{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "yRlBDWe_7mpz"
   },
   "source": [
    "# TWIGL 2D reactor: multigroup Neutron Diffusion\n",
    "This notebook implements the 2D TWIGL reactor, based on the paper *Comparison of Alternating- Direction Time-Differencing Methods with Other Implicit Methods for the Solution of the Neutron Group-Diffusion Equations* (L. A. Hageman and J. B. Yasinsky, 1968)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "BzFeuQ9lhxyP"
   },
   "outputs": [],
   "source": [
    "from dolfinx.io import gmshio\n",
    "import gmsh\n",
    "from mpi4py import MPI\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import ufl\n",
    "from ufl import Measure\n",
    "from IPython.display import clear_output\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "import sys\n",
    "\n",
    "mesh_path      = '../../../mesh/'\n",
    "benchmark_path = '../../../BenchmarkData/'\n",
    "\n",
    "sys.path.append('../../../models/fenicsx')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preamble\n",
    "The geometry and the main physical parameters will be assigned."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "PGMTWXdTbANs"
   },
   "source": [
    "### Mesh Import\n",
    "The geometry and the mesh are imported from \"TWIGL2D.msh\", generated with GMSH."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdim = 2\n",
    "model_rank = 0\n",
    "mesh_comm = MPI.COMM_WORLD\n",
    "\n",
    "mesh_factor = 1.25\n",
    "\n",
    "# Initialize the gmsh module\n",
    "gmsh.initialize()\n",
    "\n",
    "# Load the .geo file\n",
    "gmsh.merge(mesh_path+'TWIGL2D.geo')\n",
    "gmsh.model.geo.synchronize()\n",
    "\n",
    "gmsh.option.setNumber(\"Mesh.MeshSizeFactor\", mesh_factor)\n",
    "\n",
    "gmsh.model.mesh.generate(gdim)\n",
    "gmsh.model.mesh.optimize(\"Netgen\")\n",
    "clear_output()\n",
    "\n",
    "# Domain\n",
    "domain, ct, ft = gmshio.model_to_mesh(gmsh.model, comm = mesh_comm, rank = model_rank, gdim = gdim )\n",
    "gmsh.finalize()\n",
    "    \n",
    "domain1_marker = 10\n",
    "domain2_marker = 20\n",
    "domain3_marker = 30\n",
    "\n",
    "boundary_marker = 1\n",
    "\n",
    "tdim = domain.topology.dim\n",
    "fdim = tdim - 1\n",
    "\n",
    "ds = ufl.Measure(\"ds\", domain=domain, subdomain_data=ft)\n",
    "dx = ufl.Measure(\"dx\", domain=domain)\n",
    "\n",
    "domain.topology.create_connectivity(fdim, tdim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define parameter functions on the different regions\n",
    "Since there are 3 subdomains in $\\Omega$ (i.e., fuel-1, fuel-2,  fuel-3) the values of the parameters changes according to the region, therefore proper functions should be defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions = [domain1_marker, domain2_marker, domain3_marker]    \n",
    "\n",
    "neutronics_param = dict()\n",
    "\n",
    "neutronics_param['Energy Groups'] = 2\n",
    "\n",
    "neutronics_param['D'] = [np.array([1.4, 1.4, 1.3]),\n",
    "                         np.array([0.4, 0.4, 0.5])]\n",
    "neutronics_param['xs_a'] = [np.array([0.01, 0.01, 0.008]),\n",
    "                            np.array([0.15, 0.15, 0.05])]\n",
    "neutronics_param['nu_xs_f'] = [np.array([0.007, 0.007, 0.003]),\n",
    "                               np.array([0.2, 0.2, 0.06])]\n",
    "neutronics_param['chi'] = [np.array([1,1,1]),\n",
    "                           np.array([0,0,0])]\n",
    "neutronics_param['B2z'] = [np.array([0,0,0]),\n",
    "                           np.array([0,0,0])]\n",
    "neutronics_param['xs_s'] = [[np.array([0,0,0]), np.array([0.01, 0.01, 0.01])],\n",
    "                            [np.array([0,0,0]), np.array([0,0,0])]]\n",
    "\n",
    "neutronics_param['v'] = np.array([1e7, 2e5]) #cm/s\n",
    "neutronics_param['beta_l'] =  np.array([ [0.0075, 0.0075, 0.0075] ])\n",
    "neutronics_param['lambda_p_l'] =  np.array([ [0.08, 0.08, 0.08] ]) # 1/s\n",
    "\n",
    "nu_value = 2.43\n",
    "Ef = 1\n",
    "reactor_power = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solution of the eigenvalue problem\n",
    "The MG diffusion equation is discretised using the Finite Element Method, and its eigenvalue formulation is solved through the standard inverse-power method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Iter 010 | k_eff: 0.912933 | Rel Error: 1.177e-04\n",
      "    Iter 020 | k_eff: 0.913200 | Rel Error: 5.592e-06\n",
      "    Iter 030 | k_eff: 0.913215 | Rel Error: 3.960e-07\n",
      "    Iter 040 | k_eff: 0.913217 | Rel Error: 2.931e-08\n",
      "    Iter 050 | k_eff: 0.913217 | Rel Error: 2.179e-09\n",
      "    Iter 060 | k_eff: 0.913217 | Rel Error: 1.620e-10\n",
      "    Neutronics converged with 062 iter | k_eff: 0.91321667 | rho: -9503.04 pcm | Rel Error: 9.636e-11\n"
     ]
    }
   ],
   "source": [
    "from neutronics.neutr_diff import steady_neutron_diff\n",
    "\n",
    "neutr_steady_problem = steady_neutron_diff(domain, ct, ft, neutronics_param, regions, boundary_marker)\n",
    "neutr_steady_problem.assembleForm()\n",
    "\n",
    "# Solving eigenvalue problem\n",
    "phi_ss, k_eff = neutr_steady_problem.solve(power = reactor_power, Ef=Ef, nu = nu_value, \n",
    "                                            LL = 10, maxIter = 500, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transient\n",
    "Now let us assess the capabilities of the solver in solving transient problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neutronics.neutr_diff import transient_neutron_diff\n",
    "\n",
    "neutronics_param['k_eff_0'] = k_eff\n",
    "neutronics_param['nu_xs_f'] = [np.array([0.007, 0.007, 0.003]) / k_eff,\n",
    "                               np.array([0.2, 0.2, 0.06]) / k_eff]\n",
    "\n",
    "neutr_trans_problem = transient_neutron_diff(domain, ct, ft, neutronics_param, regions, boundary_marker)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us create the structures to save the data of the transient simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "power_over_time = dict()\n",
    "power_over_time['Ramp'] = list()\n",
    "power_over_time['Step'] = list()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ramp Transient\n",
    "The absorption cross section in region 1 is decreased according to the following law\n",
    "\n",
    "$$\n",
    "\\Sigma_{a,2}(\\mathbf{x},t) = \\Sigma_{a,2}^{0}(\\mathbf{x})\\cdot \\left[ \\mathcal{H}(0.2-t)\\cdot (1-0.11667\\,t) + \\mathcal{H}(t-0.2)\\cdot 0.97666 \\right]\n",
    "$$\n",
    "\n",
    "given $\\mathbf{x}\\in\\Omega_1$ and $\\mathcal{H}(t)$ the Heaviside step function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dolfinx.io import XDMFFile\n",
    "\n",
    "ramp = lambda t: np.piecewise(t, \n",
    "                             [t <= 0, ((t > 0.) & (t < 0.2)), t >= 0.2],\n",
    "                             [lambda x: 0.0 * x, lambda x: 1 - 0.11667 * x, lambda x: 0.0 * x + 0.97666])\n",
    "dt = 1e-3\n",
    "neutr_trans_problem.assembleForm(phi_ss, dt, nu = nu_value, Ef = Ef)\n",
    "\n",
    "store_snaps = False\n",
    "\n",
    "if store_snaps:\n",
    "    fast_xdmf    = XDMFFile(domain.comm, \"Ramp/fast_flux.xdmf\", \"w\")\n",
    "    thermal_xdmf = XDMFFile(domain.comm, \"Ramp/thermal_flux.xdmf\", \"w\")\n",
    "\n",
    "    fast_xdmf.write_mesh(domain)\n",
    "    thermal_xdmf.write_mesh(domain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us solve this transient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ramp Transient:   0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ramp Transient: 100%|██████████| 500/500 [00:43<00:00, 12.03it/s]"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm \n",
    "\n",
    "xs_a1_transient = lambda t: np.array([neutronics_param['xs_a'][0][0], \n",
    "                                      neutronics_param['xs_a'][0][1],\n",
    "                                      neutronics_param['xs_a'][0][2]])\n",
    "\n",
    "xs_a2_transient = lambda t: np.array([neutronics_param['xs_a'][1][0] * ramp(t), \n",
    "                                      neutronics_param['xs_a'][1][1],\n",
    "                                      neutronics_param['xs_a'][1][2]])\n",
    "\n",
    "xs_a_transient = [xs_a1_transient, xs_a2_transient]\n",
    "\n",
    "t = 0.\n",
    "T = .5\n",
    "num_steps = int(T / dt)  \n",
    "\n",
    "power_over_time['Ramp'].append( np.array([0., reactor_power]) )\n",
    "\n",
    "prog_bar_ramp = tqdm(desc=\"Ramp Transient\", total=num_steps)\n",
    "\n",
    "while t < T:\n",
    "    t += dt\n",
    "    \n",
    "    neutr_res = neutr_trans_problem.advance(t, xs_a_transient)\n",
    "    \n",
    "    power_over_time['Ramp'].append(np.array([t, neutr_res[0]]))\n",
    "    \n",
    "    if store_snaps:\n",
    "        neutr_res[1][0].name = 'phi1'\n",
    "        neutr_res[1][1].name = 'phi2'\n",
    "        fast_xdmf.write_function(neutr_res[1][0], t)\n",
    "        thermal_xdmf.write_function(neutr_res[1][1], t)\n",
    "    \n",
    "    prog_bar_ramp.update(1)\n",
    "        \n",
    "power_over_time['Ramp'] = np.asarray(power_over_time['Ramp'])\n",
    "\n",
    "if store_snaps:\n",
    "    fast_xdmf.close()\n",
    "    thermal_xdmf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step Transient\n",
    "The absorption cross section in region 1 is decreased according to the following law\n",
    "\n",
    "$$\n",
    "\\Sigma_{a,2}(\\mathbf{x},t) = \\Sigma_{a,2}^{0}(\\mathbf{x})\\cdot \\left[ \\mathcal{H}(t)\\cdot 0.97666 \\right] \n",
    "$$\n",
    "\n",
    "given $\\mathbf{x}\\in\\Omega_1$ and $\\mathcal{H}(t)$ the Heaviside step function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "step = lambda t: np.piecewise(t, \n",
    "                             [t <= 0, t > 0.],\n",
    "                             [lambda x: 0.0 * x, lambda x: 0.0 * x + 0.97666])\n",
    "\n",
    "dt = 1e-3\n",
    "neutr_trans_problem.assembleForm(phi_ss, dt, nu = nu_value, Ef = Ef)\n",
    "\n",
    "if store_snaps:\n",
    "    fast_xdmf    = XDMFFile(domain.comm, \"Step/fast_flux.xdmf\", \"w\")\n",
    "    thermal_xdmf = XDMFFile(domain.comm, \"Step/thermal_flux.xdmf\", \"w\")\n",
    "\n",
    "    fast_xdmf.write_mesh(domain)\n",
    "    thermal_xdmf.write_mesh(domain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us solve this transient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ramp Transient: 100%|██████████| 500/500 [01:08<00:00,  7.31it/s]\n",
      "Step Transient: 100%|█████████▉| 499/500 [00:47<00:00, 11.66it/s]"
     ]
    }
   ],
   "source": [
    "xs_a1_transient = lambda t: np.array([neutronics_param['xs_a'][0][0], \n",
    "                                      neutronics_param['xs_a'][0][1],\n",
    "                                      neutronics_param['xs_a'][0][2]])\n",
    "\n",
    "xs_a2_transient = lambda t: np.array([neutronics_param['xs_a'][1][0] * step(t), \n",
    "                                      neutronics_param['xs_a'][1][1],\n",
    "                                      neutronics_param['xs_a'][1][2]])\n",
    "\n",
    "xs_a_transient = [xs_a1_transient, xs_a2_transient]\n",
    "\n",
    "t = 0.\n",
    "T = .5\n",
    "num_steps = int(T / dt)  \n",
    "\n",
    "power_over_time['Step'].append( np.array([0., reactor_power]) )\n",
    "\n",
    "del prog_bar_ramp\n",
    "prog_bar_ramp = tqdm(desc=\"Step Transient\", total=num_steps)\n",
    "\n",
    "while t < T:\n",
    "    t += dt\n",
    "    \n",
    "    neutr_res = neutr_trans_problem.advance(t, xs_a_transient)\n",
    "    \n",
    "    power_over_time['Step'].append(np.array([t, neutr_res[0]]))\n",
    "    \n",
    "    if store_snaps:\n",
    "        neutr_res[1][0].name = 'phi1'\n",
    "        neutr_res[1][1].name = 'phi2'\n",
    "        fast_xdmf.write_function(neutr_res[1][0], t)\n",
    "        thermal_xdmf.write_function(neutr_res[1][1], t)\n",
    "    \n",
    "    prog_bar_ramp.update(1)\n",
    "        \n",
    "power_over_time['Step'] = np.asarray(power_over_time['Step'])\n",
    "\n",
    "if store_snaps:\n",
    "    fast_xdmf.close()\n",
    "    thermal_xdmf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post-Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Step transient')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Step Transient: 100%|██████████| 500/500 [01:01<00:00, 11.66it/s]"
     ]
    }
   ],
   "source": [
    "# Load RAMP benchmark data\n",
    "df = pd.read_csv(benchmark_path+'neutronics/TWIGL2D/POLCAramp_Power_time.csv')\n",
    "\n",
    "bench_data = dict()\n",
    "bench_data['Ramp'] = dict()\n",
    "bench_data['Ramp']['t'] = df.to_numpy()[:, 0]\n",
    "bench_data['Ramp']['Power'] = df.to_numpy()[:, 1]\n",
    "\n",
    "# Load STEP benchmark data\n",
    "df = pd.read_csv(benchmark_path+'neutronics/TWIGL2D/POLCAstep_Power_time.csv')\n",
    "\n",
    "bench_data['Step'] = dict()\n",
    "bench_data['Step']['t'] = df.to_numpy()[:, 0]\n",
    "bench_data['Step']['Power'] = df.to_numpy()[:, 1]\n",
    "\n",
    "mark_size = 7\n",
    "ls = 2\n",
    "labelsize = 25\n",
    "tickssize = 20\n",
    "legend_size = 20\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,5))\n",
    "gs = fig.add_gridspec(1, 2, hspace=0.0, wspace=0.15)\n",
    "axs = gs.subplots(sharex=True, sharey=True)\n",
    "\n",
    "axs[0].plot(bench_data['Ramp']['t']-1, bench_data['Ramp']['Power'], 'o', color=cm.jet(0.15), markersize=mark_size, label=r'POLCA-T')\n",
    "axs[0].plot(power_over_time['Ramp'][:, 0], power_over_time['Ramp'][:, 1] / reactor_power, '-', color=cm.jet(0.85), linewidth=ls, label = r'dolfinx')\n",
    "axs[0].set_xlabel(r\"$t\\,(s)$\",fontsize=labelsize)\n",
    "axs[0].set_ylabel(r\"Normalised Power $\\frac{P}{P_0}\\,(-)$\",fontsize=labelsize)\n",
    "axs[0].set_xlim(0,T)\n",
    "axs[0].set_xticks(np.arange(0.0, .55, 0.1))\n",
    "axs[0].tick_params(axis='x', labelsize=tickssize)\n",
    "axs[0].tick_params(axis='y', labelsize=tickssize)\n",
    "axs[0].grid(which='major',linestyle='-')\n",
    "axs[0].grid(which='minor',linestyle='--')\n",
    "axs[0].legend(fontsize=legend_size, framealpha=1)\n",
    "axs[0].set_title('Ramp transient', fontsize=labelsize)\n",
    "\n",
    "axs[1].plot(bench_data['Step']['t']-1, bench_data['Step']['Power'], 's', color=cm.viridis(0.8), markersize=mark_size,  label=r'POLCA-T')\n",
    "axs[1].plot(power_over_time['Step'][:, 0], power_over_time['Step'][:, 1] / reactor_power, '-', color=cm.viridis(0.1), linewidth=ls, label = r'dolfinx')\n",
    "axs[1].set_xlabel(r\"$t\\,(s)$\",fontsize=labelsize)\n",
    "axs[1].set_xlim(0,T)\n",
    "axs[1].set_xticks(np.arange(0., .55, 0.1))\n",
    "axs[1].tick_params(axis='x', labelsize=tickssize)\n",
    "axs[1].grid(which='major',linestyle='-')\n",
    "axs[1].grid(which='minor',linestyle='--')\n",
    "axs[1].legend(fontsize=legend_size, framealpha=1, loc='lower right')\n",
    "axs[1].set_title('Step transient', fontsize=labelsize)"
   ]
  }
 ],
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