{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "530d4134-08d9-4cf6-8e40-05cb2b106957",
   "metadata": {},
   "source": [
    "# DAMASK tutorial\n",
    "\n",
    "- creating necessary inputs for damask\n",
    "- defining the elastoplastic model(isotropic hardening) for tensile test\n",
    "- runing the damask jobs\n",
    "\n",
    "here more option is given to the user to select from damask python package itself.\n",
    "\n",
    "Author: Yang Bai\n",
    "\n",
    "Date  : 23.02.2022"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "413799b5-58a8-47d3-bfd1-d081c1f2c4ec",
   "metadata": {},
   "source": [
    "## Importing libraries and creatign Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "12795c73-ee40-4a15-8e52-60edc207fbf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron_continuum import Project\n",
    "from damask import Rotation # this will be used in material configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de196129-3f7b-4b70-863b-3a762f116877",
   "metadata": {},
   "source": [
    "### create a 'project' to manage all the configurations for a tensile test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "435b1538-9181-4223-a475-aa338c8b09da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 21.97it/s]\n"
     ]
    }
   ],
   "source": [
    "pr = Project('isotropic')\n",
    "pr.remove_jobs_silently(recursive=True) # automatically delete the existing project folder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c7dc7e-c88b-483b-9a82-e149dbe6f435",
   "metadata": {},
   "source": [
    "### Creating the Damask job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d7080422-6e97-48d7-8b76-f97f20eb32c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "job = pr.create.job.DAMASK('damask_job')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f30c5306-836f-4f96-93a0-4104e4a6afeb",
   "metadata": {},
   "source": [
    "#### for a damask job, one needs:\n",
    "- geometry information(i.e., the mesh)\n",
    "- material configuration(material.yaml)\n",
    "- boundary conditions(i.e., loading.yaml)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7d3a58-e559-4a60-8777-76292837a8e1",
   "metadata": {},
   "source": [
    "### For material configuration\n",
    "#### for elastoplastic material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d5b9320-b0ff-4b28-ba2a-9dea93e67f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "elasticity = pr.continuum.damask.Elasticity(type= 'Hooke', C_11= 106.75e9,\n",
    "                                   C_12= 60.41e9, C_44=28.34e9)\n",
    "plasticity = pr.continuum.damask.Plasticity(type='isotropic',dot_gamma_0=0.001,\n",
    "                                            n=20.,xi_0=0.3e+6,\n",
    "                                            xi_inf=0.6e+6,a=2.,\n",
    "                                            h_0=1.e+6, # hardening modulus\n",
    "                                            M=1.,h=1.,\n",
    "                                            dilatation=True,output=['xi'])\n",
    "# for the details of isotropic model, one is referred to https://doi.org/10.1016/j.scriptamat.2017.09.047"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a214949-102d-4343-90be-1b0a4b8f0818",
   "metadata": {},
   "source": [
    "#### for material configuration, you need\n",
    "- phase\n",
    "- roation\n",
    "- homogenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "46f5a2f0-4082-473e-953a-c5a0cb48a613",
   "metadata": {},
   "outputs": [],
   "source": [
    "phase = pr.continuum.damask.Phase(composition='Aluminum', lattice= 'cF',\n",
    "                         output_list=['F', 'P', 'F_e', 'F_p', 'L_p', 'O'],\n",
    "                         elasticity=elasticity,plasticity=plasticity)\n",
    "rotation = pr.continuum.damask.Rotation(Rotation.from_random, 4)\n",
    "\n",
    "homogenization = pr.continuum.damask.Homogenization(method='SX', \n",
    "                                                     parameters={'N_constituents': 1,\n",
    "                                                                 \"mechanical\": {\"type\": \"pass\"}})\n",
    "\n",
    "# now you can define your material.yaml configuration\n",
    "material = pr.continuum.damask.Material([rotation],['Aluminum'], phase, homogenization)\n",
    "\n",
    "# now you can save your material to your job\n",
    "job.material = material"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b485772d-5c31-44df-a8b5-0faefaaafa5e",
   "metadata": {},
   "source": [
    "## For geometry information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c804ec6-476e-4f9c-bfc0-95e2d3a282f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = pr.continuum.damask.Grid.via_voronoi_tessellation(box_size=1.0e-5, grid_dim=16, num_grains=4)\n",
    "\n",
    "# save the geometry information to your job\n",
    "job.grid = grid "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1243eb3-a38d-4f36-a363-4e878d4d2cea",
   "metadata": {},
   "source": [
    "## For boundary conditions (loading)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e45ef751-39aa-4cc4-8835-47ef256620d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_step =[{'mech_bc_dict':{'dot_F':[1e-3,0,0, 0,'x',0,  0,0,'x'],\n",
    "                            'P':['x','x','x', 'x',0,'x',  'x','x',0]},\n",
    "            'discretization':{'t': 40.,'N': 20},\n",
    "            'additional': {'f_out': 5}\n",
    "           },{'mech_bc_dict':{'dot_F':[1e-3,0,0, 0,'x',0,  0,0,'x'],\n",
    "                              'P':['x','x','x', 'x',0,'x',  'x','x',0]},\n",
    "            'discretization':{'t': 60.,'N': 20},\n",
    "            'additional': {'f_out': 5}\n",
    "           }]\n",
    "solver = job.list_solvers()[0] # choose the mechanis solver\n",
    "job.loading = pr.continuum.damask.Loading(solver=solver, load_steps=load_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8dd97049-00a3-4e11-8077-8f18a958ee49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job damask_job was saved and received the ID: 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n",
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n",
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      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n",
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n",
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n",
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n",
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n",
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n",
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n",
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n",
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n"
     ]
    }
   ],
   "source": [
    "job.run() # running your job, if you want the parallelization you can modify your 'pyiron/damask/bin/run_damask_3.0.0.sh file'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c63b08ec-9259-45bf-ae16-da2792f8c5da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='$\\\\varepsilon_x$$_x$', ylabel='$\\\\sigma_x$$_x$(Pa)'>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the strain_xx vs stress_xx profile\n",
    "job.plot_stress_strain(component='xx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55ce390e-4d54-4a59-8940-cf74896054dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='$\\\\varepsilon_{vM}$', ylabel='$\\\\sigma_{vM}$ (Pa)'>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the vonMises-strain vs vonMises-stress profile\n",
    "job.plot_stress_strain(von_mises=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "41b87943-c574-453d-85a2-7dce53f4c6e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " ██████████████████████████████████████████████████ 100% ETA 0:00:00\n"
     ]
    }
   ],
   "source": [
    "job.writeresults2vtk() # you can also save all the field quantities to vtk files"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}