{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd24739a",
   "metadata": {},
   "source": [
    "# Graphic card thermal analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42601d8f",
   "metadata": {},
   "source": [
    "This example shows how to use pyAEDT to create a graphic card setup in\n",
    "Icepak and postprocess the results.\n",
    "The example file is an Icepak project with a model that is already created and\n",
    "has materials assigned.\n",
    "\n",
    "Keywords: **Icepak**, **boundary conditions**, **postprocessing**, **monitors**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7416f90",
   "metadata": {},
   "source": [
    "## Perform imports and define constants\n",
    "\n",
    "Perform required imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65d19327",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tempfile\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7c12efc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ansys.aedt.core\n",
    "import pandas as pd\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db935a55",
   "metadata": {},
   "source": [
    "Define constants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d27b2f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "AEDT_VERSION = \"2024.2\"\n",
    "NUM_CORES = 4\n",
    "NG_MODE = False  # Do not show the graphical user interface."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96256323",
   "metadata": {},
   "source": [
    "## Create temporary directory and download project\n",
    "\n",
    "Create a temporary directory where downloaded data or\n",
    "dumped data can be stored.\n",
    "If you'd like to retrieve the project data for subsequent use,\n",
    "the temporary folder name is given by ``temp_folder.name``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd83d18e",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_folder = tempfile.TemporaryDirectory(suffix=\".ansys\")\n",
    "project_temp_name = ansys.aedt.core.downloads.download_icepak(\n",
    "    destination=temp_folder.name\n",
    ")\n",
    "# ## Open project\n",
    "#\n",
    "# Open the project without the GUI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30b49bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ipk = ansys.aedt.core.Icepak(\n",
    "    project=project_temp_name,\n",
    "    version=AEDT_VERSION,\n",
    "    new_desktop=True,\n",
    "    non_graphical=NG_MODE,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f139149",
   "metadata": {},
   "source": [
    "## Plot model and rotate\n",
    "\n",
    "Plot the model using the pyAEDT-PyVista integration and save the result to a file.\n",
    "Rotate the model and plot the rotated model again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a7f56b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot1 = ipk.plot(\n",
    "    show=False,\n",
    "    output_file=os.path.join(temp_folder.name, \"Graphics_card_1.jpg\"),\n",
    "    plot_air_objects=False,\n",
    ")\n",
    "\n",
    "ipk.modeler.rotate(ipk.modeler.object_names, \"X\")\n",
    "\n",
    "plot2 = ipk.plot(\n",
    "    show=False,\n",
    "    output_file=os.path.join(temp_folder.name, \"Graphics_card_2.jpg\"),\n",
    "    plot_air_objects=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e59463cc",
   "metadata": {},
   "source": [
    "## Define boundary conditions\n",
    "\n",
    "Create source blocks on the CPU and memories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "413a6548",
   "metadata": {},
   "outputs": [],
   "source": [
    "ipk.create_source_block(object_name=\"CPU\", input_power=\"25W\")\n",
    "ipk.create_source_block(object_name=[\"MEMORY1\", \"MEMORY1_1\"], input_power=\"5W\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2405fc2e",
   "metadata": {},
   "source": [
    "The air region object handler is used to specify the inlet (fixed velocity condition) and outlet\n",
    "(fixed pressure condition) at x_max and x_min."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f925dcba",
   "metadata": {},
   "outputs": [],
   "source": [
    "region = ipk.modeler[\"Region\"]\n",
    "ipk.assign_pressure_free_opening(\n",
    "    assignment=region.top_face_x.id, boundary_name=\"Outlet\"\n",
    ")\n",
    "ipk.assign_velocity_free_opening(\n",
    "    assignment=region.bottom_face_x.id,\n",
    "    boundary_name=\"Inlet\",\n",
    "    velocity=[\"1m_per_sec\", \"0m_per_sec\", \"0m_per_sec\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "573bce4a",
   "metadata": {},
   "source": [
    "## Assign mesh settings\n",
    "\n",
    "### Assign mesh region\n",
    "Assign a mesh region around the heat sink and CPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfe084f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_region = ipk.mesh.assign_mesh_region(assignment=[\"HEAT_SINK\", \"CPU\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dd739bf",
   "metadata": {},
   "source": [
    "Print the available settings for the mesh region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94ced8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_region.settings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e1cb1b",
   "metadata": {},
   "source": [
    "Set the mesh region settings to manual and see newly available settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9097340b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_region.manual_settings = True\n",
    "mesh_region.settings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50fd602c",
   "metadata": {},
   "source": [
    "Modify settings and update."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da2400f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_region.settings[\"MaxElementSizeX\"] = \"2mm\"\n",
    "mesh_region.settings[\"MaxElementSizeY\"] = \"2mm\"\n",
    "mesh_region.settings[\"MaxElementSizeZ\"] = \"2mm\"\n",
    "mesh_region.settings[\"EnableMLM\"] = True\n",
    "mesh_region.settings[\"MaxLevels\"] = \"2\"\n",
    "mesh_region.settings[\"MinElementsInGap\"] = 4\n",
    "mesh_region.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb0ea666",
   "metadata": {},
   "source": [
    "Modify the slack of the subregion around the objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c840cfe",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "subregion = mesh_region.assignment\n",
    "subregion.positive_x_padding = \"20mm\"\n",
    "subregion.positive_y_padding = \"5mm\"\n",
    "subregion.positive_z_padding = \"5mm\"\n",
    "subregion.negative_x_padding = \"5mm\"\n",
    "subregion.negative_y_padding = \"5mm\"\n",
    "subregion.negative_z_padding = \"10mm\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f44d688b",
   "metadata": {},
   "source": [
    "## Assign monitors\n",
    "\n",
    "Assign a temperature face monitor to the CPU face in contact with the heatsink."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "690cfb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "cpu = ipk.modeler[\"CPU\"]\n",
    "m1 = ipk.monitor.assign_face_monitor(\n",
    "    face_id=cpu.top_face_z.id,\n",
    "    monitor_quantity=\"Temperature\",\n",
    "    monitor_name=\"TemperatureMonitor1\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "571530f2",
   "metadata": {},
   "source": [
    "Assign multiple speed point monitors downstream of the assembly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f864152",
   "metadata": {},
   "outputs": [],
   "source": [
    "speed_monitors = []\n",
    "for x_pos in range(0, 10, 2):\n",
    "    m = ipk.monitor.assign_point_monitor(\n",
    "        point_position=[f\"{x_pos}mm\", \"40mm\", \"15mm\"], monitor_quantity=\"Speed\"\n",
    "    )\n",
    "    speed_monitors.append(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2fb65e5",
   "metadata": {},
   "source": [
    "## Solve project\n",
    "\n",
    "Create a setup, modify solver settings, and run the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "132aa271",
   "metadata": {},
   "outputs": [],
   "source": [
    "setup1 = ipk.create_setup()\n",
    "setup1.props[\"Flow Regime\"] = \"Turbulent\"\n",
    "setup1.props[\"Convergence Criteria - Max Iterations\"] = 5\n",
    "setup1.props[\"Linear Solver Type - Pressure\"] = \"flex\"\n",
    "setup1.props[\"Linear Solver Type - Temperature\"] = \"flex\"\n",
    "ipk.save_project()\n",
    "ipk.analyze(setup=setup1.name, cores=NUM_CORES, tasks=NUM_CORES)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da44c78",
   "metadata": {},
   "source": [
    "## Postprocess\n",
    "\n",
    "### Perform quantitative postprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb4e79e",
   "metadata": {},
   "source": [
    "Get the point monitor data. A dictionary is returned with ``'Min'``, ``'Max'``, and ``'Mean'`` keys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dd39029",
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature_data = ipk.post.evaluate_monitor_quantity(\n",
    "    monitor=m1, quantity=\"Temperature\"\n",
    ")\n",
    "temperature_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15bfff6e",
   "metadata": {},
   "source": [
    "It is also possible to get the data as a Pandas dataframe for advanced postprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a22a898",
   "metadata": {},
   "outputs": [],
   "source": [
    "speed_fs = ipk.post.create_field_summary()\n",
    "for m_name in speed_monitors:\n",
    "    speed_fs.add_calculation(\n",
    "        entity=\"Monitor\", geometry=\"Volume\", geometry_name=m_name, quantity=\"Speed\"\n",
    "    )\n",
    "speed_data = speed_fs.get_field_summary_data(pandas_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "773a5c7b",
   "metadata": {},
   "source": [
    "All the data is now in a dataframe, making it easy to visualize and manipulate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3f9a2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "speed_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0ef41f",
   "metadata": {},
   "source": [
    "The ``speed_data`` dataframe contains data from monitors, so it can be expanded with information\n",
    "of their position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b073f384",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(3):\n",
    "    direction = [\"X\", \"Y\", \"Z\"][i]\n",
    "    speed_data[\"Position\" + direction] = [\n",
    "        ipk.monitor.all_monitors[entity].location[i] for entity in speed_data[\"Entity\"]\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe50a90",
   "metadata": {},
   "source": [
    "Plot the velocity profile at different X positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0641c0bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "speed_data.plot(\n",
    "    x=\"PositionX\",\n",
    "    y=\"Mean\",\n",
    "    kind=\"line\",\n",
    "    marker=\"o\",\n",
    "    ylabel=speed_data.at[0, \"Quantity\"],\n",
    "    xlabel=f\"x [{ipk.modeler.model_units}]\",\n",
    "    grid=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b305cb",
   "metadata": {},
   "source": [
    "Extract temperature data at those same locations (so the ``speed_monitors`` list is used)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4865aa37",
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature_fs = ipk.post.create_field_summary()\n",
    "for m_name in speed_monitors:\n",
    "    temperature_fs.add_calculation(\n",
    "        entity=\"Monitor\",\n",
    "        geometry=\"Volume\",\n",
    "        geometry_name=m_name,\n",
    "        quantity=\"Temperature\",\n",
    "    )\n",
    "temperature_fs = temperature_fs.get_field_summary_data(pandas_output=True)\n",
    "temperature_fs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e7cbac",
   "metadata": {},
   "source": [
    "The two dataframes can be merged using the `pd.merge()` function. With the merge, suffixes are\n",
    "added to the column names to differentiate between the columns from each original dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbdf5af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = pd.merge(\n",
    "    temperature_fs, speed_data, on=\"Entity\", suffixes=(\"_temperature\", \"_speed\")\n",
    ")\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "362d2eed",
   "metadata": {},
   "source": [
    "The column names are renamed based on the ``Quantity`` column of the original dataframes.\n",
    "Finally, only the ``'Entity'``, ``'Mean_temperature'``, and ``'Mean_speed'`` columns are selected and\n",
    "assigned to the merged dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a43302ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature_quantity = temperature_fs[\"Quantity\"].iloc[0]\n",
    "velocity_quantity = speed_data[\"Quantity\"].iloc[0]\n",
    "merged_df.rename(\n",
    "    columns={\"Mean_temperature\": temperature_quantity, \"Mean_speed\": velocity_quantity},\n",
    "    inplace=True,\n",
    ")\n",
    "merged_df = merged_df[\n",
    "    [\n",
    "        \"Entity\",\n",
    "        temperature_quantity,\n",
    "        velocity_quantity,\n",
    "        \"PositionX\",\n",
    "        \"PositionY\",\n",
    "        \"PositionZ\",\n",
    "    ]\n",
    "]\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73960c38",
   "metadata": {},
   "source": [
    "Compute the correlation coefficient between velocity and temperature from the merged dataframe\n",
    "and plot a scatter plot to visualize their relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1b7f4ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation = merged_df[velocity_quantity].corr(merged_df[temperature_quantity])\n",
    "ax = merged_df.plot.scatter(x=velocity_quantity, y=temperature_quantity)\n",
    "ax.set_xlabel(velocity_quantity)\n",
    "ax.set_ylabel(temperature_quantity)\n",
    "ax.set_title(f\"Correlation between Temperature and Velocity: {correlation:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "441f498a",
   "metadata": {},
   "source": [
    "The further away from the assembly, the faster and colder the air due to mixing.\n",
    "Despite being extremely simple, this example demonstrates the potential of importing field\n",
    "summary data into Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "923efdbc",
   "metadata": {},
   "source": [
    "### Perform qualitative Postprocessing\n",
    "Create a temperature plot on main components and export it to a PNG file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05a07e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "surflist = [i.id for i in ipk.modeler[\"CPU\"].faces]\n",
    "surflist += [i.id for i in ipk.modeler[\"MEMORY1\"].faces]\n",
    "surflist += [i.id for i in ipk.modeler[\"MEMORY1_1\"].faces]\n",
    "plot3 = ipk.post.create_fieldplot_surface(\n",
    "    assignment=surflist, quantity=\"SurfTemperature\"\n",
    ")\n",
    "path = plot3.export_image(\n",
    "    full_path=os.path.join(temp_folder.name, \"temperature.png\"),\n",
    "    orientation=\"top\",\n",
    "    show_region=False,\n",
    ")\n",
    "Image(filename=path)  # Display the image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4368c2d8",
   "metadata": {},
   "source": [
    "Use PyVista to display the temperature map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cb60f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot4 = ipk.post.plot_field(\n",
    "    quantity=\"Temperature\",\n",
    "    assignment=[\n",
    "        \"SERIAL_PORT\",\n",
    "        \"MEMORY1\",\n",
    "        \"MEMORY1_1\",\n",
    "        \"CAPACITOR\",\n",
    "        \"CAPACITOR_1\",\n",
    "        \"KB\",\n",
    "        \"HEAT_SINK\",\n",
    "        \"CPU\",\n",
    "        \"ALPHA_MAIN_PCB\",\n",
    "    ],\n",
    "    plot_cad_objs=False,\n",
    "    show=False,\n",
    "    export_path=temp_folder.name,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f810b49",
   "metadata": {},
   "source": [
    "## Release AEDT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e15f0be",
   "metadata": {},
   "outputs": [],
   "source": [
    "ipk.save_project()\n",
    "ipk.release_desktop()\n",
    "# Wait 3 seconds to allow AEDT to shut down before cleaning the temporary directory.\n",
    "time.sleep(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f039e1d4",
   "metadata": {},
   "source": [
    "## Clean up\n",
    "\n",
    "All project files are saved in the folder ``temp_folder.name``.\n",
    "If you've run this example as a Jupyter notebook, you\n",
    "can retrieve those project files. The following cell\n",
    "removes all temporary files, including the project folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc3f73a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_folder.cleanup()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}