{ "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": "2026-10-23T22:00:58.159672Z", "iopub.status.busy": "2026-10-23T22:00:58.159453Z", "iopub.status.idle": "2026-10-23T22:00:59.176450Z", "shell.execute_reply": "2026-10-23T22:00:59.175326Z" } }, "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": "2026-10-23T22:00:59.178570Z", "iopub.status.busy": "2026-10-23T22:00:59.178278Z", "iopub.status.idle": "2026-10-23T22:01:00.950057Z", "shell.execute_reply": "2026-10-23T22:01:00.948934Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "max conservation error [J]: 2.60770320892334e-08\n", "stored energy [kWh]: 2.3031771680909383\n", "energy between 32 and 70 degC [kWh]: 2.635389843901675\n", "stored energy per unit area [MJ.m-2]: 81.45822913734806\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": "2026-10-23T22:01:00.951575Z", "iopub.status.busy": "2026-10-23T22:01:00.951437Z", "iopub.status.idle": "2026-10-23T22:01:01.070955Z", "shell.execute_reply": "2026-10-23T22:01:01.070074Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "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 1 K to 2 K (root mean square).\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: the heat the capsule\n", "takes from the averaged fluid is taken uniformly along the bed, including near the outlet,\n", "where the fluid is still cold. This is a known artefact of this formulation, the published reduced\n", "model; energy is still conserved exactly." ] }, { "cell_type": "code", "execution_count": 4, "id": "5b14d659", "metadata": { "execution": { "iopub.execute_input": "2026-10-23T22:01:01.072494Z", "iopub.status.busy": "2026-10-23T22:01:01.072353Z", "iopub.status.idle": "2026-10-23T22:01:01.335389Z", "shell.execute_reply": "2026-10-23T22:01:01.334370Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "solve time: resolved 1.532 s, averaged 75.950 ms\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "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 }