-86e9bac7c046",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.07093119621276855 s\n",
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.07613062858581543 s\n",
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.08341574668884277 s\n",
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.10603880882263184 s\n",
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.12837624549865723 s\n",
+ "OGS finished with project file output/modified.prj.\n",
+ "Execution took 0.16935420036315918 s\n",
+ "[0.006798855415340229, 0.007728027781081195, 0.00787252293068606, 0.007934707855031697, 0.007963259983774562, 0.007989988696891803]\n"
+ ]
+ }
+ ],
+ "source": [
+ "output_prefices = [\n",
+ " \"cooks_membrane_sd_edge_div_4\",\n",
+ " \"cooks_membrane_sd_refined_mesh_10\",\n",
+ " \"cooks_membrane_sd_refined_mesh_15\",\n",
+ " \"cooks_membrane_sd_refined_mesh_20\",\n",
+ " \"cooks_membrane_sd_refined_mesh_25\",\n",
+ " \"cooks_membrane_sd_refined_mesh_30\",\n",
+ "]\n",
+ "\n",
+ "uys_at_top_bbar = []\n",
+ "for mesh_name, output_prefix in zip(mesh_names, output_prefices):\n",
+ " uy_at_top = run_single_test(mesh_name, output_prefix, \"true\")\n",
+ " uys_at_top_bbar.append(uy_at_top)\n",
+ "\n",
+ "print(uys_at_top_bbar)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "7c55e390-e329-45ac-b3d0-b4f785059672",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "ne = [4, 10, 15, 20, 25, 30]\n",
+ "\n",
+ "\n",
+ "def plot_data(ne, u_y_bbar, uy_non_bbar, file_name=\"\"):\n",
+ " # Plotting\n",
+ " plt.rcParams[\"figure.figsize\"] = [5, 5]\n",
+ "\n",
+ " if len(u_y_bbar) != 0:\n",
+ " plt.plot(\n",
+ " ne, np.array(u_y_bbar) * 1e3, marker=\"o\", linestyle=\"dashed\", label=\"B bar\"\n",
+ " )\n",
+ " if len(uy_non_bbar) != 0:\n",
+ " plt.plot(\n",
+ " ne,\n",
+ " np.array(uy_non_bbar) * 1e3,\n",
+ " marker=\"x\",\n",
+ " linestyle=\"dashed\",\n",
+ " label=\"non B bar\",\n",
+ " )\n",
+ "\n",
+ " plt.xlabel(\"Number of elements per side\")\n",
+ " plt.ylabel(\"Top right corner displacement /mm\")\n",
+ " plt.legend()\n",
+ "\n",
+ " plt.tight_layout()\n",
+ " if file_name != \"\":\n",
+ " plt.savefig(file_name)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c74f1383-596b-4e2c-93f9-61b41248ca2f",
+ "metadata": {},
+ "source": [
+ "## Result\n",
+ "\n",
+ "### Vertical diplacement at the top point\n",
+ "\n",
+ "The following figure shows that the convergence of the solutions obtained by using the B bar method follows the one presented in the paper by T. Elguedj et al [1]. However, the results obtained without the B bar method are quit far from the converged solution with the finest mesh. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "96ac5068-feae-4c82-90d9-ee4535b08291",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
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