{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f38a1bfe",
   "metadata": {},
   "source": [
    "# Packed-bed model\n",
    "\n",
    "`PackedBedModel` describes a bed of spherical PCM capsules with the heat transfer fluid\n",
    "flowing through the voids. At each point along the bed, heat conducts across a capsule\n",
    "(resolved radially) and into the fluid at its surface. It is built from the same submodels\n",
    "as the finned-tube models, so it has the same outputs, conserves energy exactly and runs the same [experiments](experiments.ipynb).\n",
    "\n",
    "The default parameters are the experiments of Nallusamy et al. (2007): a 47 l tank of\n",
    "paraffin capsules in water, charged with water at 70 °C from 32 °C.\n",
    "\n",
    "For the geometry, the equations and the options of the packed-bed model, with sketches, see the [Packed bed](https://pytes.readthedocs.io/en/latest/models/packed_bed.html) page of the documentation.\n",
    "\n",
    "[Tutorial 3](getting_started/tutorial_3_packed_bed_models.ipynb) introduces this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e24ffc98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-01T11:17:00.443612Z",
     "iopub.status.busy": "2026-11-01T11:17:00.443211Z",
     "iopub.status.idle": "2026-11-01T11:17:01.550657Z",
     "shell.execute_reply": "2026-11-01T11:17:01.549649Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fluid: AdvectedFluid (Heat transfer fluid)\n",
      "    requires:\n",
      "        Heat transfer fluid heat gain per unit length [W.m-1]\n",
      "        Initial uniform temperature [K]\n",
      "    provides:\n",
      "        Heat transfer fluid specific enthalpy [J.kg-1]\n",
      "        Inlet specific enthalpy [J.kg-1]\n",
      "        Outlet specific enthalpy [J.kg-1]\n",
      "        Mass flow rate [kg.s-1]\n",
      "        Heat transfer fluid thermal energy [J]\n",
      "        Heat transfer fluid temperature [K]\n",
      "        Inlet temperature [K]\n",
      "        Outlet temperature [K]\n",
      "        Inlet-outlet temperature difference [K]\n",
      "        Heat transfer fluid velocity [m.s-1]\n",
      "        Volume flow rate [m3.s-1]\n",
      "        Pumped volume [m3]\n",
      "    parameters:\n",
      "        Bed cross-sectional area [m2]\n",
      "        Bed length [m]\n",
      "        Bed porosity\n",
      "        Heat transfer fluid density [kg.m-3]\n",
      "        Heat transfer fluid specific heat capacity [J.kg-1.K-1]\n",
      "        Inlet temperature [K]\n",
      "        Mass flow rate [kg.s-1]\n",
      "        PCM melting temperature [K]\n",
      "        Reference temperature [K]\n",
      "    assumptions:\n",
      "        The fluid is incompressible, with constant density.\n",
      "        Axial conduction and dispersion in the fluid are neglected.\n",
      "        The flow is the same in every flow path.\n",
      "storage: EnthalpyStorage (Phase-change material storage)\n",
      "    requires:\n",
      "        Phase-change material boundary heat flux [W.m-2]\n",
      "        Initial uniform temperature [K]\n",
      "    provides:\n",
      "        Phase-change material enthalpy [J.m-3]\n",
      "        Phase-change material enthalpy rate of change [W.m-3]\n",
      "        Phase-change material conduction heating rate [W.m-3]\n",
      "        Phase-change material thermal energy [J]\n",
      "        Liquid fraction\n",
      "        Phase\n",
      "        Average liquid fraction\n",
      "        Phase-change material temperature [K]\n",
      "        Phase-change material average temperature [K]\n",
      "        Phase-change material cross-section average temperature [K]\n",
      "        Phase-change material surface temperature [K]\n",
      "        Phase-change material thermal conductivity [W.m-1.K-1]\n",
      "    parameters:\n",
      "        Bed cross-sectional area [m2]\n",
      "        Bed porosity\n",
      "        Capsule radius [m]\n",
      "        PCM latent heat [J.kg-1]\n",
      "        PCM liquid phase conductivity [W.m-1.K-1]\n",
      "        PCM liquid phase density [kg.m-3]\n",
      "        PCM liquid phase specific heat capacity [J.kg-1.K-1]\n",
      "        PCM melting range [K]\n",
      "        PCM melting temperature [K]\n",
      "        PCM smoothing width [K]\n",
      "        PCM solid phase conductivity [W.m-1.K-1]\n",
      "        PCM solid phase density [kg.m-3]\n",
      "        PCM solid phase specific heat capacity [J.kg-1.K-1]\n",
      "        Reference temperature [K]\n",
      "    assumptions:\n",
      "        Heat flows along the primary spatial variable only.\n",
      "        Heat flows by conduction only (no convection in the liquid).\n",
      "        The volume change on melting is neglected.\n",
      "coupling: WallCoupling (Wall coupling)\n",
      "    requires:\n",
      "        Phase-change material conduction heating rate [W.m-3]\n",
      "        Heat transfer fluid specific enthalpy [J.kg-1]\n",
      "        Heat transfer fluid temperature [K]\n",
      "        Phase-change material surface temperature [K]\n",
      "        Mass flow rate [kg.s-1]\n",
      "    provides:\n",
      "        Heat transfer fluid heat gain per unit length [W.m-1]\n",
      "        Phase-change material boundary heat flux [W.m-2]\n",
      "        Heat transfer coefficient [W.m-2.K-1]\n",
      "        Surface Reynolds number\n",
      "        Surface Prandtl number\n",
      "    parameters:\n",
      "        Bed cross-sectional area [m2]\n",
      "        Bed porosity\n",
      "        Capsule radius [m]\n",
      "        Heat transfer coefficient [W.m-2.K-1]\n",
      "        Heat transfer fluid conductivity [W.m-1.K-1]\n",
      "        Heat transfer fluid specific heat capacity [J.kg-1.K-1]\n",
      "        Heat transfer fluid viscosity [Pa.s]\n",
      "    assumptions:\n",
      "        (none)\n",
      "device energy: DeviceEnergy (Device energy)\n",
      "    requires:\n",
      "        Mass flow rate [kg.s-1]\n",
      "        Outlet specific enthalpy [J.kg-1]\n",
      "        Inlet specific enthalpy [J.kg-1]\n",
      "        Phase-change material thermal energy [J]\n",
      "        Heat transfer fluid thermal energy [J]\n",
      "        Phase-change material average temperature [K]\n",
      "        Phase-change material cross-section average temperature [K]\n",
      "    provides:\n",
      "        Power [W]\n",
      "        Discharged energy [J]\n",
      "        Charged energy [J]\n",
      "        Discharged throughput [J]\n",
      "        Charged throughput [J]\n",
      "        Storage thermal energy [J]\n",
      "        Total thermal energy [J]\n",
      "        Heat loss power [W]\n",
      "        Storage average temperature [K]\n",
      "        Storage cross-section average temperature [K]\n",
      "        Initial uniform temperature [K]\n",
      "    parameters:\n",
      "        Initial temperature [K]\n",
      "    assumptions:\n",
      "        There are no heat losses to the surroundings.\n",
      "per-area totals: PerAreaTotals (Per-area totals)\n",
      "    requires:\n",
      "        Power [W]\n",
      "        Discharged energy [J]\n",
      "        Storage thermal energy [J]\n",
      "        Total thermal energy [J]\n",
      "    provides:\n",
      "        Power per unit area [W.m-2]\n",
      "        Discharged energy per unit area [J.m-2]\n",
      "        Storage thermal energy per unit area [J.m-2]\n",
      "        Total thermal energy per unit area [J.m-2]\n",
      "    parameters:\n",
      "        Bed cross-sectional area [m2]\n",
      "    assumptions:\n",
      "        (none)\n",
      "geometry parameters:\n",
      "    Bed length [m]\n",
      "    Capsule radius [m]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pybamm\n",
    "\n",
    "import pytes\n",
    "\n",
    "model = pytes.PackedBedModel()\n",
    "model.print_submodel_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e6d3c92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-01T11:17:01.552344Z",
     "iopub.status.busy": "2026-11-01T11:17:01.552009Z",
     "iopub.status.idle": "2026-11-01T11:17:03.434983Z",
     "shell.execute_reply": "2026-11-01T11:17:03.433976Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max conservation error [J]: 2.60770320892334e-08\n",
      "stored energy [kWh]: 2.318240801224132\n",
      "energy between 32 and 70 degC [kWh]: 2.635389843901675\n",
      "stored energy per unit area [MJ.m-2]: 81.99099617603031\n"
     ]
    }
   ],
   "source": [
    "t_end = 160 * 60\n",
    "solution = pybamm.Simulation(model).solve(\n",
    "    [0, t_end], t_interp=np.linspace(0, t_end, 321)\n",
    ")\n",
    "E = solution[\"Total thermal energy [J]\"].entries\n",
    "discharged = solution[\"Discharged energy [J]\"].entries\n",
    "print(\"max conservation error [J]:\", np.max(np.abs(E - E[0] + discharged)))\n",
    "print(\"stored energy [kWh]:\", E[-1] / 3.6e6)\n",
    "print(\n",
    "    \"energy between 32 and 70 degC [kWh]:\",\n",
    "    pytes.energy_between(model, 273.15 + 32, 273.15 + 70) / 3.6e6,\n",
    ")\n",
    "print(\n",
    "    \"stored energy per unit area [MJ.m-2]:\",\n",
    "    solution[\"Total thermal energy per unit area [J.m-2]\"].entries[-1] / 1e6,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e3714f",
   "metadata": {},
   "source": [
    "The measured fluid temperatures at a quarter, half, three quarters and the full height\n",
    "of the bed (points) against the model (lines)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eac0905a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-01T11:17:03.436416Z",
     "iopub.status.busy": "2026-11-01T11:17:03.436287Z",
     "iopub.status.idle": "2026-11-01T11:17:03.556200Z",
     "shell.execute_reply": "2026-11-01T11:17:03.555220Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = model.default_parameter_values[\"Bed length [m]\"]\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "for position, fraction in [(\"0.25L\", 0.25), (\"0.5L\", 0.5), (\"0.75L\", 0.75), (\"1L\", 1)]:\n",
    "    t_data, T_data = np.loadtxt(\n",
    "        f\"data/Nallusamy2007_HTF_{position}.csv\", delimiter=\",\", skiprows=1, unpack=True\n",
    "    )\n",
    "    t = solution[\"Time [s]\"].entries\n",
    "    T = solution[\"Heat transfer fluid temperature [degC]\"](t=t, z=fraction * Z)\n",
    "    (line,) = ax.plot(t / 60, T, label=f\"{fraction:g} L\")\n",
    "    ax.plot(t_data, T_data, \"o\", color=line.get_color(), markersize=3)\n",
    "ax.set_xlabel(\"Time [min]\")\n",
    "ax.set_ylabel(\"Fluid temperature [°C]\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aad928c",
   "metadata": {},
   "source": [
    "The fluid temperatures agree with the measurements to within 2 K (root mean square: 0.98,\n",
    "0.68, 1.41 and 1.85 K at a quarter, half, three quarters and the full height).\n",
    "Inside the capsules the agreement is looser, about 5 K at 0.8 of the capsule radius, which\n",
    "depends on where the thermocouples sat and on the capsule shell, which the model neglects."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a320bdc9",
   "metadata": {},
   "source": [
    "With `options={\"storage along flow path\": \"averaged\"}`, one capsule stands for the average along the bed and\n",
    "exchanges heat with the fluid averaged along it: much faster, but it cannot capture the\n",
    "thermal front moving through the bed. Every model has this option.\n",
    "\n",
    "Note the dip of the averaged outlet below the initial 32 °C early on, and the smaller one\n",
    "(about 0.6 K) just after it peaks. Both come from the heat the capsule takes from the\n",
    "averaged fluid, which is taken uniformly along the bed. Early on, that includes the outlet,\n",
    "where the fluid is still cold. Later, as hot fluid fills the bed, the averaged fluid warms,\n",
    "and the capsule takes more heat everywhere, cooling the fluid it had already warmed. Neither\n",
    "dip changes with the mesh: they are known artefacts of this formulation, the published\n",
    "reduced model; energy is still conserved exactly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b14d659",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-01T11:17:03.557961Z",
     "iopub.status.busy": "2026-11-01T11:17:03.557804Z",
     "iopub.status.idle": "2026-11-01T11:17:03.834598Z",
     "shell.execute_reply": "2026-11-01T11:17:03.833687Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solve time: resolved 1.653 s, averaged 77.659 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "averaged = pytes.PackedBedModel(options={\"storage along flow path\": \"averaged\"})\n",
    "averaged_solution = pybamm.Simulation(averaged).solve(\n",
    "    [0, t_end], t_interp=np.linspace(0, t_end, 321)\n",
    ")\n",
    "print(\n",
    "    f\"solve time: resolved {solution.solve_time}, averaged {averaged_solution.solve_time}\"\n",
    ")\n",
    "\n",
    "t_data, T_data = np.loadtxt(\n",
    "    \"data/Nallusamy2007_HTF_1L.csv\", delimiter=\",\", skiprows=1, unpack=True\n",
    ")\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "for sol, label in [(solution, \"resolved\"), (averaged_solution, \"averaged\")]:\n",
    "    ax.plot(\n",
    "        sol[\"Time [min]\"].entries, sol[\"Outlet temperature [degC]\"].entries, label=label\n",
    "    )\n",
    "ax.plot(t_data, T_data, \"ko\", markersize=3, label=\"measured\")\n",
    "ax.set_xlabel(\"Time [min]\")\n",
    "ax.set_ylabel(\"Outlet temperature [°C]\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytes (3.14.4)",
   "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": "3.14.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}