{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A simple heat problem for Python-Post-Processing\n", "\n", "This tutorial starts with a short description of the problem and how to simulate it with cfs. All needed files are provided. Afterwords some simple python postprocessing is done." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Short description of problem\n", "\n", "The geometry of the domain is a cylinder with the radius $r$ and the height $h$, due to symmetry reasons only on quarter is used for computation. The top volume is called `V_all` with the mantle surfaces `S_mantle` and the symmetry surfaces `S_x` and `S_y`. `S_top` is located on top of the cylinder and `S_bottom` on the bottom.\n", "\n", "|Sketch of problem|\n", "|:-:|\n", "|![](mesh.png)|\n", "\n", "`S_bottom` does have a prescribed temperature $T_{bot}$. We assume an ambient temperature of $T_{air}$ and a heat transfer coefficient $\\alpha$. Furthermore the domain moves with a certain velocity $v$ forward. On `S_x` and `S_y` the heat flux is zero, due to symmetry reasons. Because the temperature of the surrounding air is lower that the initial temperature of the cylinder, the cylinder cools down, while moving forward.\n", "\n", "| Description | Variable | Unit | Value |\n", "|:-----------:|:--------:|:----:|:-----:|\n", "|Density of iron|$\\rho$|kg/m³|7874|\n", "|Heat capacity of iron|$c$|J/(kg·K)|444|\n", "|Heat conductivity of iron|$k$|W/(m·K)|79.5|\n", "|Heat transfer coefficient|$\\alpha$|W/(m²·K)|20|\n", "|Heat source density|$\\dot{q}_v$|J/m³|50|\n", "|Bottom temperate|$T_{bot}$|K|293|\n", "|Air temperature|$T_{air}$|K|273|\n", "|Radius of cylinder|$r$|m|0.2|\n", "|Height of cylinder|$l$|m|1|\n", "|Velocity of cylinder|$v$|m/s|0.001|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation\n", "Download these files:\n", "\n", "- Journal file: [`mesh.jou`](mesh.jou)\n", "- Material file: [`mat.xml`](mat.xml)\n", "- Simulation file: [`simulation.xml`](simulation.xml)\n", " \n", "\n", "Create the mesh using `mesh.jou` and Trelis and run the simulation with cfs:\n", "\n", "- Terminal command for meshing: `trelis -batch -nographics -nojournal UnitCube.jou`\n", "- Terminal command for simulation: `cfs simulation`\n", "\n", "The simulation results should be in the `./results_hdf5/`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Postprocessing with pyhton\n", "For Python-postprocessing we are going to use the python-library [`hdf5_tools.py`](https://gitlab.com/openCFS/cfs/-/blob/master/share/python/hdf5_tools.py). This library is also enrolled automatic with every openCFS-version and can be found under `/path/to/install/dir/CFS/share/python/hdf5_tools.py`.\n", "\n", "In this tutorial we are going to use two functions from `hdf5_tools.py`:\n", "* `get_result()`\n", "* `get_coordinates()`\n", "\n", "How each function works, is described in the according docstring.\n", "\n", "Lets start with reading the nodal result (temperature) out of the CFS-file with `get_result()`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import sys\n", "sys.path.insert(0, \"./Devel/CFS_SRC/CFS/share/python/hdf\")\n", "from hdf5_tools import get_result\n", "\n", "#Reading in the cfs-file, instert here the path to your cfs-file\n", "hdf5=f'./results_hdf5/simulation.cfs'\n", "\n", "#Reading the temperatue out of the cfs-file\n", "T=get_result(hdf5,\"heatTemperature\", region=\"V_all\", multistep=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After reading the CFS-file, the array `T` contains now the nodal temperature of all the nodes in the region `V_all`. Now we could simply search for the maximum and minimum temperature:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The maximal temperature is 20.0°C.\n", "The minimal temperature is 19.189°C.\n" ] } ], "source": [ "T_max=T.max()\n", "print(f'The maximal temperature is {np.round(T_max,3)}°C.')\n", "T_min=T.min()\n", "print(f'The minimal temperature is {np.round(T_min,3)}°C.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The maximal temperature is 20°C and its clear, that it occurs on the `S_bot` since we prescribed the temperature there\n", "\n", "And where does the minimal temperature occurs? For this we going to use `get_coordinates()`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from hdf5_tools import get_coordinates\n", "# Getting the node, where the minimal temperature occurs\n", "Idx_min=np.argwhere(T==T.min())\n", "\n", "#Get the coordinates for each node\n", "X=get_coordinates(hdf5, region=\"V_all\")\n", "#Pluggin in the indices for maximal and minimal temperatures:\n", "X_min=X[Idx_min]\n", "# Reshaping X_min\n", "X_min=X_min.reshape(3)\n", "\n", "print(f'The coordinates for the minimal temperature are {X_min} ([x,y,z])')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, but actually i want them in polar coordinates. Well thats quite simple to achieve:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "In radial coordinates it is [ 2.00e-02 -1.35e+02 5.00e-02] ([r,phi,z]; phi in grad)\n" ] } ], "source": [ "#Quickly write a funtcion which convertes carthesian coord into clyindirc coords\n", "def cart2pol(X):\n", " r=np.sqrt(X[0]**2 + X[1]**2)\n", " phi=np.arctan2(X[1],X[0])\n", " phi=phi*360/(2*np.pi) #for degrees\n", " z=X[2]\n", " return [r,phi,z]\n", "\n", "print(f'In radial coordinates it is {np.round(cart2pol(X_min),2)} ([r,phi,z]; phi in grad)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cool, that seems plausible.\n", "\n", "But how to i get the temperature distribution along the z-direction in the middle of the cylinder?\n", "\n", "For this we read out all the indices where x==0 and y==0 is zero, and then we could simply use the indices and get the according temperatures, right?\n", "(Because we are using symmetry for our mesh, there are actually nodes in the middle of the cylinder along the z-direction at x==0 and y==0, otherwise you have to interpolate or take the nearest node.)\n", "\n", " Lets give it a try:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Temperature in °C')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#idx where x is zero\n", "idx_x=np.where(X[:,0]==0)\n", "#idx where y is zero\n", "idx_y=np.where(X[:,1]==0)\n", "#idx where x and y is zero, by comparing the two arrays idx_x and idx_y and only taking the indizes which occurring in both arrays\n", "idx=np.intersect1d(idx_x,idx_y)\n", "\n", "#Temperature for the indices where x and y == 0\n", "T_middle=T[idx]\n", "#Z-Coordinates for the indices where x and y ==0\n", "X_z=X[idx,2]\n", "\n", "plt.plot(X_z,T_middle)\n", "plt.xlabel('z-direction in m')\n", "plt.ylabel('Temperature in °C')\n", "plt.title('Temperature over z-direction')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So far so good, the result looks pretty promising. At z=-0.05 it has the prescribed temperature of 20°C and it gets cooled down the further it travels top. but if we have a closer look, its seem like the plot is incorrect at the beginning. There lets just plot the array `X_z`:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'X_z-value over the X_z indices')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(X_z)\n", "plt.ylabel('z-direction')\n", "plt.xlabel('X_z indices')\n", "plt.title('X_z-value over the X_z indices')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems like the first and second indices should be switched, as the first entry in X_z[0] does have a higher value that X_z[1]. Therefore we have to sort X_z that it begins with the smallest z-value and ends with the biggest. But we also have to track how the indices are changing. If we dont do this, we cant assign them to the correct temperature." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'X_z-value over the X_z indices')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Lets use np.argsort from numpy-module which is awesome\n", "Idx_sort=np.argsort(X_z)\n", "#Now the array is sorted\n", "X_z=X_z[Idx_sort]\n", "plt.plot(X_z)\n", "plt.ylabel('z-direction')\n", "plt.xlabel('X_z indices')\n", "plt.title('X_z-value over the X_z indices (sorted)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nice, now `X_z` is sorted. With the same indices the temperature can be sorted:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Temperature in °C')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Sort the T_middle accordingly to X_z\n", "T_middle=T_middle[Idx_sort]\n", "#Lets plot it again\n", "plt.plot(X_z,T_middle)\n", "plt.xlabel('z-direction in m')\n", "plt.ylabel('Temperature in °C')\n", "plt.title('Temperature over z-direction (sorted)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I hope you learned something. Please keep in mind that in this example we are using only coordinates, where we know a node exists.\n", "\n", "Here is the according [Jupyter-notebook](README.ipynb)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }