diff --git a/examples/analyze_results.ipynb b/examples/analyze_results.ipynb new file mode 100644 index 00000000..18c953b2 --- /dev/null +++ b/examples/analyze_results.ipynb @@ -0,0 +1,1575 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "49c09b62", + "metadata": {}, + "outputs": [], + "source": [ + "import h5py\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.interpolate import griddata" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "28d62683", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/bstanisl/Documents/repos/PVade/examples'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "26d72d41", + "metadata": {}, + "outputs": [], + "source": [ + "fname = '../output/empty2d/solution/solution_fluid.h5'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03b34eb9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top-level keys: ['Function', 'Mesh']\n", + "Function -> \n", + "Function/f -> \n", + "Function/f/0 -> \n", + "Function/f/0_02 -> \n", + "Function/f/0_040000000000000001 -> \n", + "Function/f/0_059999999999999998 -> \n", + "Function/f/0_080000000000000002 -> \n", + "Function/f/0_10000000000000001 -> \n", + "Function/f/0_12 -> \n", + "Function/f/0_14000000000000001 -> \n", + "Function/f/0_16 -> \n", + "Function/f/0_17999999999999999 -> \n", + "Function/f/0_20000000000000001 -> \n", + "Function/f/0_22 -> \n", + "Function/f/0_23999999999999999 -> \n", + "Function/f/0_26000000000000001 -> \n", + "Function/f/0_28000000000000003 -> \n", + "Function/f/0_29999999999999999 -> \n", + "Function/f/0_32000000000000001 -> \n", + "Function/f/0_34000000000000002 -> \n", + "Function/f/0_35999999999999999 -> \n", + "Function/f/0_38 -> \n", + "Function/f/0_40000000000000002 -> \n", + "Function/f/0_41999999999999998 -> \n", + "Function/f/0_44 -> \n", + "Function/f/0_46000000000000002 -> \n", + "Function/f/0_47999999999999998 -> \n", + "Function/f/0_5 -> \n", + "Function/f/0_52000000000000002 -> \n", + "Function/f/0_54000000000000004 -> \n", + "Function/f/0_56000000000000005 -> \n", + "Function/f/0_57999999999999996 -> \n", + "Function/f/0_59999999999999998 -> \n", + "Function/f/0_62 -> \n", + "Function/f/0_64000000000000001 -> \n", + "Function/f/0_66000000000000003 -> \n", + "Function/f/0_68000000000000005 -> \n", + "Function/f/0_70000000000000007 -> \n", + "Function/f/0_71999999999999997 -> \n", + "Function/f/0_73999999999999999 -> \n", + "Function/f/0_76000000000000001 -> \n", + "Function/f/0_78000000000000003 -> \n", + "Function/f/0_80000000000000004 -> \n", + "Function/f/0_82000000000000006 -> \n", + "Function/f/0_83999999999999997 -> \n", + "Function/f/0_85999999999999999 -> \n", + "Function/f/0_88 -> \n", + "Function/f/0_90000000000000002 -> \n", + "Function/f/0_92000000000000004 -> \n", + "Function/f/0_94000000000000006 -> \n", + "Function/f/0_95999999999999996 -> \n", + "Function/f/0_97999999999999998 -> \n", + "Function/f/1 -> \n", + "Function/f/1_02 -> \n", + "Function/f/1_04 -> \n", + "Function/f/1_0600000000000001 -> \n", + "Function/f/1_0800000000000001 -> \n", + "Function/f/1_1000000000000001 -> \n", + "Function/f/1_1200000000000001 -> \n", + "Function/f/1_1400000000000001 -> \n", + "Function/f/1_1599999999999999 -> \n", + "Function/f/1_1799999999999999 -> \n", + "Function/f/1_2 -> \n", + "Function/f/1_22 -> \n", + "Function/f/1_24 -> \n", + "Function/f/1_26 -> \n", + "Function/f/1_28 -> \n", + "Function/f/1_3 -> \n", + "Function/f/1_3200000000000001 -> \n", + "Function/f/1_3400000000000001 -> \n", + "Function/f/1_3600000000000001 -> \n", + "Function/f/1_3800000000000001 -> \n", + "Function/f/1_4000000000000001 -> \n", + "Function/f/1_4199999999999999 -> \n", + "Function/f/1_4399999999999999 -> \n", + "Function/f/1_46 -> \n", + "Function/f/1_48 -> \n", + "Function/f/1_5 -> \n", + "Function/f/1_52 -> \n", + "Function/f/1_54 -> \n", + "Function/f/1_5600000000000001 -> \n", + "Function/f/1_5800000000000001 -> \n", + "Function/f/1_6000000000000001 -> \n", + "Function/f/1_6200000000000001 -> \n", + "Function/f/1_6400000000000001 -> \n", + "Function/f/1_6600000000000001 -> \n", + "Function/f/1_6799999999999999 -> \n", + "Function/f/1_7 -> \n", + "Function/f/1_72 -> \n", + "Function/f/1_74 -> \n", + "Function/f/1_76 -> \n", + "Function/f/1_78 -> \n", + "Function/f/1_8 -> \n", + "Function/f/1_8200000000000001 -> \n", + "Function/f/1_8400000000000001 -> \n", + "Function/f/1_8600000000000001 -> \n", + "Function/f/1_8800000000000001 -> \n", + "Function/f/1_9000000000000001 -> \n", + "Function/f/1_9199999999999999 -> \n", + "Function/f/1_9399999999999999 -> \n", + "Function/f/1_96 -> \n", + "Function/f/1_98 -> \n", + "Function/f/2 -> \n", + "Function/f/2_02 -> \n", + "Function/f/2_04 -> \n", + "Function/f/2_0600000000000001 -> \n", + "Function/f/2_0800000000000001 -> \n", + "Function/f/2_1000000000000001 -> \n", + "Function/f/2_1200000000000001 -> \n", + "Function/f/2_1400000000000001 -> \n", + "Function/f/2_1600000000000001 -> \n", + "Function/f/2_1800000000000002 -> \n", + "Function/f/2_2000000000000002 -> \n", + "Function/f/2_2200000000000002 -> \n", + "Function/f/2_2400000000000002 -> \n", + "Function/f/2_2600000000000002 -> \n", + "Function/f/2_2800000000000002 -> \n", + "Function/f/2_3000000000000003 -> \n", + "Function/f/2_3199999999999998 -> \n", + "Function/f/2_3399999999999999 -> \n", + "Function/f/2_3599999999999999 -> \n", + "Function/f/2_3799999999999999 -> \n", + "Function/f/2_3999999999999999 -> \n", + "Function/f/2_4199999999999999 -> \n", + "Function/f/2_4399999999999999 -> \n", + "Function/f/2_46 -> \n", + "Function/f/2_48 -> \n", + "Function/f/2_5 -> \n", + "Function/f/2_52 -> \n", + "Function/f/2_54 -> \n", + "Function/f/2_5600000000000001 -> \n", + "Function/f/2_5800000000000001 -> \n", + "Function/f/2_6000000000000001 -> \n", + "Function/f/2_6200000000000001 -> \n", + "Function/f/2_6400000000000001 -> \n", + "Function/f/2_6600000000000001 -> \n", + "Function/f/2_6800000000000002 -> \n", + "Function/f/2_7000000000000002 -> \n", + "Function/f/2_7200000000000002 -> \n", + "Function/f/2_7400000000000002 -> \n", + "Function/f/2_7600000000000002 -> \n", + "Function/f/2_7800000000000002 -> \n", + "Function/f/2_8000000000000003 -> \n", + "Function/f/2_8199999999999998 -> \n", + "Function/f/2_8399999999999999 -> \n", + "Function/f/2_8599999999999999 -> \n", + "Function/f/2_8799999999999999 -> \n", + "Function/f/2_8999999999999999 -> \n", + "Function/f/2_9199999999999999 -> \n", + "Function/f/2_9399999999999999 -> \n", + "Function/f/2_96 -> \n", + "Function/f/2_98 -> \n", + "Function/f/3 -> \n", + "Function/f/3_02 -> \n", + "Function/f/3_04 -> \n", + "Function/f/3_0600000000000001 -> \n", + "Function/f/3_0800000000000001 -> \n", + "Function/f/3_1000000000000001 -> \n", + "Function/f/3_1200000000000001 -> \n", + "Function/f/3_1400000000000001 -> \n", + "Function/f/3_1600000000000001 -> \n", + "Function/f/3_1800000000000002 -> \n", + "Function/f/3_2000000000000002 -> \n", + "Function/f/3_2200000000000002 -> \n", + "Function/f/3_2400000000000002 -> \n", + "Function/f/3_2600000000000002 -> \n", + "Function/f/3_2800000000000002 -> \n", + "Function/f/3_3000000000000003 -> \n", + "Function/f/3_3200000000000003 -> \n", + "Function/f/3_3399999999999999 -> \n", + "Function/f/3_3599999999999999 -> \n", + "Function/f/3_3799999999999999 -> \n", + "Function/f/3_3999999999999999 -> \n", + "Function/f/3_4199999999999999 -> \n", + "Function/f/3_4399999999999999 -> \n", + "Function/f/3_46 -> \n", + "Function/f/3_48 -> \n", + "Function/f/3_5 -> \n", + "Function/f/3_52 -> \n", + "Function/f/3_54 -> \n", + "Function/f/3_5600000000000001 -> \n", + "Function/f/3_5800000000000001 -> \n", + "Function/f/3_6000000000000001 -> \n", + "Function/f/3_6200000000000001 -> \n", + "Function/f/3_6400000000000001 -> \n", + "Function/f/3_6600000000000001 -> \n", + "Function/f/3_6800000000000002 -> \n", + "Function/f/3_7000000000000002 -> \n", + "Function/f/3_7200000000000002 -> \n", + "Function/f/3_7400000000000002 -> \n", + "Function/f/3_7600000000000002 -> \n", + "Function/f/3_7800000000000002 -> \n", + "Function/f/3_8000000000000003 -> \n", + "Function/f/3_8200000000000003 -> \n", + "Function/f/3_8399999999999999 -> \n", + "Function/f/3_8599999999999999 -> \n", + "Function/f/3_8799999999999999 -> \n", + "Function/f/3_8999999999999999 -> \n", + "Function/f/3_9199999999999999 -> \n", + "Function/f/3_9399999999999999 -> \n", + "Function/f/3_96 -> \n", + "Function/f/3_98 -> \n", + "Function/f/4 -> \n", + "Function/f/4_0200000000000005 -> \n", + "Function/f/4_04 -> \n", + "Function/f/4_0600000000000005 -> \n", + "Function/f/4_0800000000000001 -> \n", + "Function/f/4_0999999999999996 -> \n", + "Function/f/4_1200000000000001 -> \n", + "Function/f/4_1399999999999997 -> \n", + "Function/f/4_1600000000000001 -> \n", + "Function/f/4_1799999999999997 -> \n", + "Function/f/4_2000000000000002 -> \n", + "Function/f/4_2199999999999998 -> \n", + "Function/f/4_2400000000000002 -> \n", + "Function/f/4_2599999999999998 -> \n", + "Function/f/4_2800000000000002 -> \n", + "Function/f/4_2999999999999998 -> \n", + "Function/f/4_3200000000000003 -> \n", + "Function/f/4_3399999999999999 -> \n", + "Function/f/4_3600000000000003 -> \n", + "Function/f/4_3799999999999999 -> \n", + "Function/f/4_4000000000000004 -> \n", + "Function/f/4_4199999999999999 -> \n", + "Function/f/4_4400000000000004 -> \n", + "Function/f/4_46 -> \n", + "Function/f/4_4800000000000004 -> \n", + "Function/f/4_5 -> \n", + "Function/f/4_5200000000000005 -> \n", + "Function/f/4_54 -> \n", + "Function/f/4_5600000000000005 -> \n", + "Function/f/4_5800000000000001 -> \n", + "Function/f/4_6000000000000005 -> \n", + "Function/f/4_6200000000000001 -> \n", + "Function/f/4_6399999999999997 -> \n", + "Function/f/4_6600000000000001 -> \n", + "Function/f/4_6799999999999997 -> \n", + "Function/f/4_7000000000000002 -> \n", + "Function/f/4_7199999999999998 -> \n", + "Function/f/4_7400000000000002 -> \n", + "Function/f/4_7599999999999998 -> \n", + "Function/f/4_7800000000000002 -> \n", + "Function/f/4_7999999999999998 -> \n", + "Function/f/4_8200000000000003 -> \n", + "Function/f/4_8399999999999999 -> \n", + "Function/f/4_8600000000000003 -> \n", + "Function/f/4_8799999999999999 -> \n", + "Function/f/4_9000000000000004 -> \n", + "Function/f/4_9199999999999999 -> \n", + "Function/f/4_9400000000000004 -> \n", + "Function/f/4_96 -> \n", + "Function/f/4_9800000000000004 -> \n", + "Function/f/5 -> \n", + "Function/pressure -> \n", + "Function/pressure/0 -> \n", + "Function/pressure/0_02 -> \n", + "Function/pressure/0_040000000000000001 -> \n", + "Function/pressure/0_059999999999999998 -> \n", + "Function/pressure/0_080000000000000002 -> \n", + "Function/pressure/0_10000000000000001 -> \n", + "Function/pressure/0_12 -> \n", + "Function/pressure/0_14000000000000001 -> \n", + "Function/pressure/0_16 -> \n", + "Function/pressure/0_17999999999999999 -> \n", + "Function/pressure/0_20000000000000001 -> \n", + "Function/pressure/0_22 -> \n", + "Function/pressure/0_23999999999999999 -> \n", + "Function/pressure/0_26000000000000001 -> \n", + "Function/pressure/0_28000000000000003 -> \n", + "Function/pressure/0_29999999999999999 -> \n", + "Function/pressure/0_32000000000000001 -> \n", + "Function/pressure/0_34000000000000002 -> \n", + "Function/pressure/0_35999999999999999 -> \n", + "Function/pressure/0_38 -> \n", + "Function/pressure/0_40000000000000002 -> \n", + "Function/pressure/0_41999999999999998 -> \n", + "Function/pressure/0_44 -> \n", + "Function/pressure/0_46000000000000002 -> \n", + "Function/pressure/0_47999999999999998 -> \n", + "Function/pressure/0_5 -> \n", + "Function/pressure/0_52000000000000002 -> \n", + "Function/pressure/0_54000000000000004 -> \n", + "Function/pressure/0_56000000000000005 -> \n", + "Function/pressure/0_57999999999999996 -> \n", + "Function/pressure/0_59999999999999998 -> \n", + "Function/pressure/0_62 -> \n", + "Function/pressure/0_64000000000000001 -> \n", + "Function/pressure/0_66000000000000003 -> \n", + "Function/pressure/0_68000000000000005 -> \n", + "Function/pressure/0_70000000000000007 -> \n", + "Function/pressure/0_71999999999999997 -> \n", + "Function/pressure/0_73999999999999999 -> \n", + "Function/pressure/0_76000000000000001 -> \n", + "Function/pressure/0_78000000000000003 -> \n", + "Function/pressure/0_80000000000000004 -> \n", + "Function/pressure/0_82000000000000006 -> \n", + "Function/pressure/0_83999999999999997 -> \n", + "Function/pressure/0_85999999999999999 -> \n", + "Function/pressure/0_88 -> \n", + "Function/pressure/0_90000000000000002 -> \n", + "Function/pressure/0_92000000000000004 -> \n", + "Function/pressure/0_94000000000000006 -> \n", + "Function/pressure/0_95999999999999996 -> \n", + "Function/pressure/0_97999999999999998 -> \n", + "Function/pressure/1 -> \n", + "Function/pressure/1_02 -> \n", + "Function/pressure/1_04 -> \n", + "Function/pressure/1_0600000000000001 -> \n", + "Function/pressure/1_0800000000000001 -> \n", + "Function/pressure/1_1000000000000001 -> \n", + "Function/pressure/1_1200000000000001 -> \n", + "Function/pressure/1_1400000000000001 -> \n", + "Function/pressure/1_1599999999999999 -> \n", + "Function/pressure/1_1799999999999999 -> \n", + "Function/pressure/1_2 -> \n", + "Function/pressure/1_22 -> \n", + "Function/pressure/1_24 -> \n", + "Function/pressure/1_26 -> \n", + "Function/pressure/1_28 -> \n", + "Function/pressure/1_3 -> \n", + "Function/pressure/1_3200000000000001 -> \n", + "Function/pressure/1_3400000000000001 -> \n", + "Function/pressure/1_3600000000000001 -> \n", + "Function/pressure/1_3800000000000001 -> \n", + "Function/pressure/1_4000000000000001 -> \n", + "Function/pressure/1_4199999999999999 -> \n", + "Function/pressure/1_4399999999999999 -> \n", + "Function/pressure/1_46 -> \n", + "Function/pressure/1_48 -> \n", + "Function/pressure/1_5 -> \n", + "Function/pressure/1_52 -> \n", + "Function/pressure/1_54 -> \n", + "Function/pressure/1_5600000000000001 -> \n", + "Function/pressure/1_5800000000000001 -> \n", + "Function/pressure/1_6000000000000001 -> \n", + "Function/pressure/1_6200000000000001 -> \n", + "Function/pressure/1_6400000000000001 -> \n", + "Function/pressure/1_6600000000000001 -> \n", + "Function/pressure/1_6799999999999999 -> \n", + "Function/pressure/1_7 -> \n", + "Function/pressure/1_72 -> \n", + "Function/pressure/1_74 -> \n", + "Function/pressure/1_76 -> \n", + "Function/pressure/1_78 -> \n", + "Function/pressure/1_8 -> \n", + "Function/pressure/1_8200000000000001 -> \n", + "Function/pressure/1_8400000000000001 -> \n", + "Function/pressure/1_8600000000000001 -> \n", + "Function/pressure/1_8800000000000001 -> \n", + "Function/pressure/1_9000000000000001 -> \n", + "Function/pressure/1_9199999999999999 -> \n", + "Function/pressure/1_9399999999999999 -> \n", + "Function/pressure/1_96 -> \n", + "Function/pressure/1_98 -> \n", + "Function/pressure/2 -> \n", + "Function/pressure/2_02 -> \n", + "Function/pressure/2_04 -> \n", + "Function/pressure/2_0600000000000001 -> \n", + "Function/pressure/2_0800000000000001 -> \n", + "Function/pressure/2_1000000000000001 -> \n", + "Function/pressure/2_1200000000000001 -> \n", + "Function/pressure/2_1400000000000001 -> \n", + "Function/pressure/2_1600000000000001 -> \n", + "Function/pressure/2_1800000000000002 -> \n", + "Function/pressure/2_2000000000000002 -> \n", + "Function/pressure/2_2200000000000002 -> \n", + "Function/pressure/2_2400000000000002 -> \n", + "Function/pressure/2_2600000000000002 -> \n", + "Function/pressure/2_2800000000000002 -> \n", + "Function/pressure/2_3000000000000003 -> \n", + "Function/pressure/2_3199999999999998 -> \n", + "Function/pressure/2_3399999999999999 -> \n", + "Function/pressure/2_3599999999999999 -> \n", + "Function/pressure/2_3799999999999999 -> \n", + "Function/pressure/2_3999999999999999 -> \n", + "Function/pressure/2_4199999999999999 -> \n", + "Function/pressure/2_4399999999999999 -> \n", + "Function/pressure/2_46 -> \n", + "Function/pressure/2_48 -> \n", + "Function/pressure/2_5 -> \n", + "Function/pressure/2_52 -> \n", + "Function/pressure/2_54 -> \n", + "Function/pressure/2_5600000000000001 -> \n", + "Function/pressure/2_5800000000000001 -> \n", + "Function/pressure/2_6000000000000001 -> \n", + "Function/pressure/2_6200000000000001 -> \n", + "Function/pressure/2_6400000000000001 -> \n", + "Function/pressure/2_6600000000000001 -> \n", + "Function/pressure/2_6800000000000002 -> \n", + "Function/pressure/2_7000000000000002 -> \n", + "Function/pressure/2_7200000000000002 -> \n", + "Function/pressure/2_7400000000000002 -> \n", + "Function/pressure/2_7600000000000002 -> \n", + "Function/pressure/2_7800000000000002 -> \n", + "Function/pressure/2_8000000000000003 -> \n", + "Function/pressure/2_8199999999999998 -> \n", + "Function/pressure/2_8399999999999999 -> \n", + "Function/pressure/2_8599999999999999 -> \n", + "Function/pressure/2_8799999999999999 -> \n", + "Function/pressure/2_8999999999999999 -> \n", + "Function/pressure/2_9199999999999999 -> \n", + "Function/pressure/2_9399999999999999 -> \n", + "Function/pressure/2_96 -> \n", + "Function/pressure/2_98 -> \n", + "Function/pressure/3 -> \n", + "Function/pressure/3_02 -> \n", + "Function/pressure/3_04 -> \n", + "Function/pressure/3_0600000000000001 -> \n", + "Function/pressure/3_0800000000000001 -> \n", + "Function/pressure/3_1000000000000001 -> \n", + "Function/pressure/3_1200000000000001 -> \n", + "Function/pressure/3_1400000000000001 -> \n", + "Function/pressure/3_1600000000000001 -> \n", + "Function/pressure/3_1800000000000002 -> \n", + "Function/pressure/3_2000000000000002 -> \n", + "Function/pressure/3_2200000000000002 -> \n", + "Function/pressure/3_2400000000000002 -> \n", + "Function/pressure/3_2600000000000002 -> \n", + "Function/pressure/3_2800000000000002 -> \n", + "Function/pressure/3_3000000000000003 -> \n", + "Function/pressure/3_3200000000000003 -> \n", + "Function/pressure/3_3399999999999999 -> \n", + "Function/pressure/3_3599999999999999 -> \n", + "Function/pressure/3_3799999999999999 -> \n", + "Function/pressure/3_3999999999999999 -> \n", + "Function/pressure/3_4199999999999999 -> \n", + "Function/pressure/3_4399999999999999 -> \n", + "Function/pressure/3_46 -> \n", + "Function/pressure/3_48 -> \n", + "Function/pressure/3_5 -> \n", + "Function/pressure/3_52 -> \n", + "Function/pressure/3_54 -> \n", + "Function/pressure/3_5600000000000001 -> \n", + "Function/pressure/3_5800000000000001 -> \n", + "Function/pressure/3_6000000000000001 -> \n", + "Function/pressure/3_6200000000000001 -> \n", + "Function/pressure/3_6400000000000001 -> \n", + "Function/pressure/3_6600000000000001 -> \n", + "Function/pressure/3_6800000000000002 -> \n", + "Function/pressure/3_7000000000000002 -> \n", + "Function/pressure/3_7200000000000002 -> \n", + "Function/pressure/3_7400000000000002 -> \n", + "Function/pressure/3_7600000000000002 -> \n", + "Function/pressure/3_7800000000000002 -> \n", + "Function/pressure/3_8000000000000003 -> \n", + "Function/pressure/3_8200000000000003 -> \n", + "Function/pressure/3_8399999999999999 -> \n", + "Function/pressure/3_8599999999999999 -> \n", + "Function/pressure/3_8799999999999999 -> \n", + "Function/pressure/3_8999999999999999 -> \n", + "Function/pressure/3_9199999999999999 -> \n", + "Function/pressure/3_9399999999999999 -> \n", + "Function/pressure/3_96 -> \n", + "Function/pressure/3_98 -> \n", + "Function/pressure/4 -> \n", + "Function/pressure/4_0200000000000005 -> \n", + "Function/pressure/4_04 -> \n", + "Function/pressure/4_0600000000000005 -> \n", + "Function/pressure/4_0800000000000001 -> \n", + "Function/pressure/4_0999999999999996 -> \n", + "Function/pressure/4_1200000000000001 -> \n", + "Function/pressure/4_1399999999999997 -> \n", + "Function/pressure/4_1600000000000001 -> \n", + "Function/pressure/4_1799999999999997 -> \n", + "Function/pressure/4_2000000000000002 -> \n", + "Function/pressure/4_2199999999999998 -> \n", + "Function/pressure/4_2400000000000002 -> \n", + "Function/pressure/4_2599999999999998 -> \n", + "Function/pressure/4_2800000000000002 -> \n", + "Function/pressure/4_2999999999999998 -> \n", + "Function/pressure/4_3200000000000003 -> \n", + "Function/pressure/4_3399999999999999 -> \n", + "Function/pressure/4_3600000000000003 -> \n", + "Function/pressure/4_3799999999999999 -> \n", + "Function/pressure/4_4000000000000004 -> \n", + "Function/pressure/4_4199999999999999 -> \n", + "Function/pressure/4_4400000000000004 -> \n", + "Function/pressure/4_46 -> \n", + "Function/pressure/4_4800000000000004 -> \n", + "Function/pressure/4_5 -> \n", + "Function/pressure/4_5200000000000005 -> \n", + "Function/pressure/4_54 -> \n", + "Function/pressure/4_5600000000000005 -> \n", + "Function/pressure/4_5800000000000001 -> \n", + "Function/pressure/4_6000000000000005 -> \n", + "Function/pressure/4_6200000000000001 -> \n", + "Function/pressure/4_6399999999999997 -> \n", + "Function/pressure/4_6600000000000001 -> \n", + "Function/pressure/4_6799999999999997 -> \n", + "Function/pressure/4_7000000000000002 -> \n", + "Function/pressure/4_7199999999999998 -> \n", + "Function/pressure/4_7400000000000002 -> \n", + "Function/pressure/4_7599999999999998 -> \n", + "Function/pressure/4_7800000000000002 -> \n", + "Function/pressure/4_7999999999999998 -> \n", + "Function/pressure/4_8200000000000003 -> \n", + "Function/pressure/4_8399999999999999 -> \n", + "Function/pressure/4_8600000000000003 -> \n", + "Function/pressure/4_8799999999999999 -> \n", + "Function/pressure/4_9000000000000004 -> \n", + "Function/pressure/4_9199999999999999 -> \n", + "Function/pressure/4_9400000000000004 -> \n", + "Function/pressure/4_96 -> \n", + "Function/pressure/4_9800000000000004 -> \n", + "Function/pressure/5 -> \n", + "Function/temperature -> \n", + "Function/temperature /0 -> \n", + "Function/temperature /0_02 -> \n", + "Function/temperature /0_040000000000000001 -> \n", + "Function/temperature /0_059999999999999998 -> \n", + "Function/temperature /0_080000000000000002 -> \n", + "Function/temperature /0_10000000000000001 -> \n", + "Function/temperature /0_12 -> \n", + "Function/temperature /0_14000000000000001 -> \n", + "Function/temperature /0_16 -> \n", + "Function/temperature /0_17999999999999999 -> \n", + "Function/temperature /0_20000000000000001 -> \n", + "Function/temperature /0_22 -> \n", + "Function/temperature /0_23999999999999999 -> \n", + "Function/temperature /0_26000000000000001 -> \n", + "Function/temperature /0_28000000000000003 -> \n", + "Function/temperature /0_29999999999999999 -> \n", + "Function/temperature /0_32000000000000001 -> \n", + "Function/temperature /0_34000000000000002 -> \n", + "Function/temperature /0_35999999999999999 -> \n", + "Function/temperature /0_38 -> \n", + "Function/temperature /0_40000000000000002 -> \n", + "Function/temperature /0_41999999999999998 -> \n", + "Function/temperature /0_44 -> \n", + "Function/temperature /0_46000000000000002 -> \n", + "Function/temperature /0_47999999999999998 -> \n", + "Function/temperature /0_5 -> \n", + "Function/temperature /0_52000000000000002 -> \n", + "Function/temperature /0_54000000000000004 -> \n", + "Function/temperature /0_56000000000000005 -> \n", + "Function/temperature /0_57999999999999996 -> \n", + "Function/temperature /0_59999999999999998 -> \n", + "Function/temperature /0_62 -> \n", + "Function/temperature /0_64000000000000001 -> \n", + "Function/temperature /0_66000000000000003 -> \n", + "Function/temperature /0_68000000000000005 -> \n", + "Function/temperature /0_70000000000000007 -> \n", + "Function/temperature /0_71999999999999997 -> \n", + "Function/temperature /0_73999999999999999 -> \n", + "Function/temperature /0_76000000000000001 -> \n", + "Function/temperature /0_78000000000000003 -> \n", + "Function/temperature /0_80000000000000004 -> \n", + "Function/temperature /0_82000000000000006 -> \n", + "Function/temperature /0_83999999999999997 -> \n", + "Function/temperature /0_85999999999999999 -> \n", + "Function/temperature /0_88 -> \n", + "Function/temperature /0_90000000000000002 -> \n", + "Function/temperature /0_92000000000000004 -> \n", + "Function/temperature /0_94000000000000006 -> \n", + "Function/temperature /0_95999999999999996 -> \n", + "Function/temperature /0_97999999999999998 -> \n", + "Function/temperature /1 -> \n", + "Function/temperature /1_02 -> \n", + "Function/temperature /1_04 -> \n", + "Function/temperature /1_0600000000000001 -> \n", + "Function/temperature /1_0800000000000001 -> \n", + "Function/temperature /1_1000000000000001 -> \n", + "Function/temperature /1_1200000000000001 -> \n", + "Function/temperature /1_1400000000000001 -> \n", + "Function/temperature /1_1599999999999999 -> \n", + "Function/temperature /1_1799999999999999 -> \n", + "Function/temperature /1_2 -> \n", + "Function/temperature /1_22 -> \n", + "Function/temperature /1_24 -> \n", + "Function/temperature /1_26 -> \n", + "Function/temperature /1_28 -> \n", + "Function/temperature /1_3 -> \n", + "Function/temperature /1_3200000000000001 -> \n", + "Function/temperature /1_3400000000000001 -> \n", + "Function/temperature /1_3600000000000001 -> \n", + "Function/temperature /1_3800000000000001 -> \n", + "Function/temperature /1_4000000000000001 -> \n", + "Function/temperature /1_4199999999999999 -> \n", + "Function/temperature /1_4399999999999999 -> \n", + "Function/temperature /1_46 -> \n", + "Function/temperature /1_48 -> \n", + "Function/temperature /1_5 -> \n", + "Function/temperature /1_52 -> \n", + "Function/temperature /1_54 -> \n", + "Function/temperature /1_5600000000000001 -> \n", + "Function/temperature /1_5800000000000001 -> \n", + "Function/temperature /1_6000000000000001 -> \n", + "Function/temperature /1_6200000000000001 -> \n", + "Function/temperature /1_6400000000000001 -> \n", + "Function/temperature /1_6600000000000001 -> \n", + "Function/temperature /1_6799999999999999 -> \n", + "Function/temperature /1_7 -> \n", + "Function/temperature /1_72 -> \n", + "Function/temperature /1_74 -> \n", + "Function/temperature /1_76 -> \n", + "Function/temperature /1_78 -> \n", + "Function/temperature /1_8 -> \n", + "Function/temperature /1_8200000000000001 -> \n", + "Function/temperature /1_8400000000000001 -> \n", + "Function/temperature /1_8600000000000001 -> \n", + "Function/temperature /1_8800000000000001 -> \n", + "Function/temperature /1_9000000000000001 -> \n", + "Function/temperature /1_9199999999999999 -> \n", + "Function/temperature /1_9399999999999999 -> \n", + "Function/temperature /1_96 -> \n", + "Function/temperature /1_98 -> \n", + "Function/temperature /2 -> \n", + "Function/temperature /2_02 -> \n", + "Function/temperature /2_04 -> \n", + "Function/temperature /2_0600000000000001 -> \n", + "Function/temperature /2_0800000000000001 -> \n", + "Function/temperature /2_1000000000000001 -> \n", + "Function/temperature /2_1200000000000001 -> \n", + "Function/temperature /2_1400000000000001 -> \n", + "Function/temperature /2_1600000000000001 -> \n", + "Function/temperature /2_1800000000000002 -> \n", + "Function/temperature /2_2000000000000002 -> \n", + "Function/temperature /2_2200000000000002 -> \n", + "Function/temperature /2_2400000000000002 -> \n", + "Function/temperature /2_2600000000000002 -> \n", + "Function/temperature /2_2800000000000002 -> \n", + "Function/temperature /2_3000000000000003 -> \n", + "Function/temperature /2_3199999999999998 -> \n", + "Function/temperature /2_3399999999999999 -> \n", + "Function/temperature /2_3599999999999999 -> \n", + "Function/temperature /2_3799999999999999 -> \n", + "Function/temperature /2_3999999999999999 -> \n", + "Function/temperature /2_4199999999999999 -> \n", + "Function/temperature /2_4399999999999999 -> \n", + "Function/temperature /2_46 -> \n", + "Function/temperature /2_48 -> \n", + "Function/temperature /2_5 -> \n", + "Function/temperature /2_52 -> \n", + "Function/temperature /2_54 -> \n", + "Function/temperature /2_5600000000000001 -> \n", + "Function/temperature /2_5800000000000001 -> \n", + "Function/temperature /2_6000000000000001 -> \n", + "Function/temperature /2_6200000000000001 -> \n", + "Function/temperature /2_6400000000000001 -> \n", + "Function/temperature /2_6600000000000001 -> \n", + "Function/temperature /2_6800000000000002 -> \n", + "Function/temperature /2_7000000000000002 -> \n", + "Function/temperature /2_7200000000000002 -> \n", + "Function/temperature /2_7400000000000002 -> \n", + "Function/temperature /2_7600000000000002 -> \n", + "Function/temperature /2_7800000000000002 -> \n", + "Function/temperature /2_8000000000000003 -> \n", + "Function/temperature /2_8199999999999998 -> \n", + "Function/temperature /2_8399999999999999 -> \n", + "Function/temperature /2_8599999999999999 -> \n", + "Function/temperature /2_8799999999999999 -> \n", + "Function/temperature /2_8999999999999999 -> \n", + "Function/temperature /2_9199999999999999 -> \n", + "Function/temperature /2_9399999999999999 -> \n", + "Function/temperature /2_96 -> \n", + "Function/temperature /2_98 -> \n", + "Function/temperature /3 -> \n", + "Function/temperature /3_02 -> \n", + "Function/temperature /3_04 -> \n", + "Function/temperature /3_0600000000000001 -> \n", + "Function/temperature /3_0800000000000001 -> \n", + "Function/temperature /3_1000000000000001 -> \n", + "Function/temperature /3_1200000000000001 -> \n", + "Function/temperature /3_1400000000000001 -> \n", + "Function/temperature /3_1600000000000001 -> \n", + "Function/temperature /3_1800000000000002 -> \n", + "Function/temperature /3_2000000000000002 -> \n", + "Function/temperature /3_2200000000000002 -> \n", + "Function/temperature /3_2400000000000002 -> \n", + "Function/temperature /3_2600000000000002 -> \n", + "Function/temperature /3_2800000000000002 -> \n", + "Function/temperature /3_3000000000000003 -> \n", + "Function/temperature /3_3200000000000003 -> \n", + "Function/temperature /3_3399999999999999 -> \n", + "Function/temperature /3_3599999999999999 -> \n", + "Function/temperature /3_3799999999999999 -> \n", + "Function/temperature /3_3999999999999999 -> \n", + "Function/temperature /3_4199999999999999 -> \n", + "Function/temperature /3_4399999999999999 -> \n", + "Function/temperature /3_46 -> \n", + "Function/temperature /3_48 -> \n", + "Function/temperature /3_5 -> \n", + "Function/temperature /3_52 -> \n", + "Function/temperature /3_54 -> \n", + "Function/temperature /3_5600000000000001 -> \n", + "Function/temperature /3_5800000000000001 -> \n", + "Function/temperature /3_6000000000000001 -> \n", + "Function/temperature /3_6200000000000001 -> \n", + "Function/temperature /3_6400000000000001 -> \n", + "Function/temperature /3_6600000000000001 -> \n", + "Function/temperature /3_6800000000000002 -> \n", + "Function/temperature /3_7000000000000002 -> \n", + "Function/temperature /3_7200000000000002 -> \n", + "Function/temperature /3_7400000000000002 -> \n", + "Function/temperature /3_7600000000000002 -> \n", + "Function/temperature /3_7800000000000002 -> \n", + "Function/temperature /3_8000000000000003 -> \n", + "Function/temperature /3_8200000000000003 -> \n", + "Function/temperature /3_8399999999999999 -> \n", + "Function/temperature /3_8599999999999999 -> \n", + "Function/temperature /3_8799999999999999 -> \n", + "Function/temperature /3_8999999999999999 -> \n", + "Function/temperature /3_9199999999999999 -> \n", + "Function/temperature /3_9399999999999999 -> \n", + "Function/temperature /3_96 -> \n", + "Function/temperature /3_98 -> \n", + "Function/temperature /4 -> \n", + "Function/temperature /4_0200000000000005 -> \n", + "Function/temperature /4_04 -> \n", + "Function/temperature /4_0600000000000005 -> \n", + "Function/temperature /4_0800000000000001 -> \n", + "Function/temperature /4_0999999999999996 -> \n", + "Function/temperature /4_1200000000000001 -> \n", + "Function/temperature /4_1399999999999997 -> \n", + "Function/temperature /4_1600000000000001 -> \n", + "Function/temperature /4_1799999999999997 -> \n", + "Function/temperature /4_2000000000000002 -> \n", + "Function/temperature /4_2199999999999998 -> \n", + "Function/temperature /4_2400000000000002 -> \n", + "Function/temperature /4_2599999999999998 -> \n", + "Function/temperature /4_2800000000000002 -> \n", + "Function/temperature /4_2999999999999998 -> \n", + "Function/temperature /4_3200000000000003 -> \n", + "Function/temperature /4_3399999999999999 -> \n", + "Function/temperature /4_3600000000000003 -> \n", + "Function/temperature /4_3799999999999999 -> \n", + "Function/temperature /4_4000000000000004 -> \n", + "Function/temperature /4_4199999999999999 -> \n", + "Function/temperature /4_4400000000000004 -> \n", + "Function/temperature /4_46 -> \n", + "Function/temperature /4_4800000000000004 -> \n", + "Function/temperature /4_5 -> \n", + "Function/temperature /4_5200000000000005 -> \n", + "Function/temperature /4_54 -> \n", + "Function/temperature /4_5600000000000005 -> \n", + "Function/temperature /4_5800000000000001 -> \n", + "Function/temperature /4_6000000000000005 -> \n", + "Function/temperature /4_6200000000000001 -> \n", + "Function/temperature /4_6399999999999997 -> \n", + "Function/temperature /4_6600000000000001 -> \n", + "Function/temperature /4_6799999999999997 -> \n", + "Function/temperature /4_7000000000000002 -> \n", + "Function/temperature /4_7199999999999998 -> \n", + "Function/temperature /4_7400000000000002 -> \n", + "Function/temperature /4_7599999999999998 -> \n", + "Function/temperature /4_7800000000000002 -> \n", + "Function/temperature /4_7999999999999998 -> \n", + "Function/temperature /4_8200000000000003 -> \n", + "Function/temperature /4_8399999999999999 -> \n", + "Function/temperature /4_8600000000000003 -> \n", + "Function/temperature /4_8799999999999999 -> \n", + "Function/temperature /4_9000000000000004 -> \n", + "Function/temperature /4_9199999999999999 -> \n", + "Function/temperature /4_9400000000000004 -> \n", + "Function/temperature /4_96 -> \n", + "Function/temperature /4_9800000000000004 -> \n", + "Function/temperature /5 -> \n", + "Function/total_mesh_disp -> \n", + "Function/total_mesh_disp/0 -> \n", + "Function/total_mesh_disp/0_02 -> \n", + "Function/total_mesh_disp/0_040000000000000001 -> \n", + "Function/total_mesh_disp/0_059999999999999998 -> \n", + "Function/total_mesh_disp/0_080000000000000002 -> \n", + "Function/total_mesh_disp/0_10000000000000001 -> \n", + "Function/total_mesh_disp/0_12 -> \n", + "Function/total_mesh_disp/0_14000000000000001 -> \n", + "Function/total_mesh_disp/0_16 -> \n", + "Function/total_mesh_disp/0_17999999999999999 -> \n", + "Function/total_mesh_disp/0_20000000000000001 -> \n", + "Function/total_mesh_disp/0_22 -> \n", + "Function/total_mesh_disp/0_23999999999999999 -> \n", + "Function/total_mesh_disp/0_26000000000000001 -> \n", + "Function/total_mesh_disp/0_28000000000000003 -> \n", + "Function/total_mesh_disp/0_29999999999999999 -> \n", + "Function/total_mesh_disp/0_32000000000000001 -> \n", + "Function/total_mesh_disp/0_34000000000000002 -> \n", + "Function/total_mesh_disp/0_35999999999999999 -> \n", + "Function/total_mesh_disp/0_38 -> \n", + "Function/total_mesh_disp/0_40000000000000002 -> \n", + "Function/total_mesh_disp/0_41999999999999998 -> \n", + "Function/total_mesh_disp/0_44 -> \n", + "Function/total_mesh_disp/0_46000000000000002 -> \n", + "Function/total_mesh_disp/0_47999999999999998 -> \n", + "Function/total_mesh_disp/0_5 -> \n", + "Function/total_mesh_disp/0_52000000000000002 -> \n", + "Function/total_mesh_disp/0_54000000000000004 -> \n", + "Function/total_mesh_disp/0_56000000000000005 -> \n", + "Function/total_mesh_disp/0_57999999999999996 -> \n", + "Function/total_mesh_disp/0_59999999999999998 -> \n", + "Function/total_mesh_disp/0_62 -> \n", + "Function/total_mesh_disp/0_64000000000000001 -> \n", + "Function/total_mesh_disp/0_66000000000000003 -> \n", + "Function/total_mesh_disp/0_68000000000000005 -> \n", + "Function/total_mesh_disp/0_70000000000000007 -> \n", + "Function/total_mesh_disp/0_71999999999999997 -> \n", + "Function/total_mesh_disp/0_73999999999999999 -> \n", + "Function/total_mesh_disp/0_76000000000000001 -> \n", + "Function/total_mesh_disp/0_78000000000000003 -> \n", + "Function/total_mesh_disp/0_80000000000000004 -> \n", + "Function/total_mesh_disp/0_82000000000000006 -> \n", + "Function/total_mesh_disp/0_83999999999999997 -> \n", + "Function/total_mesh_disp/0_85999999999999999 -> \n", + "Function/total_mesh_disp/0_88 -> \n", + "Function/total_mesh_disp/0_90000000000000002 -> \n", + "Function/total_mesh_disp/0_92000000000000004 -> \n", + "Function/total_mesh_disp/0_94000000000000006 -> \n", + "Function/total_mesh_disp/0_95999999999999996 -> \n", + "Function/total_mesh_disp/0_97999999999999998 -> \n", + "Function/total_mesh_disp/1 -> \n", + "Function/total_mesh_disp/1_02 -> \n", + "Function/total_mesh_disp/1_04 -> \n", + "Function/total_mesh_disp/1_0600000000000001 -> \n", + "Function/total_mesh_disp/1_0800000000000001 -> \n", + "Function/total_mesh_disp/1_1000000000000001 -> \n", + "Function/total_mesh_disp/1_1200000000000001 -> \n", + "Function/total_mesh_disp/1_1400000000000001 -> \n", + "Function/total_mesh_disp/1_1599999999999999 -> \n", + "Function/total_mesh_disp/1_1799999999999999 -> \n", + "Function/total_mesh_disp/1_2 -> \n", + "Function/total_mesh_disp/1_22 -> \n", + "Function/total_mesh_disp/1_24 -> \n", + "Function/total_mesh_disp/1_26 -> \n", + "Function/total_mesh_disp/1_28 -> \n", + "Function/total_mesh_disp/1_3 -> \n", + "Function/total_mesh_disp/1_3200000000000001 -> \n", + "Function/total_mesh_disp/1_3400000000000001 -> \n", + "Function/total_mesh_disp/1_3600000000000001 -> \n", + "Function/total_mesh_disp/1_3800000000000001 -> \n", + "Function/total_mesh_disp/1_4000000000000001 -> \n", + "Function/total_mesh_disp/1_4199999999999999 -> \n", + "Function/total_mesh_disp/1_4399999999999999 -> \n", + "Function/total_mesh_disp/1_46 -> \n", + "Function/total_mesh_disp/1_48 -> \n", + "Function/total_mesh_disp/1_5 -> \n", + "Function/total_mesh_disp/1_52 -> \n", + "Function/total_mesh_disp/1_54 -> \n", + "Function/total_mesh_disp/1_5600000000000001 -> \n", + "Function/total_mesh_disp/1_5800000000000001 -> \n", + "Function/total_mesh_disp/1_6000000000000001 -> \n", + "Function/total_mesh_disp/1_6200000000000001 -> \n", + "Function/total_mesh_disp/1_6400000000000001 -> \n", + "Function/total_mesh_disp/1_6600000000000001 -> \n", + "Function/total_mesh_disp/1_6799999999999999 -> \n", + "Function/total_mesh_disp/1_7 -> \n", + "Function/total_mesh_disp/1_72 -> \n", + "Function/total_mesh_disp/1_74 -> \n", + "Function/total_mesh_disp/1_76 -> \n", + "Function/total_mesh_disp/1_78 -> \n", + "Function/total_mesh_disp/1_8 -> \n", + "Function/total_mesh_disp/1_8200000000000001 -> \n", + "Function/total_mesh_disp/1_8400000000000001 -> \n", + "Function/total_mesh_disp/1_8600000000000001 -> \n", + "Function/total_mesh_disp/1_8800000000000001 -> \n", + "Function/total_mesh_disp/1_9000000000000001 -> \n", + "Function/total_mesh_disp/1_9199999999999999 -> \n", + "Function/total_mesh_disp/1_9399999999999999 -> \n", + "Function/total_mesh_disp/1_96 -> \n", + "Function/total_mesh_disp/1_98 -> \n", + "Function/total_mesh_disp/2 -> \n", + "Function/total_mesh_disp/2_02 -> \n", + "Function/total_mesh_disp/2_04 -> \n", + "Function/total_mesh_disp/2_0600000000000001 -> \n", + "Function/total_mesh_disp/2_0800000000000001 -> \n", + "Function/total_mesh_disp/2_1000000000000001 -> \n", + "Function/total_mesh_disp/2_1200000000000001 -> \n", + "Function/total_mesh_disp/2_1400000000000001 -> \n", + "Function/total_mesh_disp/2_1600000000000001 -> \n", + "Function/total_mesh_disp/2_1800000000000002 -> \n", + "Function/total_mesh_disp/2_2000000000000002 -> \n", + "Function/total_mesh_disp/2_2200000000000002 -> \n", + "Function/total_mesh_disp/2_2400000000000002 -> \n", + "Function/total_mesh_disp/2_2600000000000002 -> \n", + "Function/total_mesh_disp/2_2800000000000002 -> \n", + "Function/total_mesh_disp/2_3000000000000003 -> \n", + "Function/total_mesh_disp/2_3199999999999998 -> \n", + "Function/total_mesh_disp/2_3399999999999999 -> \n", + "Function/total_mesh_disp/2_3599999999999999 -> \n", + "Function/total_mesh_disp/2_3799999999999999 -> \n", + "Function/total_mesh_disp/2_3999999999999999 -> \n", + "Function/total_mesh_disp/2_4199999999999999 -> \n", + "Function/total_mesh_disp/2_4399999999999999 -> \n", + "Function/total_mesh_disp/2_46 -> \n", + "Function/total_mesh_disp/2_48 -> \n", + "Function/total_mesh_disp/2_5 -> \n", + "Function/total_mesh_disp/2_52 -> \n", + "Function/total_mesh_disp/2_54 -> \n", + "Function/total_mesh_disp/2_5600000000000001 -> \n", + "Function/total_mesh_disp/2_5800000000000001 -> \n", + "Function/total_mesh_disp/2_6000000000000001 -> \n", + "Function/total_mesh_disp/2_6200000000000001 -> \n", + "Function/total_mesh_disp/2_6400000000000001 -> \n", + "Function/total_mesh_disp/2_6600000000000001 -> \n", + "Function/total_mesh_disp/2_6800000000000002 -> \n", + "Function/total_mesh_disp/2_7000000000000002 -> \n", + "Function/total_mesh_disp/2_7200000000000002 -> \n", + "Function/total_mesh_disp/2_7400000000000002 -> \n", + "Function/total_mesh_disp/2_7600000000000002 -> \n", + "Function/total_mesh_disp/2_7800000000000002 -> \n", + "Function/total_mesh_disp/2_8000000000000003 -> \n", + "Function/total_mesh_disp/2_8199999999999998 -> \n", + "Function/total_mesh_disp/2_8399999999999999 -> \n", + "Function/total_mesh_disp/2_8599999999999999 -> \n", + "Function/total_mesh_disp/2_8799999999999999 -> \n", + "Function/total_mesh_disp/2_8999999999999999 -> \n", + "Function/total_mesh_disp/2_9199999999999999 -> \n", + "Function/total_mesh_disp/2_9399999999999999 -> \n", + "Function/total_mesh_disp/2_96 -> \n", + "Function/total_mesh_disp/2_98 -> \n", + "Function/total_mesh_disp/3 -> \n", + "Function/total_mesh_disp/3_02 -> \n", + "Function/total_mesh_disp/3_04 -> \n", + "Function/total_mesh_disp/3_0600000000000001 -> \n", + "Function/total_mesh_disp/3_0800000000000001 -> \n", + "Function/total_mesh_disp/3_1000000000000001 -> \n", + "Function/total_mesh_disp/3_1200000000000001 -> \n", + "Function/total_mesh_disp/3_1400000000000001 -> \n", + "Function/total_mesh_disp/3_1600000000000001 -> \n", + "Function/total_mesh_disp/3_1800000000000002 -> \n", + "Function/total_mesh_disp/3_2000000000000002 -> \n", + "Function/total_mesh_disp/3_2200000000000002 -> \n", + "Function/total_mesh_disp/3_2400000000000002 -> \n", + "Function/total_mesh_disp/3_2600000000000002 -> \n", + "Function/total_mesh_disp/3_2800000000000002 -> \n", + "Function/total_mesh_disp/3_3000000000000003 -> \n", + "Function/total_mesh_disp/3_3200000000000003 -> \n", + "Function/total_mesh_disp/3_3399999999999999 -> \n", + "Function/total_mesh_disp/3_3599999999999999 -> \n", + "Function/total_mesh_disp/3_3799999999999999 -> \n", + "Function/total_mesh_disp/3_3999999999999999 -> \n", + "Function/total_mesh_disp/3_4199999999999999 -> \n", + "Function/total_mesh_disp/3_4399999999999999 -> \n", + "Function/total_mesh_disp/3_46 -> \n", + "Function/total_mesh_disp/3_48 -> \n", + "Function/total_mesh_disp/3_5 -> \n", + "Function/total_mesh_disp/3_52 -> \n", + "Function/total_mesh_disp/3_54 -> \n", + "Function/total_mesh_disp/3_5600000000000001 -> \n", + "Function/total_mesh_disp/3_5800000000000001 -> \n", + "Function/total_mesh_disp/3_6000000000000001 -> \n", + "Function/total_mesh_disp/3_6200000000000001 -> \n", + "Function/total_mesh_disp/3_6400000000000001 -> \n", + "Function/total_mesh_disp/3_6600000000000001 -> \n", + "Function/total_mesh_disp/3_6800000000000002 -> \n", + "Function/total_mesh_disp/3_7000000000000002 -> \n", + "Function/total_mesh_disp/3_7200000000000002 -> \n", + "Function/total_mesh_disp/3_7400000000000002 -> \n", + "Function/total_mesh_disp/3_7600000000000002 -> \n", + "Function/total_mesh_disp/3_7800000000000002 -> \n", + "Function/total_mesh_disp/3_8000000000000003 -> \n", + "Function/total_mesh_disp/3_8200000000000003 -> \n", + "Function/total_mesh_disp/3_8399999999999999 -> \n", + "Function/total_mesh_disp/3_8599999999999999 -> \n", + "Function/total_mesh_disp/3_8799999999999999 -> \n", + "Function/total_mesh_disp/3_8999999999999999 -> \n", + "Function/total_mesh_disp/3_9199999999999999 -> \n", + "Function/total_mesh_disp/3_9399999999999999 -> \n", + "Function/total_mesh_disp/3_96 -> \n", + "Function/total_mesh_disp/3_98 -> \n", + "Function/total_mesh_disp/4 -> \n", + "Function/total_mesh_disp/4_0200000000000005 -> \n", + "Function/total_mesh_disp/4_04 -> \n", + "Function/total_mesh_disp/4_0600000000000005 -> \n", + "Function/total_mesh_disp/4_0800000000000001 -> \n", + "Function/total_mesh_disp/4_0999999999999996 -> \n", + "Function/total_mesh_disp/4_1200000000000001 -> \n", + "Function/total_mesh_disp/4_1399999999999997 -> \n", + "Function/total_mesh_disp/4_1600000000000001 -> \n", + "Function/total_mesh_disp/4_1799999999999997 -> \n", + "Function/total_mesh_disp/4_2000000000000002 -> \n", + "Function/total_mesh_disp/4_2199999999999998 -> \n", + "Function/total_mesh_disp/4_2400000000000002 -> \n", + "Function/total_mesh_disp/4_2599999999999998 -> \n", + "Function/total_mesh_disp/4_2800000000000002 -> \n", + "Function/total_mesh_disp/4_2999999999999998 -> \n", + "Function/total_mesh_disp/4_3200000000000003 -> \n", + "Function/total_mesh_disp/4_3399999999999999 -> \n", + "Function/total_mesh_disp/4_3600000000000003 -> \n", + "Function/total_mesh_disp/4_3799999999999999 -> \n", + "Function/total_mesh_disp/4_4000000000000004 -> \n", + "Function/total_mesh_disp/4_4199999999999999 -> \n", + "Function/total_mesh_disp/4_4400000000000004 -> \n", + "Function/total_mesh_disp/4_46 -> \n", + "Function/total_mesh_disp/4_4800000000000004 -> \n", + "Function/total_mesh_disp/4_5 -> \n", + "Function/total_mesh_disp/4_5200000000000005 -> \n", + "Function/total_mesh_disp/4_54 -> \n", + "Function/total_mesh_disp/4_5600000000000005 -> \n", + "Function/total_mesh_disp/4_5800000000000001 -> \n", + "Function/total_mesh_disp/4_6000000000000005 -> \n", + "Function/total_mesh_disp/4_6200000000000001 -> \n", + "Function/total_mesh_disp/4_6399999999999997 -> \n", + "Function/total_mesh_disp/4_6600000000000001 -> \n", + "Function/total_mesh_disp/4_6799999999999997 -> \n", + "Function/total_mesh_disp/4_7000000000000002 -> \n", + "Function/total_mesh_disp/4_7199999999999998 -> \n", + "Function/total_mesh_disp/4_7400000000000002 -> \n", + "Function/total_mesh_disp/4_7599999999999998 -> \n", + "Function/total_mesh_disp/4_7800000000000002 -> \n", + "Function/total_mesh_disp/4_7999999999999998 -> \n", + "Function/total_mesh_disp/4_8200000000000003 -> \n", + "Function/total_mesh_disp/4_8399999999999999 -> \n", + "Function/total_mesh_disp/4_8600000000000003 -> \n", + "Function/total_mesh_disp/4_8799999999999999 -> \n", + "Function/total_mesh_disp/4_9000000000000004 -> \n", + "Function/total_mesh_disp/4_9199999999999999 -> \n", + "Function/total_mesh_disp/4_9400000000000004 -> \n", + "Function/total_mesh_disp/4_96 -> \n", + "Function/total_mesh_disp/4_9800000000000004 -> \n", + "Function/total_mesh_disp/5 -> \n", + "Function/velocity -> \n", + "Function/velocity /0 -> \n", + "Function/velocity /0_02 -> \n", + "Function/velocity /0_040000000000000001 -> \n", + "Function/velocity /0_059999999999999998 -> \n", + "Function/velocity /0_080000000000000002 -> \n", + "Function/velocity /0_10000000000000001 -> \n", + "Function/velocity /0_12 -> \n", + "Function/velocity /0_14000000000000001 -> \n", + "Function/velocity /0_16 -> \n", + "Function/velocity /0_17999999999999999 -> \n", + "Function/velocity /0_20000000000000001 -> \n", + "Function/velocity /0_22 -> \n", + "Function/velocity /0_23999999999999999 -> \n", + "Function/velocity /0_26000000000000001 -> \n", + "Function/velocity /0_28000000000000003 -> \n", + "Function/velocity /0_29999999999999999 -> \n", + "Function/velocity /0_32000000000000001 -> \n", + "Function/velocity /0_34000000000000002 -> \n", + "Function/velocity /0_35999999999999999 -> \n", + "Function/velocity /0_38 -> \n", + "Function/velocity /0_40000000000000002 -> \n", + "Function/velocity /0_41999999999999998 -> \n", + "Function/velocity /0_44 -> \n", + "Function/velocity /0_46000000000000002 -> \n", + "Function/velocity /0_47999999999999998 -> \n", + "Function/velocity /0_5 -> \n", + "Function/velocity /0_52000000000000002 -> \n", + "Function/velocity /0_54000000000000004 -> \n", + "Function/velocity /0_56000000000000005 -> \n", + "Function/velocity /0_57999999999999996 -> \n", + "Function/velocity /0_59999999999999998 -> \n", + "Function/velocity /0_62 -> \n", + "Function/velocity /0_64000000000000001 -> \n", + "Function/velocity /0_66000000000000003 -> \n", + "Function/velocity /0_68000000000000005 -> \n", + "Function/velocity /0_70000000000000007 -> \n", + "Function/velocity /0_71999999999999997 -> \n", + "Function/velocity /0_73999999999999999 -> \n", + "Function/velocity /0_76000000000000001 -> \n", + "Function/velocity /0_78000000000000003 -> \n", + "Function/velocity /0_80000000000000004 -> \n", + "Function/velocity /0_82000000000000006 -> \n", + "Function/velocity /0_83999999999999997 -> \n", + "Function/velocity /0_85999999999999999 -> \n", + "Function/velocity /0_88 -> \n", + "Function/velocity /0_90000000000000002 -> \n", + "Function/velocity /0_92000000000000004 -> \n", + "Function/velocity /0_94000000000000006 -> \n", + "Function/velocity /0_95999999999999996 -> \n", + "Function/velocity /0_97999999999999998 -> \n", + "Function/velocity /1 -> \n", + "Function/velocity /1_02 -> \n", + "Function/velocity /1_04 -> \n", + "Function/velocity /1_0600000000000001 -> \n", + "Function/velocity /1_0800000000000001 -> \n", + "Function/velocity /1_1000000000000001 -> \n", + "Function/velocity /1_1200000000000001 -> \n", + "Function/velocity /1_1400000000000001 -> \n", + "Function/velocity /1_1599999999999999 -> \n", + "Function/velocity /1_1799999999999999 -> \n", + "Function/velocity /1_2 -> \n", + "Function/velocity /1_22 -> \n", + "Function/velocity /1_24 -> \n", + "Function/velocity /1_26 -> \n", + "Function/velocity /1_28 -> \n", + "Function/velocity /1_3 -> \n", + "Function/velocity /1_3200000000000001 -> \n", + "Function/velocity /1_3400000000000001 -> \n", + "Function/velocity /1_3600000000000001 -> \n", + "Function/velocity /1_3800000000000001 -> \n", + "Function/velocity /1_4000000000000001 -> \n", + "Function/velocity /1_4199999999999999 -> \n", + "Function/velocity /1_4399999999999999 -> \n", + "Function/velocity /1_46 -> \n", + "Function/velocity /1_48 -> \n", + "Function/velocity /1_5 -> \n", + "Function/velocity /1_52 -> \n", + "Function/velocity /1_54 -> \n", + "Function/velocity /1_5600000000000001 -> \n", + "Function/velocity /1_5800000000000001 -> \n", + "Function/velocity /1_6000000000000001 -> \n", + "Function/velocity /1_6200000000000001 -> \n", + "Function/velocity /1_6400000000000001 -> \n", + "Function/velocity /1_6600000000000001 -> \n", + "Function/velocity /1_6799999999999999 -> \n", + "Function/velocity /1_7 -> \n", + "Function/velocity /1_72 -> \n", + "Function/velocity /1_74 -> \n", + "Function/velocity /1_76 -> \n", + "Function/velocity /1_78 -> \n", + "Function/velocity /1_8 -> \n", + "Function/velocity /1_8200000000000001 -> \n", + "Function/velocity /1_8400000000000001 -> \n", + "Function/velocity /1_8600000000000001 -> \n", + "Function/velocity /1_8800000000000001 -> \n", + "Function/velocity /1_9000000000000001 -> \n", + "Function/velocity /1_9199999999999999 -> \n", + "Function/velocity /1_9399999999999999 -> \n", + "Function/velocity /1_96 -> \n", + "Function/velocity /1_98 -> \n", + "Function/velocity /2 -> \n", + "Function/velocity /2_02 -> \n", + "Function/velocity /2_04 -> \n", + "Function/velocity /2_0600000000000001 -> \n", + "Function/velocity /2_0800000000000001 -> \n", + "Function/velocity /2_1000000000000001 -> \n", + "Function/velocity /2_1200000000000001 -> \n", + "Function/velocity /2_1400000000000001 -> \n", + "Function/velocity /2_1600000000000001 -> \n", + "Function/velocity /2_1800000000000002 -> \n", + "Function/velocity /2_2000000000000002 -> \n", + "Function/velocity /2_2200000000000002 -> \n", + "Function/velocity /2_2400000000000002 -> \n", + "Function/velocity /2_2600000000000002 -> \n", + "Function/velocity /2_2800000000000002 -> \n", + "Function/velocity /2_3000000000000003 -> \n", + "Function/velocity /2_3199999999999998 -> \n", + "Function/velocity /2_3399999999999999 -> \n", + "Function/velocity /2_3599999999999999 -> \n", + "Function/velocity /2_3799999999999999 -> \n", + "Function/velocity /2_3999999999999999 -> \n", + "Function/velocity /2_4199999999999999 -> \n", + "Function/velocity /2_4399999999999999 -> \n", + "Function/velocity /2_46 -> \n", + "Function/velocity /2_48 -> \n", + "Function/velocity /2_5 -> \n", + "Function/velocity /2_52 -> \n", + "Function/velocity /2_54 -> \n", + "Function/velocity /2_5600000000000001 -> \n", + "Function/velocity /2_5800000000000001 -> \n", + "Function/velocity /2_6000000000000001 -> \n", + "Function/velocity /2_6200000000000001 -> \n", + "Function/velocity /2_6400000000000001 -> \n", + "Function/velocity /2_6600000000000001 -> \n", + "Function/velocity /2_6800000000000002 -> \n", + "Function/velocity /2_7000000000000002 -> \n", + "Function/velocity /2_7200000000000002 -> \n", + "Function/velocity /2_7400000000000002 -> \n", + "Function/velocity /2_7600000000000002 -> \n", + "Function/velocity /2_7800000000000002 -> \n", + "Function/velocity /2_8000000000000003 -> \n", + "Function/velocity /2_8199999999999998 -> \n", + "Function/velocity /2_8399999999999999 -> \n", + "Function/velocity /2_8599999999999999 -> \n", + "Function/velocity /2_8799999999999999 -> \n", + "Function/velocity /2_8999999999999999 -> \n", + "Function/velocity /2_9199999999999999 -> \n", + "Function/velocity /2_9399999999999999 -> \n", + "Function/velocity /2_96 -> \n", + "Function/velocity /2_98 -> \n", + "Function/velocity /3 -> \n", + "Function/velocity /3_02 -> \n", + "Function/velocity /3_04 -> \n", + "Function/velocity /3_0600000000000001 -> \n", + "Function/velocity /3_0800000000000001 -> \n", + "Function/velocity /3_1000000000000001 -> \n", + "Function/velocity /3_1200000000000001 -> \n", + "Function/velocity /3_1400000000000001 -> \n", + "Function/velocity /3_1600000000000001 -> \n", + "Function/velocity /3_1800000000000002 -> \n", + "Function/velocity /3_2000000000000002 -> \n", + "Function/velocity /3_2200000000000002 -> \n", + "Function/velocity /3_2400000000000002 -> \n", + "Function/velocity /3_2600000000000002 -> \n", + "Function/velocity /3_2800000000000002 -> \n", + "Function/velocity /3_3000000000000003 -> \n", + "Function/velocity /3_3200000000000003 -> \n", + "Function/velocity /3_3399999999999999 -> \n", + "Function/velocity /3_3599999999999999 -> \n", + "Function/velocity /3_3799999999999999 -> \n", + "Function/velocity /3_3999999999999999 -> \n", + "Function/velocity /3_4199999999999999 -> \n", + "Function/velocity /3_4399999999999999 -> \n", + "Function/velocity /3_46 -> \n", + "Function/velocity /3_48 -> \n", + "Function/velocity /3_5 -> \n", + "Function/velocity /3_52 -> \n", + "Function/velocity /3_54 -> \n", + "Function/velocity /3_5600000000000001 -> \n", + "Function/velocity /3_5800000000000001 -> \n", + "Function/velocity /3_6000000000000001 -> \n", + "Function/velocity /3_6200000000000001 -> \n", + "Function/velocity /3_6400000000000001 -> \n", + "Function/velocity /3_6600000000000001 -> \n", + "Function/velocity /3_6800000000000002 -> \n", + "Function/velocity /3_7000000000000002 -> \n", + "Function/velocity /3_7200000000000002 -> \n", + "Function/velocity /3_7400000000000002 -> \n", + "Function/velocity /3_7600000000000002 -> \n", + "Function/velocity /3_7800000000000002 -> \n", + "Function/velocity /3_8000000000000003 -> \n", + "Function/velocity /3_8200000000000003 -> \n", + "Function/velocity /3_8399999999999999 -> \n", + "Function/velocity /3_8599999999999999 -> \n", + "Function/velocity /3_8799999999999999 -> \n", + "Function/velocity /3_8999999999999999 -> \n", + "Function/velocity /3_9199999999999999 -> \n", + "Function/velocity /3_9399999999999999 -> \n", + "Function/velocity /3_96 -> \n", + "Function/velocity /3_98 -> \n", + "Function/velocity /4 -> \n", + "Function/velocity /4_0200000000000005 -> \n", + "Function/velocity /4_04 -> \n", + "Function/velocity /4_0600000000000005 -> \n", + "Function/velocity /4_0800000000000001 -> \n", + "Function/velocity /4_0999999999999996 -> \n", + "Function/velocity /4_1200000000000001 -> \n", + "Function/velocity /4_1399999999999997 -> \n", + "Function/velocity /4_1600000000000001 -> \n", + "Function/velocity /4_1799999999999997 -> \n", + "Function/velocity /4_2000000000000002 -> \n", + "Function/velocity /4_2199999999999998 -> \n", + "Function/velocity /4_2400000000000002 -> \n", + "Function/velocity /4_2599999999999998 -> \n", + "Function/velocity /4_2800000000000002 -> \n", + "Function/velocity /4_2999999999999998 -> \n", + "Function/velocity /4_3200000000000003 -> \n", + "Function/velocity /4_3399999999999999 -> \n", + "Function/velocity /4_3600000000000003 -> \n", + "Function/velocity /4_3799999999999999 -> \n", + "Function/velocity /4_4000000000000004 -> \n", + "Function/velocity /4_4199999999999999 -> \n", + "Function/velocity /4_4400000000000004 -> \n", + "Function/velocity /4_46 -> \n", + "Function/velocity /4_4800000000000004 -> \n", + "Function/velocity /4_5 -> \n", + "Function/velocity /4_5200000000000005 -> \n", + "Function/velocity /4_54 -> \n", + "Function/velocity /4_5600000000000005 -> \n", + "Function/velocity /4_5800000000000001 -> \n", + "Function/velocity /4_6000000000000005 -> \n", + "Function/velocity /4_6200000000000001 -> \n", + "Function/velocity /4_6399999999999997 -> \n", + "Function/velocity /4_6600000000000001 -> \n", + "Function/velocity /4_6799999999999997 -> \n", + "Function/velocity /4_7000000000000002 -> \n", + "Function/velocity /4_7199999999999998 -> \n", + "Function/velocity /4_7400000000000002 -> \n", + "Function/velocity /4_7599999999999998 -> \n", + "Function/velocity /4_7800000000000002 -> \n", + "Function/velocity /4_7999999999999998 -> \n", + "Function/velocity /4_8200000000000003 -> \n", + "Function/velocity /4_8399999999999999 -> \n", + "Function/velocity /4_8600000000000003 -> \n", + "Function/velocity /4_8799999999999999 -> \n", + "Function/velocity /4_9000000000000004 -> \n", + "Function/velocity /4_9199999999999999 -> \n", + "Function/velocity /4_9400000000000004 -> \n", + "Function/velocity /4_96 -> \n", + "Function/velocity /4_9800000000000004 -> \n", + "Function/velocity /5 -> \n", + "Mesh -> \n", + "Mesh/fluid_mesh.xdmf -> \n", + "Mesh/fluid_mesh.xdmf/geometry -> \n", + "Mesh/fluid_mesh.xdmf/topology -> \n", + "(538, 3) float64\n" + ] + } + ], + "source": [ + "# Open the file (read-only)\n", + "with h5py.File(fname, \"r\") as f:\n", + " # Explore the structure\n", + " print(\"Top-level keys:\", list(f.keys()))\n", + "\n", + " # print(f.visit(print))\n", + "\n", + " # Recursively print structure and types\n", + " # def show_structure(name, obj):\n", + " # print(f\"{name} -> {type(obj)}\")\n", + "\n", + " # f.visititems(show_structure)\n", + "\n", + " # Access a dataset\n", + " data = f[\"Function/velocity /0\"][:]\n", + " print(data.shape, data.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b5a9b154", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coords shape: (538, 2)\n", + "Combined velocity shape: (251, 538, 3)\n", + "Combined temperature shape: (251, 538, 1)\n" + ] + } + ], + "source": [ + "with h5py.File(fname, \"r\") as f:\n", + " # Mesh coordinates ===============\n", + " coords = f[\"Mesh/fluid_mesh.xdmf/geometry\"][:]\n", + " print(\"Coords shape:\", coords.shape) # Should be (538, 3) or (538, 2)\n", + "\n", + " # Velocity ===============\n", + " velocity_group = f[\"Function/velocity \"]\n", + "\n", + " # Sort dataset keys numerically\n", + " keys = sorted(velocity_group.keys(), key=lambda k: float(k.replace(\"_\", \".\")))\n", + "\n", + " # Load all time steps into list\n", + " data_list = [velocity_group[k][:] for k in keys]\n", + " data_array = np.stack(data_list) # Shape: (n_timesteps, n_points, 3)\n", + "\n", + " # Temperature ===============\n", + " temp_group = f[\"Function/temperature \"]\n", + "\n", + " # Sort dataset keys numerically\n", + " keys2 = sorted(velocity_group.keys(), key=lambda k: float(k.replace(\"_\", \".\")))\n", + "\n", + " # Load all time steps into list\n", + " data_list2 = [temp_group[k][:] for k in keys2]\n", + " temp_data_array = np.stack(data_list2) # Shape: (n_timesteps, n_points, 3)\n", + "\n", + "\n", + "print(\"Combined velocity shape:\", data_array.shape) # e.g. (100, 5000, 3)\n", + "print(\"Combined temperature shape:\", temp_data_array.shape) # e.g. (100, 5000, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5db717e2", + "metadata": {}, + "outputs": [], + "source": [ + "# Interpolate to regular grid\n", + "data = {}\n", + "\n", + "nt, npoints, ncomp = data_array.shape\n", + "\n", + "nx = 240 #120\n", + "ny = 40 #20\n", + "\n", + "# Create regular grid to interpolate onto\n", + "xi = np.linspace(coords[:, 0].min(), coords[:, 0].max(), nx)\n", + "yi = np.linspace(coords[:, 1].min(), coords[:, 1].max(), ny)\n", + "X, Y = np.meshgrid(xi, yi, indexing='ij')\n", + "\n", + "data['u'] = np.empty((nt, nx, ny))\n", + "data['v'] = np.empty((nt, nx, ny))\n", + "data['T'] = np.empty((nt, nx, ny))\n", + "\n", + "# Interpolate\n", + "for t in range(nt):\n", + " data['u'][t, :, :] = griddata(coords, data_array[t, :, 0], (X, Y), method='linear')\n", + " data['v'][t, :, :] = griddata(coords, data_array[t, :, 1], (X, Y), method='linear')\n", + " data['T'][t, :, :] = griddata(coords, temp_data_array[t, :, 0], (X, Y), method='linear')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5e30308e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot with pcolormesh or imshow\n", + "t = -1 #200\n", + "\n", + "# fig, axs = plt.subplots(2, 1, figsize=[8,6], sharex=True, dpi=300)\n", + "fig, axs = plt.subplots(3, 1, figsize=[8,9], sharex=True, dpi=300)\n", + "\n", + "fig.suptitle(\"Interpolated Velocity (nt = {})\".format(t))\n", + "\n", + "for comp,ax in zip(data.keys(), axs.ravel()):\n", + " pcm = ax.pcolormesh(X, Y, data[comp][t, :, :], shading='auto', cmap='viridis')\n", + " plt.colorbar(pcm, ax=ax, label=comp)\n", + " ax.set_ylabel(\"y\")\n", + "axs[-1].set_xlabel(\"x\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4fcb3d85", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot vertical profiles\n", + "fig, axs = plt.subplots(1, 3, figsize=[8,4], dpi=300)\n", + "\n", + "fig.suptitle(\"Mean Velocity Profiles\")\n", + "\n", + "for comp,ax in zip(data.keys(), axs.ravel()):\n", + " ax.plot(np.average(np.average(data[comp],axis=0),axis=0), yi, 'k.-')\n", + " ax.set_xlabel(\"{}\".format(comp))\n", + " ax.grid()\n", + "axs[0].set_ylabel(\"y [m]\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5eda4689", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute speed (vector magnitude) at point 100 across time\n", + "point_index = 100\n", + "speed = np.linalg.norm(data_array[:, point_index, :], axis=1)\n", + "\n", + "plt.plot(speed)\n", + "plt.title(f\"Velocity magnitude over time at point #{point_index}\")\n", + "plt.xlabel(\"Time step\")\n", + "plt.ylabel(\"Speed\")\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b5eecf00", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use a time step\n", + "t = 50\n", + "v = data_array[t]\n", + "speed = np.linalg.norm(v, axis=1)\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.scatter(coords[:, 0], coords[:, 1], c=speed, cmap=\"viridis\", s=20)\n", + "# plt.scatter(coords[:, 0], coords[:, 1], c=v[:,0], cmap=\"viridis\", s=20)\n", + "plt.colorbar(label=\"Velocity magnitude\")\n", + "plt.title(f\"Velocity field at time step {t}\")\n", + "plt.xlabel(\"x\")\n", + "plt.ylabel(\"y\")\n", + "plt.axis(\"equal\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pvade-pvlib", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/input/empty_domain.yaml b/input/empty_domain.yaml new file mode 100644 index 00000000..4f2e9517 --- /dev/null +++ b/input/empty_domain.yaml @@ -0,0 +1,69 @@ +general: + geometry_module: panels2d + output_dir: output/empty2d + mesh_only: false + structural_analysis: false + fluid_analysis: true + thermal_analysis: true +domain: + x_min: -20.0 + x_max: 100.0 + y_min: 0.0 + y_max: 20.0 + # y_min: -30.0 + # y_max: 30.0 + # z_min: 0.0 + # z_max: 20.0 + l_char: 1.25 +pv_array: + stream_rows: 0 + span_rows: 0 + stream_spacing: 10.0 + span_spacing: 30.0 + panel_chord: 4.1 + panel_span: 24.25 + panel_thickness: 0.1 + elevation: 2.1 + tracker_angle: 0.0 + span_fixation_pts: [13.2] +solver: + dt: 0.01 + t_final: 20.0 + solver1_ksp: gmres + solver2_ksp: gmres + solver3_ksp: gmres + solver5_ksp: gmres + solver1_pc: hypre + solver2_pc: hypre + solver3_pc: hypre + solver5_pc: hypre + save_text_interval: 0.02 + save_xdmf_interval: 0.02 +fluid: + time_varying_inflow_window: 0.0 + velocity_profile_type: loglaw + u_ref: 8.0 + rho: 1.0 + nu: 1.8e-05 + beta: 0.00333 + alpha: 0.00225 #2.25e-05 # high Pe # 0.00225 # low Pe (no stability needed) # + turbulence_model: smagorinsky + bc_y_max: slip + bc_y_min: slip + bc_z_max: slip + bc_z_min: noslip + wind_direction: 270.0 + T_ambient: 300.0 + T_bottom: 320.0 +# structure: +# dt : 0.01 +# rho: 124.0 +# poissons_ratio: 0.3 +# elasticity_modulus: 4.0e+09 +# body_force_x: 0.0 +# body_force_y: 0.0 +# body_force_z: 0.0 +# bc_list: [] +# motor_connection: true +# tube_connection: true +# beta_relaxation: 0.5 \ No newline at end of file diff --git a/pvade/IO/input_schema.yaml b/pvade/IO/input_schema.yaml index 0182a442..e74c5ea8 100644 --- a/pvade/IO/input_schema.yaml +++ b/pvade/IO/input_schema.yaml @@ -20,6 +20,12 @@ properties: default: "panels3d" type: "string" description: "The name of the directory in pvade/geometry/ that contains a DomainCreation.py file for generating the CAD description, prescribing mesh refinement, etc." + enum: + - "panels2d" + - "panels3d" + - "heliostats3d" + - "flag2d" + - "cylinder3d" input_mesh_dir: default: null type: @@ -119,14 +125,14 @@ properties: properties: stream_rows: default: 3 - minimum: 1 + minimum: 0 maximum: 10 type: "integer" description: "The number of panel rows in the streamwise direction." units: "unitless" span_rows: default: 1 - minimum: 1 + minimum: 0 maximum: 10 type: "integer" description: "The number of panel rows in the spanwise direction." diff --git a/pvade/fluid/boundary_conditions.py b/pvade/fluid/boundary_conditions.py index 1d82be98..90d59973 100644 --- a/pvade/fluid/boundary_conditions.py +++ b/pvade/fluid/boundary_conditions.py @@ -310,7 +310,7 @@ def build_velocity_boundary_conditions(domain, params, functionspace, current_ti ) if domain.rank == 0: - print("inflow_function = ", inflow_function.x.array[:]) + print("inflow_function = ", inflow_function.x.array[:10]) dofs = get_facet_dofs_by_gmsh_tag(domain, functionspace, "x_min") bcu.append(dolfinx.fem.dirichletbc(inflow_function, dofs)) @@ -432,7 +432,7 @@ def __call__(self, x): # only applying Tbottom to cells from x=0 to x=0.75*x_max to avoid jet at the outlet sfc due to pressure BCa at exit heated_cells = dolfinx.mesh.locate_entities( - domain.fluid.msh, ndim, lambda x: x[0] < (0.375 * params.domain.x_max) + domain.fluid.msh, ndim, lambda x: x[0] < (0.75 * params.domain.x_max) ) T_bottom_function = dolfinx.fem.Function(functionspace) diff --git a/pvade/geometry/MeshManager.py b/pvade/geometry/MeshManager.py index be30aab3..d0a074e2 100644 --- a/pvade/geometry/MeshManager.py +++ b/pvade/geometry/MeshManager.py @@ -63,6 +63,8 @@ def _get_domain_markers(self, params): # Cell Markers self.domain_markers["fluid"] = {"idx": 8, "entity": "cell", "gmsh_tags": []} self.domain_markers["structure"] = {"idx": 9, "entity": "cell", "gmsh_tags": []} + print('*** structure added to domain_markers') + # print('self.structure = ', self.structure) # Structure Facet Markers if ( @@ -161,6 +163,7 @@ def build(self, params): ): self.geometry.build_structure(params) else: + # TODO: add special treatment for panels2d when structural_analysis == False self.geometry.build_FSI(params) # Build the domain markers for each surface and cell if hasattr(self.geometry, "domain_markers"): @@ -329,6 +332,7 @@ def _create_submeshes_from_parent(self, params): submesh_list.append("fluid") if params.general.structural_analysis == True: submesh_list.append("structure") + print('*** submesh_list = ', submesh_list) for sub_domain_name in submesh_list: if self.rank == 0: diff --git a/pvade/structure/StructureMain.py b/pvade/structure/StructureMain.py index 68fd33f2..e04c15fc 100644 --- a/pvade/structure/StructureMain.py +++ b/pvade/structure/StructureMain.py @@ -80,9 +80,16 @@ def __init__(self, domain, params): # find hmin in mesh num_cells = domain.structure.msh.topology.index_map(self.ndim).size_local h = dolfinx.cpp.mesh.h(domain.structure.msh, self.ndim, range(num_cells)) + # print('h = ', h) + # print('len(h) = ', len(h)) # This value of hmin is local to the mesh portion owned by the process hmin_local = np.amin(h) + # if len(h) > 0: + # hmin_local = [np.amin(h)] + # else: + # hmin_local = [5.0] # what value should this be? + # print('hmin_local = ', hmin_local) # collect the minimum hmin from all processes self.hmin = np.zeros(1) diff --git a/pvade_main.py b/pvade_main.py index 1c2e7689..8d3938e4 100644 --- a/pvade_main.py +++ b/pvade_main.py @@ -138,7 +138,7 @@ def main(input_file=None): print(f"| f_x (drag) = {fx:.4f}") print(f"| f_y (lift) = {fy:.4f}") - else: + elif params.pv_array.num_panels > 1: #excludes case when num_panels == 0 # still print info, but just for first row fx = flow.integrated_force_x[0] fy = flow.integrated_force_y[0]