From 5fc928ba4f97b57200c1124113affdd5495ff228 Mon Sep 17 00:00:00 2001
From: hhutyou <hhutyou@126.com>
Date: Sat, 10 Dec 2022 13:02:29 +0800
Subject: [PATCH] [T] Add three point bending test notebook

---
 ProcessLib/PhaseField/Tests.cmake             |   1 +
 .../PhaseField/tpb_jupyter_notebook/TPB.gml   |  13 +
 .../PhaseField/tpb_jupyter_notebook/TPB.ipynb | 400 ++++++++++++++++++
 .../PhaseField/tpb_jupyter_notebook/TPB.prj   | 294 +++++++++++++
 .../PhaseField/tpb_jupyter_notebook/TPB.vtu   |  24 ++
 .../tpb_jupyter_notebook/figures/F_vs_u.png   | Bin 0 -> 53147 bytes
 .../figures/TPB_model.png                     | Bin 0 -> 29183 bytes
 .../figures/TPB_pf_profile.png                | Bin 0 -> 21822 bytes
 .../figures/experiment_data_lower_limit.csv   | 160 +++++++
 .../figures/experiment_data_upper_limit.csv   | 171 ++++++++
 .../benchmarks/phase-field/pf_tpb/TPB_exp.png | Bin 6210 -> 0 bytes
 .../pf_tpb/VPF_ME1_NF_CMOD_comp.png           | Bin 79238 -> 0 bytes
 .../phase-field/pf_tpb/VPF_ME1_frac.png       | Bin 21887 -> 0 bytes
 .../benchmarks/phase-field/pf_tpb/index.md    |  25 --
 14 files changed, 1063 insertions(+), 25 deletions(-)
 create mode 100644 Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.gml
 create mode 100644 Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb
 create mode 100644 Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.prj
 create mode 100644 Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.vtu
 create mode 100644 Tests/Data/PhaseField/tpb_jupyter_notebook/figures/F_vs_u.png
 create mode 100644 Tests/Data/PhaseField/tpb_jupyter_notebook/figures/TPB_model.png
 create mode 100644 Tests/Data/PhaseField/tpb_jupyter_notebook/figures/TPB_pf_profile.png
 create mode 100644 Tests/Data/PhaseField/tpb_jupyter_notebook/figures/experiment_data_lower_limit.csv
 create mode 100644 Tests/Data/PhaseField/tpb_jupyter_notebook/figures/experiment_data_upper_limit.csv
 delete mode 100644 web/content/docs/benchmarks/phase-field/pf_tpb/TPB_exp.png
 delete mode 100644 web/content/docs/benchmarks/phase-field/pf_tpb/VPF_ME1_NF_CMOD_comp.png
 delete mode 100644 web/content/docs/benchmarks/phase-field/pf_tpb/VPF_ME1_frac.png
 delete mode 100644 web/content/docs/benchmarks/phase-field/pf_tpb/index.md

diff --git a/ProcessLib/PhaseField/Tests.cmake b/ProcessLib/PhaseField/Tests.cmake
index 0fffea4336e..3bb449833f0 100644
--- a/ProcessLib/PhaseField/Tests.cmake
+++ b/ProcessLib/PhaseField/Tests.cmake
@@ -140,4 +140,5 @@ AddTest(
 if(OGS_USE_PETSC)
     NotebookTest(NOTEBOOKFILE PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb RUNTIME 25)
     NotebookTest(NOTEBOOKFILE PhaseField/beam_jupyter_notebook/beam.ipynb RUNTIME 500 PROPERTIES PROCESSORS 3)
+    NotebookTest(NOTEBOOKFILE PhaseField/tpb_jupyter_notebook/TPB.ipynb RUNTIME 110 PROPERTIES PROCESSORS 4)
 endif()
diff --git a/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.gml b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.gml
new file mode 100644
index 00000000000..e731699baaa
--- /dev/null
+++ b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.gml
@@ -0,0 +1,13 @@
+<?xml version="1.0" encoding="ISO-8859-1"?>
+<?xml-stylesheet type="text/xsl" href="OpenGeoSysGLI.xsl"?>
+
+<OpenGeoSysGLI xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="http://www.opengeosys.org/images/xsd/OpenGeoSysGLI.xsd" xmlns:ogs="http://www.opengeosys.org">
+    <name>TPB</name>
+    <points>
+        <point id="0" x="0" y="0" z="0" name="left"/>
+        <point id="1" x="450" y="0" z="0" name="right"/>
+        <point id="2" x="225" y="100" z="0" name="top"/>
+
+    </points>
+
+</OpenGeoSysGLI>
diff --git a/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb
new file mode 100644
index 00000000000..553163e51cb
--- /dev/null
+++ b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb
@@ -0,0 +1,400 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "90c995c2",
+   "metadata": {},
+   "source": [
+    "notebook = \"PhaseField/TPB_jupyter_notebook/TPB.ipynb\"\n",
+    "author = \"Tao You, Keita Yoshioka, Mostafa Mollaali\"\n",
+    "date = \"2022-11-28\"\n",
+    "title = \"Three point bending test\"\n",
+    "web_subsection = \"phase-field\"\n",
+    "<!--eofm-->"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "41d0a5bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ogs6py import ogs\n",
+    "import os\n",
+    "import shutil\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pyvista as pv\n",
+    "import time\n",
+    "import pandas as pd\n",
+    "from xml.dom import minidom\n",
+    "from types import MethodType"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa987d95",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Problem description\n",
+    "\n",
+    "The notched beam under three-point bending is simulated to show how the phase field models can capture crack propagation and the structural strength. The geometry and boundary conditions of the model are depicted below. The bottom left point is fixed on both $x$ and $y$ directions and bottom right point is fixed on the $y$ direction. A displacement boundary condition $u^{\\ast}$ is applied on the midpoint of the upper edge. The crack will initiate at the notch tip and propagate upward to the upper edge.\n",
+    "\n",
+    "* Three phase field models are implemented in OGS, i.e., $\\texttt{COHESIVE}$, $\\texttt{AT}_1$ and $\\texttt{AT}_2$.\n",
+    "* The energy-split models include *EffectiveStress*, *Isotropic* and the *VolumetricDeviatoric*. \n",
+    "* The softening rules implemented for the $\\texttt{COHESIVE}$ model are *Linear* and *Exponential*.\n",
+    "\n",
+    "The force-displacement curves ($F^{\\ast}$-$u^{\\ast}$) for various sets of the above models will be recorded and compared with the experimental data. \n",
+    "\n",
+    "![Model](./figures/TPB_model.png \"Model\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ac26dee",
+   "metadata": {},
+   "source": [
+    "## Define some helper functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b137ce19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = os.environ.get('OGS_DATA_DIR', '../../..')\n",
+    "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n",
+    "if not os.path.exists(out_dir):\n",
+    "    os.makedirs(out_dir)    \n",
+    "\n",
+    "output_dir= out_dir\n",
+    "\n",
+    "# define function to replace a specific curve, given by name \n",
+    "def replace_curve(self, name=None, value=None, coords=None, parametertype=None, valuetag=\"values\", coordstag=\"coords\"):\n",
+    "    root = self._get_root()\n",
+    "    parameterpath = \"./curves/curve\"\n",
+    "    parameterpointer = self._get_parameter_pointer(root, name, parameterpath)\n",
+    "    self._set_type_value(parameterpointer, value, parametertype, valuetag=valuetag)\n",
+    "    self._set_type_value(parameterpointer, coords, parametertype, valuetag=coordstag)\n",
+    "# define function to change time_stepping in project file    \n",
+    "def set_timestepping(model,repeat_list, delta_t_list):\n",
+    "    model.remove_element(xpath='./time_loop/processes/process/time_stepping/timesteps/pair')\n",
+    "    for i in range(len(repeat_list)):\n",
+    "        model.add_block(blocktag = 'pair',parent_xpath='./time_loop/processes/process/time_stepping/timesteps', taglist = ['repeat', 'delta_t'], textlist = [repeat_list[i], delta_t_list[i]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7d09975",
+   "metadata": {},
+   "source": [
+    "## Define functions generating mesh, modifying project file and running ogs with given parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "09e560e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ogs_TPB(phasefield_model, energy_split_model, softening_curve = \"Linear\", length_scale = 5., bc_displacement = -1., ts_coords='0 1.0', values ='0 1.0', repeat_list=None, delta_t_list=None, hypre = False, MPI = True, ncores = 4):\n",
+    "   ## define input file\n",
+    "\n",
+    "    without_hypre='-ksp_type cg -pc_type bjacobi -ksp_atol 1e-14 -ksp_rtol 1e-14'\n",
+    "    with_hypre='-ksp_type cg -pc_type hypre -pc_hypre_type boomeramg -pc_hypre_boomeramg_strong_threshold 0.7 -ksp_atol 1e-8 -ksp_rtol 1e-8' \n",
+    "\n",
+    "    prj_name = \"TPB.prj\"\n",
+    "    print(f\"> Running three point bending test {phasefield_model} - {energy_split_model} - {softening_curve} ... <\")\n",
+    "    logfile = f\"{out_dir}/log_{phasefield_model}_{energy_split_model}.txt\"\n",
+    "    model = ogs.OGS(INPUT_FILE=prj_name, PROJECT_FILE=f\"{out_dir}/{prj_name}\", MKL=True)\n",
+    "    #generate prefix from properties\n",
+    "    prefix = f\"{phasefield_model}\" + f\"_{energy_split_model}\"\n",
+    "    if phasefield_model == 'COHESIVE':\n",
+    "        prefix = f\"{phasefield_model}\" + f\"_{energy_split_model}\" + f\"_{softening_curve}\"\n",
+    "\n",
+    "    if MPI:\n",
+    "        #partition mesh \n",
+    "        ! NodeReordering -i TPB.vtu -o {out_dir}/TPB.vtu >> {logfile}\n",
+    "        ! constructMeshesFromGeometry -m {out_dir}/TPB.vtu -g TPB.gml >> {logfile}\n",
+    "        shutil.move(\"TPB_left.vtu\",f\"{out_dir}/TPB_left.vtu\")\n",
+    "        shutil.move(\"TPB_right.vtu\",f\"{out_dir}/TPB_right.vtu\")\n",
+    "        shutil.move(\"TPB_top.vtu\",f\"{out_dir}/TPB_top.vtu\")\n",
+    "        ! partmesh -s -o {out_dir} -i {out_dir}/TPB.vtu >> {logfile}\n",
+    "        ! partmesh -m -n {ncores} -o {out_dir} -i {out_dir}/TPB.vtu -- {out_dir}/TPB_right.vtu {out_dir}/TPB_left.vtu {out_dir}/TPB_top.vtu >> {logfile}\n",
+    "    else :\n",
+    "        ! NodeReordering -i TPB.vtu -o {out_dir}/TPB.vtu >> {logfile}\n",
+    "    #change properties in prj file\n",
+    "    model = ogs.OGS(INPUT_FILE=prj_name, PROJECT_FILE=f\"{out_dir}/{prj_name}\", MKL=True)\n",
+    "    model.replace_parameter_value(name=\"ls\", value=length_scale)\n",
+    "    model.replace_text(phasefield_model, xpath=\"./processes/process/phasefield_model\")\n",
+    "    model.replace_text(energy_split_model, xpath=\"./processes/process/energy_split_model\")\n",
+    "    model.replace_text(softening_curve, xpath=\"./processes/process/softening_curve\")\n",
+    "    model.replace_text(prefix, xpath=\"./time_loop/output/prefix\")\n",
+    "    model.replace_parameter_value(name=\"dirichlet_load\", value=bc_displacement)\n",
+    "    model.replace_curve = MethodType(replace_curve, model)\n",
+    "    model.replace_curve(name=\"dirichlet_time\",value=values, coords=ts_coords)\n",
+    "    if repeat_list != None and delta_t_list != None: \n",
+    "        set_timestepping(model,repeat_list, delta_t_list)\n",
+    "    else:\n",
+    "        set_timestepping(model,['1'], ['1e-2']) \n",
+    "    if hypre == True:\n",
+    "        model.replace_text(with_hypre, xpath='./linear_solvers/linear_solver/petsc/parameters',occurrence=1)\n",
+    "    else:\n",
+    "        model.replace_text(without_hypre, xpath='./linear_solvers/linear_solver/petsc/parameters', occurrence=1)\n",
+    "    model.replace_text(\"./TPB.gml\", xpath=\"./geometry\")\n",
+    "    model.write_input()\n",
+    "    #run ogs\n",
+    "    t0 = time.time()\n",
+    "    if MPI:\n",
+    "        print(\"  > OGS started execution with MPI - \" f\"{ncores} cores...\")\n",
+    "        ! mpirun -np {ncores} ogs {out_dir}/{prj_name} -o {output_dir} >> {logfile}\n",
+    "    else :\n",
+    "        print(\"  > OGS started execution ...\")\n",
+    "        ! ogs {out_dir}/{prj_name} -o {output_dir} >> {logfile}\n",
+    "    tf = time.time()\n",
+    "    print(\"  > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "324f380a",
+   "metadata": {},
+   "source": [
+    "## Input data\n",
+    "\n",
+    "The general parameters are chosen as follows.\n",
+    "\n",
+    "\n",
+    "| **Name**                       | **Value**          | **Unit**     | **Symbol** |\n",
+    "|--------------------------------|--------------------|--------------|------------|\n",
+    "| _Young's modulus_              | 20000               | MPa          | $E$        |\n",
+    "| _Poisson's ratio_              | 0.2                | $-$          | $v$        |\n",
+    "| _Critical energy release rate_ | 0.113              | N/mm          | $G_{c}$    |\n",
+    "| _Regularization parameter_     | 5                  | mm           | $\\ell$     |\n",
+    "| _Minimum element size_         | 1                  | mm           | $h$        |\n",
+    "\n",
+    "The material parameters for the **COHESIVE** model are as follows.\n",
+    "\n",
+    "| **Name**                       | **Value**          | **Unit**     | **Symbol** |\n",
+    "|--------------------------------|--------------------|--------------|------------|\n",
+    "| _Tensile strength_               | 2.4           | MPa          | $f_t$       |\n",
+    "| _Irwin's length_                 | 392.4          | mm           | $\\ell_{ch}$ |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02482e8b",
+   "metadata": {},
+   "source": [
+    "## Run Simulations\n",
+    "> In the following, coarse time steps are chosen for the sake of less computational cost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "71c6b710",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> Running three point bending test COHESIVE - EffectiveStress - Linear ... <\n",
+      "  > OGS started execution with MPI - 4 cores...\n",
+      "  > OGS terminated execution. Elapsed time:  23.58  s.\n",
+      "> Running three point bending test COHESIVE - EffectiveStress - Exponential ... <\n",
+      "  > OGS started execution with MPI - 4 cores...\n",
+      "  > OGS terminated execution. Elapsed time:  37.58  s.\n",
+      "> Running three point bending test AT1 - Isotropic - Linear ... <\n",
+      "  > OGS started execution with MPI - 4 cores...\n",
+      "  > OGS terminated execution. Elapsed time:  21.56  s.\n",
+      "> Running three point bending test AT2 - Isotropic - Linear ... <\n",
+      "  > OGS started execution with MPI - 4 cores...\n",
+      "  > OGS terminated execution. Elapsed time:  21.17  s.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# phasefield_model = [\"COHESIVE\",'AT1', 'AT2']\n",
+    "# energy_split_model = [\"EffectiveStress\",'VolumetricDeviatoric','Isotropic']\n",
+    "# softening_curve = ['Line','Exponential']\n",
+    "\n",
+    "disp = -1.\n",
+    "ls = 5.\n",
+    "mpi_cores = 4 # MPI cores\n",
+    "## only run selected cases\n",
+    "# For the COHESIVE model\n",
+    "for a in [\"Linear\",\"Exponential\"]:\n",
+    "    ogs_TPB(\"COHESIVE\", \"EffectiveStress\", a, length_scale = ls, bc_displacement = disp, repeat_list=['1'], delta_t_list=['1e-1'], ncores = mpi_cores)\n",
+    "\n",
+    "# For AT1 and AT2 models with isotropic split\n",
+    "for b in [\"AT1\",\"AT2\"]:\n",
+    "    ogs_TPB(b, \"Isotropic\", length_scale = ls, bc_displacement = disp, repeat_list=['1'], delta_t_list=['1e-1'], ncores = mpi_cores)\n",
+    "                "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44016656",
+   "metadata": {},
+   "source": [
+    "## Results\n",
+    "\n",
+    "The phase field profile in the final time step of the *COHESIVE EffectiveStress* case with *Exponential* softening is shown below.\n",
+    "\n",
+    "![Phase-field](./figures/TPB_pf_profile.png \"Phase-field\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2b7386b",
+   "metadata": {},
+   "source": [
+    "## Post-processing\n",
+    "The force-displacement curves ($F^{\\ast}$-$u^{\\ast}$) applied to the beam are compared."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b1357ab9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define function to obtain displacement applied at the loading point from vtu file\n",
+    "def displ_midpoint(filename):\n",
+    "    data = pv.read(filename)\n",
+    "    return np.sum(data.point_data[\"displacement\"][:,1], where= (data.points[:,0]==225) * (data.points[:,1]==100))\n",
+    "# define function to obtain force at the loading point from vtu file\n",
+    "def force_midpoint(filename):\n",
+    "    data = pv.read(filename)\n",
+    "    return np.sum(data.point_data[\"NodalForces\"][:,1], where= (data.points[:,0]==225) * (data.points[:,1]==100)) / 10.0\n",
+    "# define function to apply the above functions on all vtu files listed in pvd file, returning force-displacement curves\n",
+    "def force_displ_from_pvd(pvd):\n",
+    "    doc = minidom.parse(pvd)\n",
+    "    DataSets = doc.getElementsByTagName(\"DataSet\")\n",
+    "    vtu_files = [x.getAttribute(\"file\") for x in DataSets]\n",
+    "    forces_midpoint = [force_midpoint(f\"{out_dir}/{x}\") for x in vtu_files]\n",
+    "    displs_midpoint = [displ_midpoint(f\"{out_dir}/{x}\") for x in vtu_files]\n",
+    "    return [forces_midpoint,displs_midpoint]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5375302a",
+   "metadata": {},
+   "source": [
+    "## Plot force-displacement curves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "73ae2d9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load experimental data\n",
+    "data_lower = pd.read_csv(f\"figures/experiment_data_lower_limit.csv\")  \n",
+    "data_upper = pd.read_csv(f\"figures/experiment_data_upper_limit.csv\")  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5ec1718d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PolyCollection at 0x7f4cdd329330>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prefixes = ['AT1_Isotropic','AT2_Isotropic','COHESIVE_EffectiveStress_Linear','COHESIVE_EffectiveStress_Exponential']\n",
+    "labels = [r'${AT}_1$ Isotropic',r'${AT}_2$ Isotropic',r'${COHESIVE}$ EffectiveStress Linear',r'${COHESIVE}$ EffectiveStress Exponential']\n",
+    "ls=['-.','--', '.', '-']\n",
+    "colors = ['#ffdf4d','#006ddb','#8f4e00','#ff6db6']\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "for i,pre in enumerate(prefixes):\n",
+    "    pvd = f\"{output_dir}/{pre}.pvd\"\n",
+    "    if os.path.isfile(pvd) :\n",
+    "        curve = force_displ_from_pvd(pvd)\n",
+    "        plt.plot(curve[1],curve[0],ls[i%2],label = labels[i], linewidth=2, color = colors[i], alpha= 1)\n",
+    "\n",
+    "plt.rcParams['xtick.labelsize'] = 12\n",
+    "plt.rcParams['ytick.labelsize'] = 12 \n",
+    "ax.grid(linestyle='dashed')\n",
+    "ax.set_xlabel('$u^{\\\\ast}$ [mm]',fontsize =12)\n",
+    "ax.set_ylabel('$F^{\\\\ast}$ [kN]',fontsize =12)\n",
+    "plt.xlim(plt.xlim()[::-1])\n",
+    "plt.ylim(plt.ylim()[::-1])\n",
+    "plt.legend(ncol = 1)\n",
+    "ax.axhline(y = 0, color = 'black',linewidth=1)\n",
+    "ax.axvline(x = 0, color = 'black',linewidth=1)\n",
+    "plt.fill_between(data_upper.iloc[:,0],0,data_upper.iloc[:,1], facecolor='green', alpha=0.3)\n",
+    "plt.fill_between(data_lower.iloc[:,0],0,data_lower.iloc[:,1], facecolor='white', alpha=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9346910f",
+   "metadata": {},
+   "source": [
+    "Running all the selected cases with fine time steps (100 steps) yields the following figure in which the green patch bounds the experimental data. The cohesive models match the experimental results better than $\\texttt{AT}_1$ and $\\texttt{AT}_2$. \n",
+    "\n",
+    "![Results](./figures/F_vs_u.png \"Results\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.6 64-bit",
+   "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": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.prj b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.prj
new file mode 100644
index 00000000000..07fe9d9f9f8
--- /dev/null
+++ b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.prj
@@ -0,0 +1,294 @@
+<?xml version="1.0" encoding="ISO-8859-1"?>
+<OpenGeoSysProject>
+    <meshes>
+        <mesh>TPB.vtu</mesh>
+        <mesh>TPB_left.vtu</mesh>
+        <mesh>TPB_right.vtu</mesh>
+        <mesh>TPB_top.vtu</mesh>
+    </meshes>
+    <processes>
+        <process>
+            <name>PhaseField</name>
+            <type>PHASE_FIELD</type>
+            <coupling_scheme>staggered</coupling_scheme>
+            <integration_order>2</integration_order>
+            <phasefield_model>COHESIVE</phasefield_model>
+            <softening_curve>Linear</softening_curve>
+            <energy_split_model>EffectiveStress</energy_split_model>
+            <irreversible_threshold>0.05</irreversible_threshold>
+            <characteristic_length>392.4</characteristic_length> 
+            <constitutive_relation>
+                <type>LinearElasticIsotropic</type>
+                <youngs_modulus>E</youngs_modulus>
+                <poissons_ratio>nu</poissons_ratio>
+            </constitutive_relation>
+            <phasefield_parameters>
+                <residual_stiffness>k</residual_stiffness>
+                <crack_resistance>gc</crack_resistance>
+                <crack_length_scale>ls</crack_length_scale>
+            </phasefield_parameters>
+            <solid_density>rho_sr</solid_density>
+            <process_variables>
+                <phasefield>phasefield</phasefield>
+                <displacement>displacement</displacement>
+            </process_variables>
+            <secondary_variables>
+                <secondary_variable type="static" internal_name="sigma" output_name="sigma"/>
+                <secondary_variable type="static" internal_name="epsilon" output_name="epsilon"/>
+            </secondary_variables>
+            <specific_body_force>0 0</specific_body_force>
+        </process>
+    </processes>
+    <time_loop>
+        <global_process_coupling>
+            <max_iter> 1000 </max_iter>
+            <convergence_criteria>
+                <!-- convergence criterion for the first process -->
+                <convergence_criterion>
+                    <type>DeltaX</type>
+                    <norm_type>INFINITY_N</norm_type>
+                    <abstol>1.e-1</abstol>
+                    <reltol>1.e-1</reltol>
+                </convergence_criterion>
+                <!-- convergence criterion for the second process -->
+                <convergence_criterion>
+                    <type>DeltaX</type>
+                    <norm_type>INFINITY_N</norm_type>
+                    <abstol>1.e-10</abstol>
+                    <reltol>1.e-4</reltol>
+                </convergence_criterion>
+            </convergence_criteria>
+        </global_process_coupling>
+        <processes>
+            <process ref="PhaseField">
+                <nonlinear_solver>basic_newton_u</nonlinear_solver>
+                <convergence_criterion>
+                    <type>DeltaX</type>
+                    <norm_type>NORM2</norm_type>
+                    <reltol>1.e-10</reltol>
+                </convergence_criterion>
+                <time_discretization>
+                    <type>BackwardEuler</type>
+                </time_discretization>
+                <time_stepping>
+                    <type>FixedTimeStepping</type>
+                    <t_initial>0</t_initial>
+                    <t_end>1.0</t_end>
+                    <timesteps>
+                        <pair>
+                            <repeat>1</repeat>
+                            <delta_t>1.e-2</delta_t>
+                        </pair>
+                    </timesteps>
+                </time_stepping>
+            </process>
+            <process ref="PhaseField">
+                <nonlinear_solver>petsc_snes</nonlinear_solver>
+                <convergence_criterion>
+                    <type>DeltaX</type>
+                    <norm_type>NORM2</norm_type>
+                    <reltol>1.e-10</reltol>
+                </convergence_criterion>
+                <time_discretization>
+                    <type>BackwardEuler</type>
+                </time_discretization>
+                <time_stepping>
+                    <type>FixedTimeStepping</type>
+                    <t_initial>0</t_initial>
+                    <t_end>1.0</t_end>
+                    <timesteps>
+                        <pair>
+                            <repeat>1</repeat>
+                            <delta_t>1.e-2</delta_t>
+                        </pair>
+                    </timesteps>
+                </time_stepping>
+            </process>
+        </processes>
+        <output>
+            <variables>
+                <variable>displacement</variable>
+                <variable>phasefield</variable>
+                <variable>sigma</variable>
+                <variable>epsilon</variable>
+            </variables>
+            <type>VTK</type>
+            <prefix>TPB_</prefix>
+            <timesteps>
+                <pair>
+                    <repeat>10</repeat>
+                    <each_steps>1</each_steps>
+                </pair>
+                <pair>
+                    <repeat>10</repeat>
+                    <each_steps>1</each_steps>
+                </pair>
+            </timesteps>
+        </output>
+    </time_loop>
+    <parameters>
+        <!-- Mechanics -->
+        <parameter>
+            <name>E</name>
+            <type>Constant</type>
+            <value>20000.0</value>
+        </parameter>
+        <parameter>
+            <name>nu</name>
+            <type>Constant</type>
+            <value>0.2</value>
+        </parameter>
+        <parameter>
+            <name>k</name>
+            <type>Constant</type>
+            <value>1e-16</value>
+        </parameter>
+        <parameter>
+            <name>gc</name>
+            <type>Constant</type>
+            <value>0.113</value>
+        </parameter>
+        <parameter>
+            <name>ls</name>
+            <type>Constant</type>
+            <value>5</value>
+        </parameter>
+        <parameter>
+            <name>rho_sr</name>
+            <type>Constant</type>
+            <value>0.0</value>
+        </parameter>
+        <parameter>
+            <name>displacement0</name>
+            <type>Constant</type>
+            <values>0 0</values>
+        </parameter>
+        <parameter>
+            <name>phasefield_ic</name>
+            <type>Constant</type>
+            <value>1</value>
+        </parameter>
+        <parameter>
+            <name>phasefield_bc</name>
+            <type>Constant</type>
+            <value>1</value>
+        </parameter>
+        <parameter>
+            <name>dirichlet0</name>
+            <type>Constant</type>
+            <value>0</value>
+        </parameter>
+        <parameter>
+            <name>dirichlet_top_y</name>
+            <type>CurveScaled</type>
+            <curve>dirichlet_time</curve>
+            <parameter>dirichlet_load</parameter>
+        </parameter>
+        <parameter>
+            <name>dirichlet_load</name>
+            <type>Constant</type>
+            <value>-1.0</value>
+        </parameter>
+    </parameters>
+    <curves>
+        <curve>
+            <name>dirichlet_time</name>
+            <coords>0  1.0</coords>
+            <values>0  1.0</values>
+        </curve>
+    </curves>
+    <process_variables>
+        <process_variable>
+            <name>phasefield</name>
+            <components>1</components>
+            <order>1</order>
+            <initial_condition>phasefield_ic</initial_condition>
+            <boundary_conditions>
+                <boundary_condition>
+                    <geometrical_set>TPB</geometrical_set>
+                    <geometry>left</geometry>
+                    <type>Dirichlet</type>
+                    <component>0</component>
+                    <parameter>phasefield_bc</parameter>
+                </boundary_condition>
+                <boundary_condition>
+                    <geometrical_set>TPB</geometrical_set>
+                    <geometry>top</geometry>
+                    <type>Dirichlet</type>
+                    <component>0</component>
+                    <parameter>phasefield_bc</parameter>
+                </boundary_condition>
+            </boundary_conditions>
+        </process_variable>
+        <process_variable>
+            <name>displacement</name>
+            <components>2</components>
+            <order>1</order>
+            <initial_condition>displacement0</initial_condition>
+            <boundary_conditions>
+                <boundary_condition>
+                    <geometrical_set>TPB</geometrical_set>
+                    <geometry>left</geometry>
+                    <type>Dirichlet</type>
+                    <component>0</component>
+                    <parameter>dirichlet0</parameter>
+                </boundary_condition>
+                <boundary_condition>
+                    <geometrical_set>TPB</geometrical_set>
+                    <geometry>left</geometry>
+                    <type>Dirichlet</type>
+                    <component>1</component>
+                    <parameter>dirichlet0</parameter>
+                </boundary_condition>
+                <boundary_condition>
+                    <geometrical_set>TPB</geometrical_set>
+                    <geometry>right</geometry>
+                    <type>Dirichlet</type>
+                    <component>1</component>
+                    <parameter>dirichlet0</parameter>
+                </boundary_condition>
+                <boundary_condition>
+                    <geometrical_set>TPB</geometrical_set>
+                    <geometry>top</geometry>
+                    <type>Dirichlet</type>
+                    <component>1</component>
+                    <parameter>dirichlet_top_y</parameter>
+                </boundary_condition>
+            </boundary_conditions>
+        </process_variable>
+    </process_variables>
+    <nonlinear_solvers>
+        <nonlinear_solver>
+            <name>petsc_snes</name>
+            <type>PETScSNES</type>
+            <max_iter>50</max_iter>
+            <linear_solver>linear_solver_d</linear_solver>
+        </nonlinear_solver>
+        <nonlinear_solver>
+            <name>basic_newton_u</name>
+            <type>Newton</type>
+            <max_iter>200</max_iter>
+            <linear_solver>linear_solver_u</linear_solver>
+        </nonlinear_solver>
+    </nonlinear_solvers>
+    <linear_solvers>
+        <linear_solver>
+            <name>linear_solver_d</name>
+            <eigen>
+                <solver_type>BiCGSTAB</solver_type>
+                <precon_type>ILUT</precon_type>
+                <max_iteration_step>10000</max_iteration_step>
+                <error_tolerance>1e-16</error_tolerance>
+            </eigen>
+            <petsc>
+                <parameters>-ksp_type cg -pc_type bjacobi -ksp_atol 1e-10 -ksp_rtol 1e-10 -snes_type vinewtonrsls -snes_linesearch_type l2 -snes_atol 1.e-8 -snes_rtol 1.e-8 -snes_max_it 1000 -snes_monitor </parameters>
+            </petsc>
+        </linear_solver>
+        <linear_solver>
+            <name>linear_solver_u</name>
+            <petsc>
+                <parameters>-ksp_type cg -pc_type bjacobi -ksp_atol 1e-14 -ksp_rtol 1e-14 </parameters>
+            </petsc>
+        </linear_solver>
+    </linear_solvers>
+</OpenGeoSysProject>
diff --git a/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.vtu b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.vtu
new file mode 100644
index 00000000000..efe6c8092b9
--- /dev/null
+++ b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.vtu
@@ -0,0 +1,24 @@
+<?xml version="1.0"?>
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+      <Cells>
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+        <DataArray type="Int64" Name="offsets" format="appended" RangeMin=""                     RangeMax=""                     offset="383856"              />
+        <DataArray type="UInt8" Name="types" format="appended" RangeMin=""                     RangeMax=""                     offset="425916"              />
+      </Cells>
+    </Piece>
+  </UnstructuredGrid>
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new file mode 100644
index 00000000000..b748dec6a18
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diff --git a/web/content/docs/benchmarks/phase-field/pf_tpb/index.md b/web/content/docs/benchmarks/phase-field/pf_tpb/index.md
deleted file mode 100644
index 0714df5dee0..00000000000
--- a/web/content/docs/benchmarks/phase-field/pf_tpb/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-+++
-author = "Keita Yoshioka"
-date = "2020-07-27"
-title = "Three point bending test"
-image = "VPF_ME1_NF_CMOD_comp.png"
-+++
-
-{{< data-link >}}
-
-## Problem description
-
-**Note**, this project file runs only with a modified version of OGS
-which you can find [here](https://github.com/KeitaYoshioka/ogs/tree/H2M_phasefield).
-
-We simulate a three point bending test performed on Rockville granite as shown below. Details of the experiment can be found in Tarok et al. 2017.
-{{< img src="TPB_exp.png" >}}
-
-## Results and evaluation
-
-Developed crack (phase-field) and the crack mouth opening displacement (CMOD) vs. the force are shown below.
-
-{{< img src="VPF_ME1_frac.png" >}}
-{{< img src="VPF_ME1_NF_CMOD_comp.png" >}}
-
-The model is able to simulate up to the brittle elastic failure, but as cracked surfaces are currently treated as frictionless, the behavior after the failure deviates from the experiment results.
-- 
GitLab