{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Turbulent Channel (Reynolds 2003)\n",
    "\n",
    "The simulation of a periodic turbulent channel is also used for validation of the equilibrium wall model implemented with the thin boundary layer (TBL) equation. A friction Reynolds number of 2003 is fixed for this case. The reference article used for comparison of results is [Han et al., 2020](https://iopscience.iop.org/article/10.1088/1873-7005/ac1782/meta). The pressure gradient is estabilished through a constant body force."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nassu.cfg.model import ConfigScheme\n",
    "\n",
    "filename = \"tests/validation/cases/04.1_turbulent_channel_flow_wm.nassu.yaml\"\n",
    "\n",
    "sim_cfgs = ConfigScheme.sim_cfgs_from_file_dct(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_cfg = next(\n",
    "    sim_cfg\n",
    "    for (name, _), sim_cfg in sim_cfgs.items()\n",
    "    if sim_cfg.name == \"periodicTurbulentChannel\"\n",
    ")\n",
    "sim_cfg_wm = next(\n",
    "    sim_cfg\n",
    "    for (name, _), sim_cfg in sim_cfgs.items()\n",
    "    if sim_cfg.name == \"periodicTurbulentChannelMultilevel\"\n",
    ")\n",
    "sim_cfg_wm_mb = next(\n",
    "    sim_cfg\n",
    "    for (name, _), sim_cfg in sim_cfgs.items()\n",
    "    if sim_cfg.name == \"periodicTurbulentChannelNoWM\"\n",
    ")\n",
    "sim_cfgs_use = {\"ref\": sim_cfg, \"wm\": sim_cfg_wm, \"mb\": sim_cfg_wm_mb}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions to use for turbulence channel processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pathlib\n",
    "from nassu.cfg.schemes.simul import SimulationConfigs\n",
    "from vtk.util.numpy_support import vtk_to_numpy\n",
    "from tests.validation.notebooks import common\n",
    "\n",
    "\n",
    "def get_experimental_profiles(reynolds_tau: float) -> dict[str, pd.DataFrame]:\n",
    "    files_tau: dict[float, dict[str, str]] = {\n",
    "        2003: {\n",
    "            \"ux\": \"Turbulent_channel/Re_tau_2003/u_avg.csv\",\n",
    "            \"ux_rms\": \"Turbulent_channel/Re_tau_2003/u_rms.csv\",\n",
    "            \"uy_rms\": \"Turbulent_channel/Re_tau_2003/v_rms.csv\",\n",
    "            \"uz_rms\": \"Turbulent_channel/Re_tau_2003/w_rms.csv\",\n",
    "        },\n",
    "    }\n",
    "\n",
    "    files_get = files_tau[reynolds_tau]\n",
    "    vals_exp: dict[str, pd.DataFrame] = {}\n",
    "    for name, comp_file in files_get.items():\n",
    "        filename = pathlib.Path(\"tests/validation/comparison\") / comp_file\n",
    "        df = pd.read_csv(filename, delimiter=\",\")\n",
    "        vals_exp[name] = df\n",
    "    return vals_exp\n",
    "\n",
    "\n",
    "def get_height_scale(sim_cfg: SimulationConfigs, reynolds_tau: float, u_ref: float) -> float:\n",
    "    kin_visc = sim_cfg.models.LBM.kinematic_viscosity\n",
    "    return u_ref / kin_visc\n",
    "\n",
    "\n",
    "reader_output = {}\n",
    "for name, sim_cfg in sim_cfgs_use.items():\n",
    "    stats_export = sim_cfg.output.stats[\"default\"]\n",
    "    last_step = stats_export.interval.get_all_process_steps(sim_cfg.n_steps)[-1]\n",
    "    reader = stats_export.read_vtm_export(last_step)\n",
    "    reader_output[sim_cfg.name] = reader.GetOutput()\n",
    "\n",
    "\n",
    "def get_macr_compressed(\n",
    "    sim_cfg: SimulationConfigs, macr_name: str, is_2nd_order: bool\n",
    ") -> np.ndarray:\n",
    "    global reader, reader_output\n",
    "\n",
    "    output_use = reader_output[sim_cfg.name]\n",
    "\n",
    "    macr_name_read = macr_name if not is_2nd_order else f\"{macr_name}_2nd\"\n",
    "    ds = sim_cfg.domain.domain_size\n",
    "\n",
    "    p0 = np.array((ds.x // 2, ds.y // 2, 4))\n",
    "    p1 = np.array((ds.x // 2, ds.y // 2, ds.z // 2))\n",
    "    n_points = ds.z - 8\n",
    "    pos = np.linspace(p0, p1, num=n_points, endpoint=True)\n",
    "    norm_pos = (pos[:, 2] - 4) / (ds.z - 8)\n",
    "\n",
    "    # Sum 0.5 because data is cell centered in vtm\n",
    "\n",
    "    line = common.create_line(p0, p1, n_points - 1)\n",
    "\n",
    "    probe_filter = common.probe_over_line(line, output_use)\n",
    "    probed_data = vtk_to_numpy(probe_filter.GetOutput().GetPointData().GetArray(macr_name_read))\n",
    "\n",
    "    return np.array([norm_pos, probed_data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y, ux_avg = get_macr_compressed(sim_cfg, \"ux\", is_2nd_order=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "The average velocity profile is shown for the case. It can be seen a good approximation of desired profile when using the wall model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "reynolds_tau = 2003\n",
    "u_ref = 0.00225\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "fig.set_size_inches(8, 5)\n",
    "\n",
    "for i, (sim_cfg, color) in enumerate(zip(sim_cfgs_use.values(), (\"r\", \"g\", \"k\"))):\n",
    "    height_scale = get_height_scale(sim_cfg, reynolds_tau, u_ref)\n",
    "    name = sim_cfg.name.removeprefix(\"periodic\")\n",
    "\n",
    "    analytical_values = get_experimental_profiles(reynolds_tau)\n",
    "\n",
    "    y, ux_avg = get_macr_compressed(sim_cfg, \"ux\", is_2nd_order=False)\n",
    "    if not sim_cfg.name.endswith(\"Multilevel\"):\n",
    "        y, ux_avg = y[::2], ux_avg[::2]\n",
    "\n",
    "    ux_avg /= u_ref\n",
    "    y *= height_scale * (sim_cfg.domain.domain_size.z - 8)\n",
    "\n",
    "    exp_y = analytical_values[\"ux\"][\"y+\"]\n",
    "    exp_ux_avg = analytical_values[\"ux\"][\"u/u*\"]\n",
    "\n",
    "    ax.plot(y, ux_avg, f\"v{color}\", label=f\"{name}\", fillstyle=\"none\")\n",
    "\n",
    "ax.plot(exp_y, exp_ux_avg, \"ok\", label=\"Experimental\", fillstyle=\"none\")\n",
    "\n",
    "ax.set_title(\"Turbulent Channel\")\n",
    "\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.set_xlim(5, 2000)\n",
    "ax.set_ylim(0, 25)\n",
    "ax.set_xscale(\"symlog\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results of the ${\\mathrm{u_{rms}}}$ velocity profiles shown below also indicate a good representation with the multilevel approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 3)\n",
    "fig.set_size_inches(20, 5)\n",
    "\n",
    "vel_name_map = {\"ux\": \"u\", \"uy\": \"v\", \"uz\": \"w\"}\n",
    "\n",
    "for i, sim_cfg in enumerate(sim_cfgs_use.values()):\n",
    "    name = sim_cfg.name.removeprefix(\"periodic\")\n",
    "    for macr_name, marker, color in [(\"ux\", \"o\", \"k\"), (\"uy\", \"^\", \"r\"), (\"uz\", \"s\", \"b\")]:\n",
    "        macr_compr_rms = get_macr_compressed(sim_cfg, macr_name, is_2nd_order=True)\n",
    "        macr_compr_avg = get_macr_compressed(sim_cfg, macr_name, is_2nd_order=False)\n",
    "\n",
    "        y = macr_compr_rms[0].copy()\n",
    "        vel_2nd = macr_compr_rms[1].copy()\n",
    "        vel_avg = macr_compr_avg[1].copy()\n",
    "        vel_rms = (vel_2nd - vel_avg**2) ** 0.5\n",
    "\n",
    "        vel_rms /= u_ref\n",
    "        y *= height_scale * (sim_cfg.domain.domain_size.z - 8)\n",
    "        # Remove wall value\n",
    "        vel_rms = vel_rms[::2]\n",
    "        y = y[::2]\n",
    "\n",
    "        name_vel = vel_name_map[macr_name]\n",
    "        df = analytical_values[f\"{macr_name}_rms\"]\n",
    "        exp_y = df[\"y+\"]\n",
    "        exp_rms = df[f\"{name_vel}'/u*\"]\n",
    "\n",
    "        ax[i].plot(y, vel_rms, f\"v{color}\", label=f\"{name} {name_vel.upper()}\")\n",
    "        ax[i].plot(\n",
    "            exp_y, exp_rms, f\"{marker}{color}\", label=f\"Exp. {name_vel.upper()}\", fillstyle=\"none\"\n",
    "        )\n",
    "        # ax[i].set_xscale(\"symlog\")\n",
    "\n",
    "    ax[i].set_title(f\"{name}\")\n",
    "\n",
    "    ax[i].legend()\n",
    "    ax[i].set_xlim(10, 2000)\n",
    "    ax[i].set_ylim(0, 3.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen good approach of results with the use of wall model and subsequent combination with multigrid approach. As the shell point becomes nearer the surface for a high refinement, the first point velocity becomes a little smaller than for a coarser refinement."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version: 1.6.40\n",
      "Commit hash: 79342cd137bdb267e91047977e942600a6962053\n"
     ]
    }
   ],
   "source": [
    "sim_info = sim_cfg.output.read_info()\n",
    "\n",
    "nassu_commit = sim_info[\"commit\"]\n",
    "nassu_version = sim_info[\"version\"]\n",
    "print(\"Version:\", nassu_version)\n",
    "print(\"Commit hash:\", nassu_commit)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       ".output_html .gd { color: #A00000 } /* Generic.Deleted */\n",
       ".output_html .ge { font-style: italic } /* Generic.Emph */\n",
       ".output_html .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */\n",
       ".output_html .gr { color: #E40000 } /* Generic.Error */\n",
       ".output_html .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       ".output_html .gi { color: #008400 } /* Generic.Inserted */\n",
       ".output_html .go { color: #717171 } /* Generic.Output */\n",
       ".output_html .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
       ".output_html .gs { font-weight: bold } /* Generic.Strong */\n",
       ".output_html .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       ".output_html .gt { color: #04D } /* Generic.Traceback */\n",
       ".output_html .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
       ".output_html .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
       ".output_html .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
       ".output_html .kp { color: #008000 } /* Keyword.Pseudo */\n",
       ".output_html .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
       ".output_html .kt { color: #B00040 } /* Keyword.Type */\n",
       ".output_html .m { color: #666 } /* Literal.Number */\n",
       ".output_html .s { color: #BA2121 } /* Literal.String */\n",
       ".output_html .na { color: #687822 } /* Name.Attribute */\n",
       ".output_html .nb { color: #008000 } /* Name.Builtin */\n",
       ".output_html .nc { color: #00F; font-weight: bold } /* Name.Class */\n",
       ".output_html .no { color: #800 } /* Name.Constant */\n",
       ".output_html .nd { color: #A2F } /* Name.Decorator */\n",
       ".output_html .ni { color: #717171; font-weight: bold } /* Name.Entity */\n",
       ".output_html .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */\n",
       ".output_html .nf { color: #00F } /* Name.Function */\n",
       ".output_html .nl { color: #767600 } /* Name.Label */\n",
       ".output_html .nn { color: #00F; font-weight: bold } /* Name.Namespace */\n",
       ".output_html .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
       ".output_html .nv { color: #19177C } /* Name.Variable */\n",
       ".output_html .ow { color: #A2F; font-weight: bold } /* Operator.Word */\n",
       ".output_html .w { color: #BBB } /* Text.Whitespace */\n",
       ".output_html .mb { color: #666 } /* Literal.Number.Bin */\n",
       ".output_html .mf { color: #666 } /* Literal.Number.Float */\n",
       ".output_html .mh { color: #666 } /* Literal.Number.Hex */\n",
       ".output_html .mi { color: #666 } /* Literal.Number.Integer */\n",
       ".output_html .mo { color: #666 } /* Literal.Number.Oct */\n",
       ".output_html .sa { color: #BA2121 } /* Literal.String.Affix */\n",
       ".output_html .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
       ".output_html .sc { color: #BA2121 } /* Literal.String.Char */\n",
       ".output_html .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
       ".output_html .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
       ".output_html .s2 { color: #BA2121 } /* Literal.String.Double */\n",
       ".output_html .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */\n",
       ".output_html .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
       ".output_html .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */\n",
       ".output_html .sx { color: #008000 } /* Literal.String.Other */\n",
       ".output_html .sr { color: #A45A77 } /* Literal.String.Regex */\n",
       ".output_html .s1 { color: #BA2121 } /* Literal.String.Single */\n",
       ".output_html .ss { color: #19177C } /* Literal.String.Symbol */\n",
       ".output_html .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
       ".output_html .fm { color: #00F } /* Name.Function.Magic */\n",
       ".output_html .vc { color: #19177C } /* Name.Variable.Class */\n",
       ".output_html .vg { color: #19177C } /* Name.Variable.Global */\n",
       ".output_html .vi { color: #19177C } /* Name.Variable.Instance */\n",
       ".output_html .vm { color: #19177C } /* Name.Variable.Magic */\n",
       ".output_html .il { color: #666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"nt\">simulations</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">  </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">periodicTurbulentChannel</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">save_path</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">./tests/validation/results/04.1_turbulent_channel_flow_wm/periodic</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">run_simul</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">n_steps</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">200000</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">report</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1000</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">domain</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">domain_size</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">x</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">168</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">y</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">168</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">z</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">64</span><span class=\"w\"> </span><span class=\"c1\"># spacing 56</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">block_size</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">8</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">bodies</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">plane_floor</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">run</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">True</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">cfg_use</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">plane_cfg</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">lnas_path</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">fixture/lnas/wind_tunnel/full_plane.lnas</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">transformation</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">scale</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">2</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">2</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">2</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">translation</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">4</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">plane_ceil</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">run</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">True</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">cfg_use</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">plane_cfg</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">lnas_path</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">fixture/lnas/wind_tunnel/full_plane.lnas</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">transformation</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">scale</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">2</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">2</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">2</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">fixed_point</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">80</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">rotation</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"kt\">!math</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">radians(180)</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">translation</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">60</span><span class=\"p p-Indicator\">]</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">data</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">export_IBM_nodes</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">ibm_exp</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">body_name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">plane_floor</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">5000</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">divergence</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">1</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">instantaneous</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">default</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">5000</span><span class=\"w\"> </span><span class=\"p p-Indicator\">},</span><span class=\"nt\"> macrs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">rho</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">u</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">omega_LES</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">f_IBM</span><span class=\"p p-Indicator\">]</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">statistics</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">100</span><span class=\"p p-Indicator\">,</span><span class=\"nt\"> start_step</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">100000</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">macrs_1st_order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">rho</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">u</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">macrs_2nd_order</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">u</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">exports</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">default</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> interval</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">{</span><span class=\"nt\"> frequency</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"nv\">50000</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span><span class=\"w\"> </span><span class=\"p p-Indicator\">}</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">models</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">precision</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">default</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">single</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">LBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">tau</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.500096682620786</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">F</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">x</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1.89820067659664E-07</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">y</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">z</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">vel_set</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">D3Q27</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">coll_oper</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">RRBGK</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">initialization</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">vtm_filename</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"s\">&quot;../nassuArtifacts/macrs/turbulent_channel_wm.vtm&quot;</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">engine</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">CUDA</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">LES</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">model</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">Smagorinsky</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">sgs_cte</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.17</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">BC</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">periodic_dims</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">true</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">true</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">true</span><span class=\"p p-Indicator\">]</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">dirac_delta</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">3_points</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">forces_accomodate_time</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">reset_forces</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">body_cfgs</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">default</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">n_iterations</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">forces_factor</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1.0</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">plane_cfg</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">n_iterations</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">forces_factor</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_model</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">EqTBL</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">dist_ref</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">2.0</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">dist_shell</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.25</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">start_step</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0</span>\n",
       "<span class=\"w\">              </span><span class=\"nt\">params</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">z0</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">0.00155</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">TDMA_max_error</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1e-04</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">TDMA_max_iters</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">10</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">TDMA_min_div</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">51</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">TDMA_max_div</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">51</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">visc_correction</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">False</span>\n",
       "\n",
       "<span class=\"w\">      </span><span class=\"nt\">multiblock</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">overlap_F2C</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">2</span>\n",
       "\n",
       "<span class=\"w\">  </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">periodicTurbulentChannelMultilevel</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">parent</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">periodicTurbulentChannel</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">run_simul</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">domain</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">refinement</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">static</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">default</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">volumes_refine</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">              </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">start</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">168</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">168</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">16</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">lvl</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">is_abs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "<span class=\"w\">              </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">start</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">0</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">48</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">end</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"p p-Indicator\">[</span><span class=\"nv\">168</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">168</span><span class=\"p p-Indicator\">,</span><span class=\"w\"> </span><span class=\"nv\">64</span><span class=\"p p-Indicator\">]</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">lvl</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">1</span>\n",
       "<span class=\"w\">                </span><span class=\"nt\">is_abs</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "\n",
       "<span class=\"w\">  </span><span class=\"p p-Indicator\">-</span><span class=\"w\"> </span><span class=\"nt\">name</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">periodicTurbulentChannelNoWM</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">parent</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">periodicTurbulentChannel</span>\n",
       "<span class=\"w\">    </span><span class=\"nt\">run_simul</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "\n",
       "<span class=\"w\">    </span><span class=\"nt\">models</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">      </span><span class=\"nt\">IBM</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"nt\">body_cfgs</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">          </span><span class=\"nt\">plane_cfg</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">            </span><span class=\"nt\">wall_model</span><span class=\"p\">:</span><span class=\"w\"> </span><span class=\"l l-Scalar l-Scalar-Plain\">null</span>\n",
       "</pre></div>\n"
      ],
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       "\\PY{+w}{              }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{start}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{                }\\PY{n+nt}{end}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{168}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{168}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{16}\\PY{p+pIndicator}{]}\n",
       "\\PY{+w}{                }\\PY{n+nt}{lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\\PY{+w}{                }\\PY{n+nt}{is\\PYZus{}abs}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
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       "\\PY{+w}{                }\\PY{n+nt}{lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n",
       "\\PY{+w}{                }\\PY{n+nt}{is\\PYZus{}abs}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\n",
       "\\PY{+w}{  }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicTurbulentChannelNoWM}\n",
       "\\PY{+w}{    }\\PY{n+nt}{parent}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicTurbulentChannel}\n",
       "\\PY{+w}{    }\\PY{n+nt}{run\\PYZus{}simul}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n",
       "\n",
       "\\PY{+w}{    }\\PY{n+nt}{models}\\PY{p}{:}\n",
       "\\PY{+w}{      }\\PY{n+nt}{IBM}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{n+nt}{body\\PYZus{}cfgs}\\PY{p}{:}\n",
       "\\PY{+w}{          }\\PY{n+nt}{plane\\PYZus{}cfg}\\PY{p}{:}\n",
       "\\PY{+w}{            }\\PY{n+nt}{wall\\PYZus{}model}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{null}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "simulations:\n",
       "  - name: periodicTurbulentChannel\n",
       "    save_path: ./tests/validation/results/04.1_turbulent_channel_flow_wm/periodic\n",
       "    run_simul: true\n",
       "\n",
       "    n_steps: 200000\n",
       "    report:\n",
       "      frequency: 1000\n",
       "\n",
       "    domain:\n",
       "      domain_size:\n",
       "        x: 168\n",
       "        y: 168\n",
       "        z: 64 # spacing 56\n",
       "      block_size: 8\n",
       "      bodies:\n",
       "        plane_floor:\n",
       "          IBM:\n",
       "            run: True\n",
       "            cfg_use: plane_cfg\n",
       "            order: 0\n",
       "          lnas_path: fixture/lnas/wind_tunnel/full_plane.lnas\n",
       "          transformation:\n",
       "            scale: [2, 2, 2]\n",
       "            translation: [0, 0, 4]\n",
       "        plane_ceil:\n",
       "          IBM:\n",
       "            run: True\n",
       "            cfg_use: plane_cfg\n",
       "            order: 0\n",
       "          lnas_path: fixture/lnas/wind_tunnel/full_plane.lnas\n",
       "          transformation:\n",
       "            scale: [2, 2, 2]\n",
       "            fixed_point: [0, 80, 0]\n",
       "            rotation: !math [radians(180), 0, 0]\n",
       "            translation: [0, 0, 60]\n",
       "\n",
       "    data:\n",
       "      export_IBM_nodes:\n",
       "        ibm_exp:\n",
       "          body_name: plane_floor\n",
       "          frequency: 5000\n",
       "      divergence: { frequency: 1 }\n",
       "      instantaneous:\n",
       "        default: { interval: { frequency: 5000 }, macrs: [rho, u, omega_LES, f_IBM] }\n",
       "      statistics:\n",
       "        interval: { frequency: 100, start_step: 100000 }\n",
       "        macrs_1st_order: [rho, u]\n",
       "        macrs_2nd_order: [u]\n",
       "        exports:\n",
       "          default: { interval: { frequency: 50000 } }\n",
       "\n",
       "    models:\n",
       "      precision:\n",
       "        default: single\n",
       "\n",
       "      LBM:\n",
       "        tau: 0.500096682620786\n",
       "        F:\n",
       "          x: 1.89820067659664E-07\n",
       "          y: 0\n",
       "          z: 0\n",
       "        vel_set: D3Q27\n",
       "        coll_oper: RRBGK\n",
       "      initialization:\n",
       "        vtm_filename: \"../nassuArtifacts/macrs/turbulent_channel_wm.vtm\"\n",
       "\n",
       "      engine:\n",
       "        name: CUDA\n",
       "\n",
       "      LES:\n",
       "        model: Smagorinsky\n",
       "        sgs_cte: 0.17\n",
       "\n",
       "      BC:\n",
       "        periodic_dims: [true, true, true]\n",
       "\n",
       "      IBM:\n",
       "        dirac_delta: 3_points\n",
       "        forces_accomodate_time: 0\n",
       "        reset_forces: true\n",
       "        body_cfgs:\n",
       "          default:\n",
       "            n_iterations: 1\n",
       "            forces_factor: 1.0\n",
       "          plane_cfg:\n",
       "            n_iterations: 1\n",
       "            forces_factor: 0.25\n",
       "            wall_model:\n",
       "              name: EqTBL\n",
       "              dist_ref: 2.0\n",
       "              dist_shell: 0.25\n",
       "              start_step: 0\n",
       "              params:\n",
       "                z0: 0.00155\n",
       "                TDMA_max_error: 1e-04\n",
       "                TDMA_max_iters: 10\n",
       "                TDMA_min_div: 51\n",
       "                TDMA_max_div: 51\n",
       "            visc_correction: False\n",
       "\n",
       "      multiblock:\n",
       "        overlap_F2C: 2\n",
       "\n",
       "  - name: periodicTurbulentChannelMultilevel\n",
       "    parent: periodicTurbulentChannel\n",
       "    run_simul: true\n",
       "\n",
       "    domain:\n",
       "      refinement:\n",
       "        static:\n",
       "          default:\n",
       "            volumes_refine:\n",
       "              - start: [0, 0, 0]\n",
       "                end: [168, 168, 16]\n",
       "                lvl: 1\n",
       "                is_abs: true\n",
       "              - start: [0, 0, 48]\n",
       "                end: [168, 168, 64]\n",
       "                lvl: 1\n",
       "                is_abs: true\n",
       "\n",
       "  - name: periodicTurbulentChannelNoWM\n",
       "    parent: periodicTurbulentChannel\n",
       "    run_simul: true\n",
       "\n",
       "    models:\n",
       "      IBM:\n",
       "        body_cfgs:\n",
       "          plane_cfg:\n",
       "            wall_model: null"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Code\n",
    "\n",
    "Code(filename=filename)"
   ]
  }
 ],
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