From 67f3964f799be1b3017b6a61aca33b452a0c3a75 Mon Sep 17 00:00:00 2001 From: "Nagrani, Pranay Praveen" Date: Mon, 10 Apr 2023 11:06:34 -0400 Subject: [PATCH] Add files via upload --- .../rheology_TIMs-NEVP-DOWTC5550-Final.ipynb | 3599 +++++++++++++++++ 1 file changed, 3599 insertions(+) create mode 100644 DOWSIL TC-5550/rheology_TIMs-NEVP-DOWTC5550-Final.ipynb diff --git a/DOWSIL TC-5550/rheology_TIMs-NEVP-DOWTC5550-Final.ipynb b/DOWSIL TC-5550/rheology_TIMs-NEVP-DOWTC5550-Final.ipynb new file mode 100644 index 0000000..2f4fe83 --- /dev/null +++ b/DOWSIL TC-5550/rheology_TIMs-NEVP-DOWTC5550-Final.ipynb @@ -0,0 +1,3599 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "2f3d6c3f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Deepxde backend not selected or invalid. Assuming tensorflow.compat.v1 for now.\n", + "Using backend: tensorflow.compat.v1\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting the default backend to \"tensorflow.compat.v1\". You can change it in the ~/.deepxde/config.json file or export the DDEBACKEND environment variable. Valid options are: tensorflow.compat.v1, tensorflow, pytorch (all lowercase)\n", + "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\compat\\v2_compat.py:111: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n", + "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\deepxde\\nn\\initializers.py:116: The name tf.keras.initializers.he_normal is deprecated. Please use tf.compat.v1.keras.initializers.he_normal instead.\n", + "\n", + "2.7.0\n" + ] + } + ], + "source": [ + "# Loading all Modules #\n", + "import numpy as np\n", + "import deepxde as dde\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from deepxde.backend import tf\n", + "from scipy import interpolate\n", + "print(tf.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "bb66e151", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Setting non-dimensional parameters based on experimental data #\n", + "\n", + "del_stress = 40.0-9.0 #70-10\n", + "stress_min = 9.0 \n", + "del_time = 9.858-0.031\n", + "time_min = 0.031\n", + "del_shear_rate = 0.102-0.05 #0.202 \n", + "shear_min = 0.05\n", + "\n", + "# Generating training data by reading excel files #\n", + "\n", + "def gen_training_data():\n", + " \n", + " time_data = []\n", + " gammadot_data = []\n", + " shear_stress_data = []\n", + " \n", + " for itr in range(0,5): \n", + " if itr==2: # skipping shear rate of 0.08/s from giving as training data so the prediction can be checked later\n", + " continue\n", + " t_expt = []\n", + " t_expt_first = []\n", + " t_expt_last = []\n", + " gammadot = []\n", + " gammadot_first = []\n", + " gammadot_last = []\n", + " shear_stress = []\n", + " shear_stress_first = []\n", + " shear_stress_last = []\n", + " t_fitted = []\n", + " gammadot_fitted = []\n", + " shear_stress_fitted = []\n", + " \n", + " startUpFlow = pd.read_excel('DOW1_030323.xlsx',header = None, names=['shear rate','t','shear stress'], sheet_name=itr, skiprows=range(155,298)) \n", + " # Reading data from sheet number - itr\n", + " t_expt = np.array(startUpFlow[\"t\"])\n", + " t_expt_first = t_expt[0:54]\n", + " t_expt_last = t_expt[54:]\n", + " print(t_expt_first)\n", + " print(t_expt_last)\n", + " \n", + " gammadot = np.array(startUpFlow[\"shear rate\"])\n", + " gammadot_first = gammadot[0:54]\n", + " gammadot_last = gammadot[54:]\n", + " \n", + " shear_stress = np.array(startUpFlow[\"shear stress\"])\n", + " shear_stress_first = shear_stress[0:54]\n", + " shear_stress_last = shear_stress[54:]\n", + " \n", + " # interpolating shear stress and shear rate separately with time (increasing data points)\n", + " f_shear_stress = interpolate.interp1d(t_expt_first, shear_stress_first) \n", + " f_gammadot = interpolate.interp1d(t_expt_first, gammadot_first)\n", + " t_fitted = np.linspace(t_expt_first[0],t_expt_first[t_expt_first.size-1],50) #150 \n", + " gammadot_fitted = f_gammadot(t_fitted)\n", + " shear_stress_fitted = f_shear_stress(t_fitted) \n", + " \n", + " f_shear_stress_1 = interpolate.interp1d(t_expt_last, shear_stress_last) \n", + " f_gammadot_1 = interpolate.interp1d(t_expt_last, gammadot_last)\n", + " t_fitted_1 = np.linspace(t_expt_last[0],t_expt_last[t_expt_last.size-1], 50) #100\n", + " gammadot_fitted_1 = f_gammadot_1(t_fitted_1)\n", + " shear_stress_fitted_1 = f_shear_stress_1(t_fitted_1)\n", + " \n", + " time_itr = []\n", + " gammadot_itr = []\n", + " shear_stress_itr = []\n", + " \n", + " time_itr = np.append(t_fitted,t_fitted_1)\n", + " time_itr = time_itr[:,np.newaxis]\n", + " \n", + " gammadot_itr = np.append(gammadot_fitted,gammadot_fitted_1)\n", + " gammadot_itr = gammadot_itr[:,np.newaxis] \n", + " \n", + " shear_stress_itr = np.append(shear_stress_fitted,shear_stress_fitted_1)\n", + " shear_stress_itr = shear_stress_itr[:,np.newaxis]\n", + " \n", + " plt.plot(time_itr,shear_stress_itr, '-r', label='Fit')\n", + " plt.plot(t_expt,shear_stress,'*b', label='Experiment')\n", + " plt.legend()\n", + " plt.xscale('log')\n", + "\n", + " plt.xlabel('time (s)')\n", + " plt.ylabel('Shear stress (Pa)')\n", + " plt.show()\n", + " \n", + " # Non-Dimensionalizing - O(0-1)\n", + " time_itr = (t_expt-time_min)/(del_time) \n", + " gammadot_itr = (gammadot-shear_min)/(del_shear_rate) \n", + " shear_stress_itr = (shear_stress-stress_min)/(del_stress) \n", + " \n", + " # Appending data from each sheet to a global array and converting to a column\n", + " \n", + " time_data = np.append(time_data,time_itr)\n", + " time_data = time_data[:,np.newaxis]\n", + " \n", + " gammadot_data = np.append(gammadot_data,gammadot_itr)\n", + " gammadot_data = gammadot_data[:,np.newaxis] \n", + " \n", + " shear_stress_data = np.append(shear_stress_data,shear_stress_itr)\n", + " shear_stress_data = shear_stress_data[:,np.newaxis]\n", + " \n", + " print(np.size(gammadot_data))\n", + " print(np.size(time_data)) \n", + " print(np.size(shear_stress_data))\n", + " \n", + " \n", + " return np.hstack((gammadot_data, time_data)) , shear_stress_data \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "id": "0eafd147", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.031 0.042 0.052 0.063 0.075 0.086 0.098 0.11 0.122 0.134 0.147 0.159\n", + " 0.173 0.186 0.199 0.213 0.227 0.242 0.256 0.271 0.286 0.302 0.318 0.334\n", + " 0.35 0.367 0.384 0.402 0.419 0.437 0.456 0.474 0.494 0.513 0.533 0.553\n", + " 0.574 0.595 0.616 0.638 0.661 0.683 0.706 0.73 0.754 0.779 0.804 0.829\n", + " 0.855 0.881 0.908 0.936 0.964 0.992]\n", + "[1.022 1.051 1.081 1.112 1.144 1.176 1.208 1.242 1.275 1.31 1.345 1.381\n", + " 1.418 1.455 1.493 1.532 1.571 1.611 1.653 1.694 1.737 1.78 1.825 1.87\n", + " 1.916 1.963 2.011 2.059 2.109 2.159 2.211 2.264 2.317 2.372 2.427 2.484\n", + " 2.542 2.601 2.661 2.722 2.785 2.848 2.913 2.979 3.047 3.115 3.185 3.257\n", + " 3.329 3.404 3.479 3.556 3.635 3.715 3.796 3.879 3.964 4.05 4.138 4.228\n", + " 4.319 4.412 4.507 4.604 4.703 4.803 4.906 5.01 5.117 5.225 5.336 5.449\n", + " 5.564 5.681 5.8 5.922 6.046 6.173 6.301 6.433 6.567 6.703 6.842 6.984\n", + " 7.128 7.276 7.426 7.579 7.735 7.894 8.056 8.221 8.389 8.561 8.736 8.914\n", + " 9.096 9.281 9.469 9.662 9.858]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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IXAGcAXTy230Q+BT4B/CCqu6NdpCRsnIfJqApU+D222HPHvfpXkqV1OSDFGhKWJfxeODMM20dBlP/VFXuo8o7C2+S+AtwCvAlMBf4L5AH5AMtga7AIOA+4D4R+TMwVVUjXHbFmGpSdX/e+/RuB5yBvWwZHHVUxUQB5G4T+h51gCNWvcm7nMkB0khLgzPOAC0u5t23izlwqEnQ9StsZrNpCEI1Q30E/A2YqKorqzpQRDzA+cBNuCI0OVGJ0JhQpk6FP/0JPvgAjjsOqFj9tazw3fLlrn/CT04OfLr6cHo1HUDBfk9Z01K7dqCaTEFxsvUrmAYvVLLorqphDcLz3km8DLwsIu1qHJkx4Xr/fde0dMYZpO7dRkFh+biN6dPdw5Oi5BducpXxvCpWahW+2Z8JuKG42dnlfQiR9IsYU19VmSzCTRQBzttevXCMiZAqfPIJjBwJK1awttUJ3Hj8R7w2r2nFpU/P/AAuAQYMKDt17drIlkm15ibTkNnQWVO3rV7t6muMHQvvvkvGT6tJ//ANCgq0YtPRusXu+P79y06N5jKpxtR3ESULERklInNE5BsRWev3+D5WQRoTlKvPDYMGuUTw5pts35dKdstXWTI/v3w+xfLlATu3bYU3Y8IT9jwLETkLNxrqfaAX8C8gDTgJ2AAsjEWAxlRp6VJo1syt/gNw8snMfm0unH8xPDaOac895+qAdF4OJ59c6XRbr8GY8ERyZ3EHMA04y/v97ao6DDgGSAbeiW5opiELu5rsJ5+4Tuvk5PJt557rhkG98AI89hjs2OGmXfv0VxhjIhNJsuiFu7MowdVCbgSgqt8Bd+GSiTFR4Tv0NaiCAlixwjVB+Zs0ySWNG24gd8ossviQbZmDYxWuMfVeJMmiBCjyFpvKAzr77NsKdI9mYKZhiqia7IoVrsDSCScAfncjSUnw3HPQtSs5f0135TretjsLY6orkmSxCsj0fr0MuF5EMkSkDfB7YH10QzMNkf/iPlUW6fPt3Kby3Uhq+8OQ1d8xnWvcokV/T6myjLkxJrhIksULwNHer+/E9VVsBrYBpwJ/DHUB72iqD0Rkm4gUishmEXlFRHr7HddCRGaKyE4R2S8i74vIsRHEauqoiIazLl0KHTqQ2qNDwLsRVW/iSSkGbAU5Y2oi7GShqtNU9Sbv18uBY4Grgf8F+qnqP8O4TEtgOXAtcDpwKy7pLBGRLgAiIsAbuKKFvwX+B2gMzBeRjuHGa+qusIezLl0KgwYFvRtZv96beA5ZuQ5jairk0FkR6QVkAz2AH4BXVHWuqm4GZkbyYqr6D1x1Wt/rfwJ8C/wcmAqcBwwFTlXV+d5jFgPrcHWnrovkNU3d4Fv4L6zhrHl5rs3q6qurvBuJtIy5MSawUFVnT8AVE0zBdWq3BMaJyNWqGlGiqMIu7/Mh7/N5wNbSRAGgqntEZC6uUKEli3oo545CFi1ozOST/83jX57sPvWr8umn7tnbXxEsKdg8CmOiI9Sdxd3AGuAcVd0gIocBzwB/IsK7Cl8ikoybm9EFV9p8G/CSd/cxwFcBTvsa+JWINFPVn6r72iaxlBfzc2tJTF8zkump3sJ/BRL8xKVL3Ygn79wJSwrGxFaoPou+QI6qbgD3Fz5u5FMbETmiBq+7FCgEvgN+hmty2uHd1xLX3OVvt/e5RaALishVIrJMRJbl5eXVIDRTm9auhXEDV5HGfgDSmhQxXl5kXeZw2Lw5+IlLl8Ixx7jZ28aYmAuVLNpTeUjsekCAmpQh/yUwGBgH7AXmiUimd5/gJv35q+LPTFDVGao6UFUHtmnTpgahmdqUsXU56Z99SAGpeDxKQVEj0s85hfZbP4MTT4Rvvql8Umml2UCT8YwxMRHOaKjQ665GSFVXqupSb4f3CKAZcIt3927c3YW/0juKQHcdpo7JzYWsk4rYNnoi21M6k33ZQZYsETf6qVFHWLDATbgbOtR97au00qwlC2NqTTiFBGeIyL4A2/8uIr59B6qqWZEGoKo/isga3GgrcH0Tpwc4tDew0for6oecycqij5OYLJcx+z+HwxDXoV3e39APPv4YRo1y07LHj3er4WVmkvvuF1zMh7zcvS02CtaY2hHqzmIB8CNQ7Pf4CNjjt62kOgF4V9XrBZSWOH8D6CAiWT7HpAPneveZOAq7wF8QZeU8nhBKSGK6ZiMnDgk8q7prVzfq6ZZb4P/+D3r2hBtuIOfRw135jpd71uhnMcaET1ypp1p6MZE5wGfAF7i+iqNwk/raAyeo6ncikgQsAjoBf8A1O92K6wjvq6qbQr3OwIEDddmyZbH5IRq4a66BJ5+Eq6/2Wds6Arm5cOOvd/Lau6kcoClpacro0RJ0dboymzeTmtmWguImlXZ5PJCfH3ksxpiKRGS5qg4MtK+2V8pbAlyAG377FnAD7i6ln7d6LapaApwDzAMeB+bg7lyGh5MoTGxEVOAvgNI7EhFI/+8CCvDgSVEKCiTkrOrcXMga35Ely5sw7qwfSUsuBKx8hzG1qcpkISIZ1bmoiAT81VfV+1V1gKoerqppqtpTVa9W1fV+x+1W1ctUtaX3uBGq+nl1YjHREVGBvwDKivz9fg/bdyjZQ75gyVIJa3W60nOffBLSOx9OgaZY+Q5jalmoDu41IjITmK6q31Z1oIikAqNxJTn+iZu4Z+qJ6q5XXT7pzpn+4mHA/+D5TJnWt+oJdJXOne6ek5Nd3Sgr32FM7QnVDHUK0Af4WkT+KyJ/FZHLReQ8bwXZi0Vkkoi8BmzHNRu9AvwltmGbeKjOetWV7kgkn/Ft57FufZXTZgKf672b2bwZ+noTje/MbWNM7FR5Z+GtLjtCRI4DrsT1JVzrd1gBbkb2TcALqhpomK2pB6pTUqPCHUmTEgoONiG91xFhNR1V927GGBN94cyzQFU/AyYCiEhb4AjAgysCuF5VD1Vxumngyor87XuIGc+nkdv88sjPtaqxxsRVrQ6drS02dDYBFRdD584wcCC8/nq8ozHGBJBIQ2dNQ7VwIWzd6johjDF1jiULUzteeAGaNoVzz413JMaYarBkYWKvsBD++U8YPbp8aJMxpk6xZGFi71//gh9/dONejTF1kiULE3svvABt2sCIEfGOxBhTTWEnCxFJEpFGfttGicjvRaR/9EMz9cLevTB3LowdC40bxzsaY0w1hTXPwusfuKVQfwUgItm4GdsAh0TkbFV9P8rxmbrutdfcbDprgjKmToukGWow8LbP938AZgKHAbOB26IYl6kvXnwRMjNh8OB4R2KMqYFIkkVbYAuAiPQAugKPect7PAUcG/3wTELbtQu+/z74/u3bYd48N7dCQteCMsYkrkiSxV6glffrYcBOVf3C+30xrvyHaSgWLIAjj4QePaBfP7jvvsr1yl95xS1+YU1QxtR5kSSLj4FbROQc4HoqNkn1ADZHMS6TyF58EUaOhHbt4IEH3NyJW2+Fbt1g0CB48EFXGvbFF1152N694x2xMaaGIkkWNwEtcetge4C7fPZdBCyOXlgmIanClCnuTmHIEPj4Y7jxRve8fj38+c9QVAS//z106uRqmVt5D2PqhYgLCYpIK1Xd5bftWGCbquZFM7jqskKCMXDokCv/OmuWSxZ//zukpAQ+dvVqcme+xcV/P42XP2xP+z6tazdWY0y1RLWQoG+iEJGWIjIA+C5REoWJntJ1s7d9txfOPtslittvh+eeC54oAI48kpx917Pohz5MftwShTH1QSST8m4XkXt9vj8FWA98AqwWkSOjH56Jp5wcWLRQmTzkHZg/391N5ORUObIpNdXtnj7d9W1Pn+6+T02txcCNMVEXyZ3FJcBan+//DHwOXIBbUjUnemGZeKrwga/C9N0XIUWHSP3NZSHPDbYUqv9AKWNM3RJJsugArAYQkTbA8cAdqjoXuA84OfrhmXhYuxbGjcwjjQMApHlKwv7At6VQjamfIkkWxUAT79en4Nbe/o/3+zzcSClTD2S0LSZ9+XwKSMGTohQcTKryA7+sb2Ob+750KdQlS9xz6XZjTN0VSW2or4BLRORj4DLgI5+1tzsBO6IdnImT555j++7mZI9cy1UPHBly7eucHFi0CCZPhscfh9mzy/dNmxb7cI0xsRf20FkRGQW8DjQGDgGjVPUj774XgDRVHR2rQCNhQ2drYP9+OOoo6NjR3RqE6MwuKKi83eOB/PwYxmiMiYmoDJ1V1XeBo4GxwDGlicJrAXB/jaI0ieHBB91a2VOnhqznZJ3ZxjQckTRDoarrgEofBar6ZNQiMvGzbRvcfz+MGQNDh4Y83DqzjWk4IpqUJyIdRORBEVkmImtFpI93+/UiMig2IZpa88c/uvWy77sv7FOsM9uYhiHsOwsROQZYiBsVtRjoT/noqC7ACUCVhYBE5OfAL4CBuJLnG3FrYdzjLXWOiGQS4O7Fq4Wq/hhuzCYCX33lJt399reummyYrDPbmIYhkmaoqcBKYBRu2OxBn30fE16fxY24BDEJV6W2P64g4XAROVFVS3yOvRdXtNDXvgjiNZG46SZo3hzuuCPekRhjElAkyWIo8AtV/UlEkv32bQfCaak+16+G1Ecisht4BrdGxgc++9aq6pII4jPVNW8evPOOKzfeqlXo440xDU4kfRYlVexrDYQcLBmk2OCn3ucOEcRioqW42JUZz8x0TVDGGBNAJMniE+DXQfaNpXw2d6SyvM8r/bbfKyJFIrJHRN7wlkE30fbss/DFF65Tu6pKssaYBi2SZqgc4H0ReQ94EVDgNBH5HTAaVwIkIiLSAZgMvK+qpbPoCoEngfdwZUR64fo4PhaRE1TVP6mY6tq/n9xbHuHi5st5+ZT+YbUjGmMapkgm5X2EqzDbFZgFCOUFBC9Q1aWRvLCINMPNCC/C545FVXNVNVtVZ6vqQlX9Gy4RKXBbFde7yjukd1leni2tEZapU8nZcRWLfurP5JyqJ+AZYxq2sMp9eDu0+wBbVTVPRHrghr7uUtVVEb+oiAe3hnc/IEtVvwzjnLeB7qraM9SxVu4jtFSPUlBYOUFYqQ5jGq5olPtQYBluqCuqukZVP65momgM/B9uXsZZ4SSK0lO9cZgoWPs/f2Cc/IM0jxu3YKU6jDFVCStZeOc/bAKa1uTFRCQJeAEYAZwf7tBYEekMnARE1NRlgvjySzJeeoj0Pp0pOJhkpTqMMSFF0sH9JHC9iLylqgdDHh3YNOBCYAqwX0QG++zbrKqbRWQqLoktxnVw9wRuxQ3dvaear2t83XQTpKezvfPxZJ8MV11FyDLkxpiGLZJk0RzoDqwVkX8BuVRsFlJVvTPENc70Pt9G5c7qu3Gzub8GJgITvK+5EzdZ7+7qNHsZP++9B//6F0ydyuwbmpRttlIdxpiqRLKeRVWT8sAlC/+Z3XFhHdyV5ebCxWOLeXnHqbQ/tAlWrrR5FcaYCqK1nkVSiEdCJIqGyn9pU385k5VFi4TJ310E06dbojDGRCTsZCEinb0jmQLta+TthDZx4ru0qa/UVLeG0fQnhBKSmM41yBmjSE2NT5zGmLopknIf6/AOnQ2gL8HLipsYKksG06GkxD2LUJYM1q6FcWfsJo0DAKSlqg2RNcZELJJkUdUU38ZUXWjQxEiopU0zPD+Q/p+3KSAFT4qbiGdDZI0xkaoyWYjI4SLSTUS6eTd1KP3e53EMcClga6TFQZVLm5aUwKWXsv2npmSPyWPJUrHV7Iwx1RJq6OzvgDtxQ2QV+GeQ48R7nImD0qVNK82XeOABmDuX2Y+cBteNBmyIrDGmeqocOisifXH1mwRXPPBPwPd+hxUC36jqFzGKMWI2dBb48EMYMQJ+/nN46SXXkWGMMVWoauhslXcWqvo58Ln3Igq8pao7ox+iiarcXLj4YreW9syZliiMMTUWyQzu5/Dr4xCRUbhqtB+o6n+jGZippkOH4KKLYN8++Pe/3braxhhTQ5Eki3/gmpx+BSAi2cDj3n2HRORsVX0/yvGZSN12GyxcCM8/D8ccE+9ojDH1RCRDZwfj1qAo9QdgJnAYMJsqFiYyteS111yn9sSJbvysMcZESSTJoi2wBcC7+FFX4DFV3Qc8Bdga2fG0Zg1ceikMHAgPPRTvaIwx9UwkyWIv0Mr79TBgp88IqGLAE8W4TCTy892op+RkePVVq/tkjIm6SPosPgZuEZEi4HoqNkn1ADZHMS4TiWuvhc8/h7fegszMeEdjjKmHIrmzuAloCbyBu4u4y2ffRbjFikxtmzXLPW6/Hc46K97RGGPqqbDvLFR1NXCUiLRS1V1+u3+HlfuofStWwG9+4ybf3XVXvKMxxtRjkTRDARAgUaCqX0YnHBO2gwfdfIpWreDFF11/hTHGxEjEycIkiBkz4Lvv4M03oW3beEdjjKnnIumzMIli7164+24YPtz6KYwxtcKSRV305z/Dzp3u2eo+GWNqgSWLumbLFnjwQfjFL9wEPGOMqQVhJQsRaSIin4nI6bEOyIRw551QVARTpsQ7EmNMAxJWslDVg7jyHkWxDcdU6auv4Kmn3CS8rl3jHY0xpgGJpBlqHmB3FvF0yy2u5PhtVrPRGFO7Ihk6+yjwvIg0Al4DcnFLrZZR1bXRC81UMH++K+dx//1uboUxxtSiKpdVrXCgSInPtwFPUtWEmBlW75ZVLSmBQYPcYturVkFqarwjMsbUQ9VeVtXPr6MUj4nUK6/AsmXwzDOWKIwxcRFJbahnYhmICaKwECZNgr59bUEjY0zcWLmPRDd9OqxbB+++a/WfjDFxE1GyEJG2wC+AnlRe7EhV9fIQ5//ce/5A3Mp7G3FLst7jXXGv9LgWwAPABUAqrvz5/za4goU//gg5OTByJJxuA9GMMfETdrIQkZ7AEiAZaArsxK1vkQz8AOwJ4zI34hLEJNxiSf1x62IMF5ETVbVERAS3ZkZX4Lfea98KzBeRfqracBZZuu8++OEHNwLKGGPiKJI7iweAT3B/7e8HzgS+AH4F3A2MDuMa56pqns/3H4nIbuAZ3FKtHwDnAUOBU1V1PoCILAbW4RZgui6CmOuujRvh4Yfhkkugf/94R2OMaeAimZR3PPA4UFh6rqoWqeos3ByMh0NdwC9RlPrU+9zB+3wesLU0UXjP2wPMBc6PIN667Y9/dM85OfGNwxhjiCxZNAN2q2oJrsmptc++ZbhkUh1Z3ueV3udjgK8CHPc10FlEmlXzdeqOzz+HZ5+F666DLl3iHY0xxkSULNYD7b1frwIu9Nl3DvBjpC8uIh2AycD7qlo6i64lrp/C327vc4sg17pKRJaJyLK8vEA3MHXIzTfD4YfDrbfGOxJjjAEirw010vv1g8CvRWSViHyNW4N7ViQv7L1DeB1XnNB3wp8QeIZ4lQs3qOoMVR2oqgPbtGkTSSiJZd48N0z29tuhhcuLubmQlQXbbJVzY0ycRJIsbsWNZkJVX8H1H3yKu8uYCNwZ7oVExIMb8dQNGOU3wmk37u7CX+kdRaC7jvqhpARuugkyM+E3vynbnJMDixbB5MnxC80Y07BFMoO7kPLObVR1Lq7TOSIi0hj4P+AE4LQAcye+JnB1297ARlX9KdLXrDNefBFWrIAXXoCUFFJToaCgfPf06e7h8UB+ftyiNMY0QBGvlCcirUXkHBG5VERaerd5RCTktbzHvACMAM5X1SUBDnsD6CAiWT7npQPnevfVTwUFrvT4ccfBxRcDsHYtjBsHaWnukLQ0V/Fj3bo4xmmMaZAimZQnwJ9xE+Wa4PoVjsc1G70OLAJCjfOchusYnwLsF5HBPvs2e5uj3sDN2H5eRP5A+aS80tevnx57zM2teOopSHJ5NyMD0tNdHvF43HN6OrRvH+JaxhgTZZH2WVyLG700iIodznNxI6JCOdP7fBsuIfg+rgDwDs09B9eh/jgwBygGhqvqpgjirTt273bLpJ55Jpx6aoVd27dDdjYsWeKerZPbGBMPkczgvgKYrKr3ioh/Rbs1QPdQF1DVzHBeSFV3A5d5H/XfPffAnj0By3rMnl3+9bRptRiTMcb4iOTOogOuNlQgB3H1okyk1q+HRx+FCRPg2GPjHY0xxgQUSbLYAvQJsq8vrnaTiUBuLmQNymebZNi4WGNMQoskWbwK/FFETvLZpiJyFPB74KWoRtYA5FyXx6IdRzG590vQsWO8wzHGmKAiWYM7FXgPOBHYAGQCa4FOwMe4yXUHYxNmZBJ9DW7/+ROlbP6EMSaeqlqDO+w7C1XNx5URn4BLDu/jZnBfBYxMlERRF6xdC+OGbSWN/YDNnzDGJL6IVspT1WLgOe/DVFNG22LSv1hEAT/H41EKCsTmTxhjEpqtwR0PL77I9t1pZI/8nqseOJIZM1xntzG+9uzZw86dOzl40G7aTc00adKE1q1bc9hhh1X7GpH0WTTBTcz7BdAZSPE7RFU1IZJPQvdZFBZCr17QsiV8+mnZbG1jfBUUFLBx40Y6duxIamoqroCCMZFTVfLz89m8eTOdO3fG4/EEPbaqPotIl1X9DfAOMBufooImAjNmuLkVTz5picIElZeXR5s2bUgrLQxmTDWJCGlpabRu3Zq8vDw6depUretEkix+DtypqlOq9UoGfvrJ1RsfPhxGjgx9vGmwCgoKaG+dWCaKmjdvzq5du6p9fqTLqi6u9isZePhhyMtz5T2sWcFUoaioiEaNEqJV19QTjRo1oqioqNrnR5Is5gKnVPuVGrqdO+GBB+CCC2Dw4JCHG2P9FCaaavr/qco/XUSkm8+3jwLPikgJ8Dbla2KXUdW1NYqmPrvvPtcM9ac/xTsSY4yJWKg7izXAau/jY+BI4C5gqc9234cJZPNmt17FL38JxxwT72iMqXVPP/00IhLwMXPmTESE9evXlx1/11138cEHH8QvYFNJqEbRX9dKFPXd3XeDKtx1V7wjMSauXn31VTr61UHLyMhg8eLFZGRklG27++67ue222zjVb30XEz9VJgtVfaa2Aqm3Vq2CWbPgt7+FzMx4R2NMXPXr148ePXpU2t6lS5c4RGMiUe2B/iJymIgMFBErl1qVO+5wlQMnTYp3JMYkpNImqtJmqNKO2ClTppQ1Vd1ld+VxV2WyEJFRInJfgO2TgB24vosNIvKiiNg4P3/LlsGrr8Lvfw9t28Y7GmPirri4mKKiorJHcXFxpWMWL3Yj9CdMmMDixYtZvHgxV1xxRW2HavyE+oDPBirUAxGRkcCfgC+BmcDRwNXAcmBqDGKsU3Jz4eKL4eWXof2kSdCqlUsWxtTU9dfDihXxjaFfPzdfqJp69epV4fuTTjqpUiIY7B1a3qFDh7KvTfyFShb9gRy/bb8GCnDrV2yDstvGcViyICcHFi2CyVdt5vF582DqVEhPj3dYxiSEOXPmVOjgbt68edmdhElsoZJFW+B7v20jgUWlicLrLeCX0QysrvFf0Gj63I5MR/HcpuTfEL+4TD1Sg7/oE0WfPn0qdXBbsqgbQnVw7wOaln4jIkcCrYAlfsftBZKjG1rdsnYtjBvnFjICSGM/4wevYd06m4VrjKn7QiWLb4Hzfb4/H9eH8Z7fcV2B7VGMq87JyHCtTQUFikcKKCCV9L7dbEEjY6qhSZMm5NsawwklVLJ4CLhCRP4pItOAu3Ed2//xO2408HkM4qtTtm+H7GGrWKKDyB61jm07rAS5MdXRu3dv3nrrLebNm8eyZcvYunVrvENq8Kr8NFPV14DrgeOBX+Gany5UnxWTvPMshuPqRTVos/9RyLQ1o+g7sAnT3unG7NnxjsiYuumxxx6jadOmnHvuuRx//PHMmDEj3iE1eGGvlFeXxG2lvEceccMb582D006r/dc39cbKlSs5+uij4x2GqWdC/b+qaqU8ayeJln37XEXZESMsURhj6h1LFtHy4INuzYp77413JMYYE3WWLKIhLw/+8hcYMwaOPz7e0RhjTNTVerIQkY4i8qiILBaRAyKiIpLpd0ymd3ugx+G1HXNI994LBw7YwkbGmHorHsX/egBjcbWkFgKnV3HsvcAbftv2xSiu6tm4EaZNgwkTwDokjTH1VDySxQJVbQcgIldQdbJYq6r+s8UTS2np5DvvjGsYxhgTS7XeDKWqJbX9mjGzciU88wz85jfQuXO8ozHGmJhJ9A7ue0WkSET2iMgbInJsvAOq4PbboWlTuPXWeEdijDExlajJohB4ErdOxnDgRuBY4GMRCdgxICJXicgyEVmWl5cX+wg/+QRmz3ZrVbRpE/vXM8aYOErIZKGquaqaraqzVXWhqv4NOAVXxPC2IOfMUNWBqjqwTW18eE+aBK1bww1Wf9wYU/8lZLIIRFU3AYtwdari6/334d//ds1QzZvHOxpjEl7pOtuBHocffni8wwsq0df/fvjhh5ldS0Xo6tq62YLfMq+1TtX1UXTuDNnZcQ3FmLrm1VdfrbBSHkCjRon7MbR48eJK8SaShx9+mKFDhzJmzJiYv1bivkt+RKQzcBIwJ66BzJ4Ny5bBU09BSkpcQzGmrunXr1+llfISUWFhISkpKbYGuI+4NEOJyM9F5OfAAO+mM73bsrz7p4rIQyIyVkSGi0g2bgJfCXBPPGIGoKgIbrsNeveGXzboVWRNHZSbC1lZsG1b6GNrW0lJCcOGDSMzM5M9e/aUbf/yyy9JTU3lD3/4Q9m2zMxMLrnkEv72t7/Ro0cPPB4Pxx13HPPnz6903Y8++ogRI0bQvHlzmjZtyqhRo/jqq68qHDNs2DCGDh3K3Llz6d+/PykpKTz++ONA5Waou+66CxHh22+/ZdSoUTRt2pTOnTvz1FNPAfDcc8/Rq1cvmjVrxvDhw/n+e/9VqeFvf/sbffv2xePx0Lp1ay6//HJ2795d4RgR4fbbb+evf/0rXbt2pXnz5mRlZfH1119X+HfYsGEDL7zwQlmT3oQJE8L/R4+Uqtb6A9eUFOjxoXf/ZcCnwA9AEbANeBHoGc71BwwYoDExc6YqqM6eHZvrG+P1zTffRP2aEyeqJiW559r21FNPKaDffvutHjp0qMKjuLhYVVU3bdqkLVu21IsuukhVVQ8cOKC9e/fWAQMGaGFhYdm1unTpoh07dtRevXrpSy+9pHPmzNHBgwdrSkqKfvvtt2XHvfnmm5qcnKznnXeevvbaa/raa6/pkCFD9PDDD9eNGzeWHZeVlaVt2rTRzMxM/fvf/67z58/Xzz//XFVVAb3zzjvLjr3zzjsV0D59+ugjjzyi7733nl5wwQUK6K233qpDhgzROXPm6CuvvKIZGRl6wgknVPh3uPnmm7VRo0Z6ww036LvvvquzZs3SI444Qk844QQtKioqOw7QLl266Omnn66vv/66vvrqq5qZmandu3fXQ4cOqarqZ599pu3bt9dRo0bp4sWLdfHixbpmzZoq34dQ/6+AZRrsczvYjrr8iEmyyM9X7dhR9YQTVEtKon99Y3xEM1l4PO433f/h8UTtJUIqTRaBHmeffXbZcbNnz1ZAZ82apVdeeaU2bdpUV61aVeFaXbp00caNG+uGDRvKtu3du1dbtGihl1xySdm27t2766mnnlrh3D179mirVq30d7/7Xdm2rKwsFRH973//WynuYMnimWeeKdu2e/duTU5O1pYtW+qePXvKtj/yyCMK6Pr161VVdd26dZqUlKR33313hddYtGiRAjpnzpwKr9ujRw89ePBg2bZXX31VAf3Pf/5T4d9i/PjxleIOpibJos70WcTd44/D5s3w7LMgEu9ojAnb2rVw443w2muu3mVaGowe7Qol17Y5c+ZU6jD2HQ01evRorr76aiZOnEhhYSGzZs3iqKOOqnSdwYMH09mnakLz5s05++yzWbx4MQCrV6/m+++/Z9KkSRQVFZUdl5aWxpAhQ1iwYEGF62VmZtKvX7+wf44zzzyz7OsWLVrQtm1b+vfvT3p6etn2Xr16AbBp0ya6dOnCvHnzKCkpYfz48RViGjRoEOnp6SxYsIALLrigbPvIkSNp3Lhx2ffHHuvmJG/cuJETTzwx7FijxZJFOPbuhXvugZEjYfjweEdjTEQyMiA9HQoKwONxz+np0L597cfSp0+fkB3cl156KU8++SRt27Zl3LhxAY9p165dwG1btmwBYMeOHQBcfvnlXH755ZWO7exXnicjIyOs+Eu1aNGiwvdNmjQJuA2goKCgQkzBfv5du3ZV+L5ly5YVvk/xDqgpvV5ts2QRjqlTYdculzCMqYO2b3cjva+6CmbMcJ3diejAgQNcdtll9OnTh9WrV3PLLbfw0EMPVTpu+/btAbd16NABgFatWgFw7733clqAlStLP8hLSS20FpTG9N5771VKLL77E5Uli1B27HDJ4sILYWDApWmNSXi+87amTYtfHKH87ne/Y8uWLaxYsYI333yT66+/nlGjRnHGGWdUOG7JkiVs2rSJTp06AbBv3z7eeustzj77bAB69uxJZmYmX3/9Nbfcckut/xyBjBw5kqSkJDZu3MjIkSOjcs2UlBTy8/Ojcq1QLFmEMmWKu2/PyYl3JMbUeStWrGDnzp2Vtg8cOJDXX3+dmTNn8txzz9GtWzeuu+463nvvPSZMmMAXX3xB27Zty45v164dp59+OnfddRcpKSncf//97N+/nzvuuANwdwrTpk3j/PPP5+DBg4wdO5bWrVuzfft2Pv74Yzp37swNtVyqp3v37tx8881ce+21rFq1iqysLDweD5s2bWLevHlcccUVDI+wmbt3794sXLiQN998k/bt29O6dWsyMzNjEr8li6qsXw9PPAG//jX07BnvaIyp8y688MKA2zdu3MiVV17J+PHjueSSS8q2P/XUU/zsZz9jwoQJvPXWW2XNRVlZWQwbNoxJkyaxefNmevfuzTvvvFOhM/yss85iwYIFTJkyhSuuuIL8/Hzat2/P4MGDueiii2L7gwZxzz33cPTRRzNt2jSmTZuGiNCpUydGjBjBkUceGfH17r33Xq688krGjh1Lfn4+l156KU8//XT0AwfEjZaqXwYOHKjLli2r+YUmTICXXoI1ayCBp/yb+mflypUcbSsvBpSZmcnQoUN5/vnn4x1KnRPq/5WILFfVgO3tdaaQYK376is3TPbaay1RGGMaPEsWwZRWlLWFjYwxxvosAlqyBF5/3XVqJ/hwNmMamvXr18c7hAbJ7iz85G5Vss7wsK3VMXD99fEOxxhjEoIlCz85V21i0Z5jmdzrRWjWLN7hGGNMQrBmKK/UVDedAlwZgOn/+RnTxZVHqKU5L8ZUoKq1MrPYNAw1HflqdxZea9fCuHP3ksYBwBVbGz8e1q2Lc2CmQWrcuHGtzcw1DUN+fn6FwoSRsmThlZEB6R3SKUhKxePRuBZbM6Zt27Zs2bKFAwcO1PgvQtOwqSoHDhxgy5YtFWbBR8qaoXy4YmuS8MXWTP1XWup669atHDp0KM7RmLqucePGtGvXrkIJ9UjZDG5jjDGAzeA2xhhTQ5YsjDHGhGTJwhhjTEiWLIwxxoRkycIYY0xIliyMMcaEVC+HzopIHrChGqceBuyp5stGem64x4c6rqr9wfYF294aqLzmZfzU5P2I1XVj8T7H4j0Oti/R3mOIzfucaO9xOMclwu9yF1VtE3CPqtrD+wBm1Na54R4f6riq9gfbV8X2ZfF+D6L1ftSl9zkW73GwfYn2HsfqfU609zhW73Nt/i5bM1RFc2vx3HCPD3VcVfuD7avJz1mbYhVnor3PsXiPw33tRBCLOBPtPQ7nuIT+Xa6XzVCmekRkmQaZvWnqB3uPG4ZYvM92Z2F8zYh3ACbm7D1uGKL+PtudhTHGmJDszsIYY0xIliyMMcaEZMnCVJuITBKRVSJSIiIXxDseU3Mi0l1EFonIdyLyXxGxzvB6prq/t5YsTE38GzgLWBDvQEzUPAE8rapHATcBL4gtBF7fVOv31pJFPSEiHUXkURFZLCIHRERFJDPIsZ1E5J8iskdE9orIbBHpHOlrqupSVf2+xsGbaovm+y4ibYDBwDMAqjrPu2tArH8OE1y0f7er+3tryaL+6AGMBX4AFgY7SETSgA+AXsClwC+BI4H5ItK0FuI00RXN970zsFVVfddx3eDdbuInIX63bQ3u+mOBqrYDEJErgNODHHcl0A3oqaprvMd/AawGrgYe9G77jOAfEv1VdVMUYzfVF9X3PQBrgoq/WL/HYbE7i3pCVUvCPPQ8YEnpfybvueuA/wDn+2w7TlVbB3lYokgQUX7fNwJHiEhjn/O6eLebOIn273Z1WbJoeI4Bvgqw/Wugdy3HYmpPyPddVfOAT4AJACIyEndnsbx2QjQ1FNPfbUsWDU9LXNunv91Ai0guJCK3i8hmYAgwU0Q2i0j7KMRooi/c9z0b+LWIfAc8AIxXK/NQV4T1Hlf399b6LBqmQL/8EbdNq+qfgD/VPBxTS0K+76q6GjixdsIxMRDOe1yt31u7s2h4fsD9BeKvBYH/KjH1g73v9V9M32NLFg3P17i2TX+9gW9qORZTe+x9r/9i+h5bsmh43gAGi0i30g3eCT4nefeZ+sne9/ovpu+xlSivR0Tk594vR+A6Kq8B8oA8Vf3Ie0xT4HMgH7gd18aZAzQHfqaqP9V23KZm7H2v/xLhPbZkUY+ISLA38yNVHeZzXGfgIaB0aOS/getVdX2sYzTRZ+97/ZcI77ElC2OMMSFZn4UxxpiQLFkYY4wJyZKFMcaYkCxZGGOMCcmShTHGmJAsWRhjjAnJkoUxxpiQLFkYY4wJyZKFabBE5AIRuSHA9mEioiIyrPajCkxEBojIARHpEME5j4jIW7GMyzQcNoPbNFgi8jRwmqp29NuejrdSp6rujUds/kTkA1w810ZwTgawFjhbVT+IWXCmQbA7C2P8qOpeVV2SQIniOGA4MD2S81Q1F5gL3BiLuEzDYsnCNEjeu4pLgQ7eJicVkfXefZWaoUTkQxFZJCJniMgKEckXkf+KyCARaSQi94hIrojsFpGnvRVAfV8vTUTuF5F1InLQ+3ybiITzO3gl8IWqfu13zXHeGH4SkT0i8qWIXO137kvAKBHpFOm/kTG+bFlV01DlAG2A44HzvNsKQ5zTA7cu9RTgJ+DPuHUC3sD9Lk0AjvYeswO4CUBEGgHv4pq2coAvgcHAHbiVzX4f4nXPACr0PYjIUOB54K/AH3B/+PUCDvc7d4F330hgVojXMSYoSxamQVLV70UkDzioqkvCPK0VcKKqrgXw3hW8DnRV1dO8x7wrIqcAF+JNFsAvgKFAlqou8G77t4gA3Cki96vqjkAvKCLtgEzcOgW+BgM/qur1PtveC/Bz7hSRzd7jLVmYarNmKGPC911povD61vv8rt9x3wIdxZsNcHcGG4CPvU1Wjbx3G+8BjXEf5MEc4X3O89v+KdBCRJ4XkXNE5PAqrpHncx1jqsWShTHh81/0/mAV2xsByd7v2wJdgEN+j0+8+1tV8Zoe73OFJjLv6mgXAp2AOUCeiLwvIj8LcI18ILWK1zAmJGuGMib2dgHrgLFB9q8PcS5AC/8dqvpP4J8i0gwYBtwP/EtEOqpqic+hLYEvIozZmAosWZiGrJDa+Yv7X8D/AD+p6rehDvazHigAugU7wLu28psi0g14BHenkgcgIsm4u49XIw/bmHKWLExD9g3QUkQmAsuAAlX9Mgav8wLwa1yn9lRcZ3UToDtuJNYFqnog0ImqelBElgIn+G4XkclAO2A+sBXoCFwHrFBV3/6NPkBT3KgoY6rNkoVpyGbiOpfvwQ053YAbeRRVqnpIREYBtwBXAV2B/cD3uCGxB6s4HeBl4AERaaqq+73bluKSw0O4ZqYduA7zO/zOPQfYBnxY85/ENGRW7sOYBOctP7IZuEZVn4/w3G+A/1NV/yRiTERsNJQxCc5bduR+4Caf4bghicj5uKaqqbGKzTQc1gxlTN3wIG4obgaujyIcqcAlqvpjrIIyDYc1QxljjAnJmqGMMcaEZMnCGGNMSJYsjDHGhGTJwhhjTEiWLIwxxoT0/0IUAONtNLHiAAAAAElFTkSuQmCC\n", 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[0.96319355]\n", + " [0.96622581]\n", + " [0.96896774]\n", + " [0.96945161]\n", + " [0.96229032]\n", + " [0.9656129 ]\n", + " [0.97016129]\n", + " [0.96967742]\n", + " [0.96893548]\n", + " [0.96841935]\n", + " [0.96909677]\n", + " [0.96925806]\n", + " [0.96870968]\n", + " [0.97193548]\n", + " [0.9703871 ]\n", + " [0.96935484]\n", + " [0.96690323]\n", + " [0.9703871 ]\n", + " [0.96793548]\n", + " [0.96925806]]\n", + "Warning: 50000 points required, but 50176 points sampled.\n" + ] + } + ], + "source": [ + "# time interval and shear interval of interest\n", + "timedomain = dde.geometry.TimeDomain(0,1) # non-dimensional end time\n", + "geom = dde.geometry.Interval(0,1) # non-dimensional shear rate\n", + "timexgeom = dde.geometry.GeometryXTime(geom, timedomain) # Creating time and geometry fields\n", + "\n", + "# defining and initializing training model paramaters (to be determined by solving inverse problem)\n", + "G = dde.Variable(9000.0) \n", + "eta_s = dde.Variable(10.0) \n", + "yield_stress = dde.Variable(60.0) \n", + "k = dde.Variable(200.0) \n", + "n = dde.Variable(0.5) \n", + "\n", + "\n", + "# defining the NEVP rheology model\n", + "def pde(x,y):\n", + " \n", + " stress = y[:,0:1]\n", + " shear_rate, t = x[:,0:1], x[:,1:2]\n", + " stress_rate = dde.grad.jacobian(y,x,i=0,j=1)\n", + " \n", + " eqn1 = stress_rate - (del_time/del_stress)*\\\n", + " ((G*(shear_rate*del_shear_rate + shear_min))/(yield_stress + k*(shear_rate*del_shear_rate + shear_min)**n + eta_s*(shear_rate*del_shear_rate + shear_min)))\\\n", + " * (-stress*del_stress - stress_min + yield_stress + k*(shear_rate*del_shear_rate + shear_min)**n)\n", + "\n", + " return [eqn1]\n", + "\n", + "\n", + "# training data\n", + "observe_x, shear_stress_training = gen_training_data() # observe_x gets shear rate and time \n", + "print(observe_x)\n", + "print(shear_stress_training)\n", + "observe_y1 = dde.PointSetBC(observe_x, shear_stress_training) \n", + "\n", + "# defining the data \n", + "data = dde.data.TimePDE(\n", + " timexgeom,\n", + " pde,\n", + " [observe_y1], \n", + " num_domain = 3000, \n", + " anchors = observe_x,\n", + " num_test = 50000, \n", + " #num_initial = 200,\n", + " train_distribution='pseudo',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "id": "3b0f8e70", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "layer_size = [2] + [10]*4 + [1] # NN architecture\n", + "activation = \"tanh\" #activation function\n", + "initializer = \"Glorot normal\" # Xavier initialization\n", + "\n", + "# developing the Neural network\n", + "net = dde.nn.FNN(layer_size, activation, initializer)\n", + "\n", + "# developing the model\n", + "model = dde.Model(data,net)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "id": "2de7b943", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Compiling model...\n", + "Warning: For the backend tensorflow.compat.v1, `external_trainable_variables` is ignored, and all trainable ``tf.Variable`` objects are automatically collected.\n", + "Building feed-forward neural network...\n", + "'build' took 0.108091 s\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\deepxde\\nn\\tensorflow_compat_v1\\fnn.py:110: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " kernel_constraint=self.kernel_constraint,\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\legacy_tf_layers\\core.py:255: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n", + " return layer.apply(inputs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'compile' took 0.977269 s\n", + "\n" + ] + } + ], + "source": [ + "# compiling the NN\n", + "model.compile(\"adam\",\n", + " lr=0.0005, # learning rate\n", + " loss_weights=[1,1e2], # w_sigma, w_data\n", + " external_trainable_variables=[G, eta_s,yield_stress,k,n]) # Unknown model parameters to be determined\n", + "\n", + "# extracting training parameters value any intermediate intervals and saving the file\n", + "variable = dde.callbacks.VariableValue([G, eta_s,yield_stress,k,n], \n", + " period=1000, # Show output every 1000 epochs \n", + " filename=\"variables_DOW1_unification_008_030323.dat\") #File name where the parameters are stored\n" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "id": "17391d5e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing variables...\n", + "Training model...\n", + "\n", + "Step Train loss Test loss Test metric\n", + "0 [4.26e+04, 5.69e+01] [4.16e+04, 5.69e+01] [] \n", + "INFO:tensorflow:model-DOW1-unification-008s-030323/model-DOW1-unification-1.ckpt is not in all_model_checkpoint_paths. 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Manually adding it.\n", + "992000 [1.12e-03, 8.95e-02] [7.36e-04, 8.95e-02] [] \n", + "INFO:tensorflow:model-DOW1-unification-008s-030323/model-DOW1-unification-992000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "993000 [1.05e-03, 8.95e-02] [6.73e-04, 8.95e-02] [] \n", + "INFO:tensorflow:model-DOW1-unification-008s-030323/model-DOW1-unification-993000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "994000 [1.04e-03, 8.95e-02] [6.54e-04, 8.95e-02] [] \n", + "INFO:tensorflow:model-DOW1-unification-008s-030323/model-DOW1-unification-994000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "995000 [1.05e-03, 8.94e-02] [6.32e-04, 8.94e-02] [] \n", + "INFO:tensorflow:model-DOW1-unification-008s-030323/model-DOW1-unification-995000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "996000 [1.09e-03, 8.92e-02] [7.15e-04, 8.92e-02] [] \n", + "INFO:tensorflow:model-DOW1-unification-008s-030323/model-DOW1-unification-996000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "997000 [2.00e-03, 8.91e-02] [1.47e-03, 8.91e-02] [] \n", + "998000 [1.07e-03, 8.91e-02] [6.80e-04, 8.91e-02] [] \n", + "INFO:tensorflow:model-DOW1-unification-008s-030323/model-DOW1-unification-998000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "999000 [1.08e-03, 8.90e-02] [7.30e-04, 8.90e-02] [] \n", + "INFO:tensorflow:model-DOW1-unification-008s-030323/model-DOW1-unification-999000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1000000 [1.49e-03, 8.90e-02] [1.23e-03, 8.90e-02] [] \n", + "\n", + "Best model at step 999000:\n", + " train loss: 9.01e-02\n", + " test loss: 8.98e-02\n", + " test metric: []\n", + "\n", + "INFO:tensorflow:C/model-DOW1-unification-008s-030323-1000000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "Epoch 1000000: saving model to C/model-DOW1-unification-008s-030323-1000000.ckpt ...\n", + "\n", + "'train' took 10833.690483 s\n", + "\n", + "Saving loss history to C:\\Users\\pnagrani\\loss.dat ...\n", + "Saving training data to C:\\Users\\pnagrani\\train.dat ...\n", + "Saving test data to C:\\Users\\pnagrani\\test.dat ...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Saving model at intermediate steps #\n", + "checkpointer = dde.callbacks.ModelCheckpoint(\n", + " \"model-DOW1-unification-008s-030323/model-DOW1-unification\", verbose=0, save_better_only=True\n", + " )\n", + "\n", + "# Resampling PDE loss data points #\n", + "resampler = dde.callbacks.PDEResidualResampler(period=1000)\n", + "\n", + "# Training the model # \n", + "losshistory, trainstate = model.train(epochs = 1000000, callbacks = [variable, checkpointer, resampler],model_save_path='C/model-DOW1-unification-008s-030323') \n", + "\n", + "# Saving train and test error plots #\n", + "dde.saveplot(losshistory, trainstate, issave=True, isplot=True)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "id": "ef422396", + "metadata": {}, + "outputs": [], + "source": [ + "# experimental data to validate the model results at unseen 0.05/s data #\n", + "\n", + "valPoints = pd.read_excel('DOW1_030323.xlsx',header = None, names=['shear rate','t','shear stress'], sheet_name=2,skiprows=range(155,298))\n", + "\n", + "t_expt1 = np.array(valPoints[\"t\"])\n", + "t_expt1 = t_expt1[:,np.newaxis] # Converting to a column vector\n", + "\n", + "gammadot1 = np.array(valPoints[\"shear rate\"])\n", + "gammadot1 = gammadot1[:,np.newaxis]\n", + "\n", + "shear_stress1 = np.array(valPoints[\"shear stress\"])\n", + "shear_stress1 = shear_stress1[:,np.newaxis] # experiment value\n", + "\n", + "\n", + "t_non_dim = (t_expt1-time_min)/(del_time) #(t_expt1-0.5)/(600.0-0.5)\n", + "gammadot_non_dim = (gammadot1-shear_min)/(del_shear_rate) # (gammadot1-0.1)/(20.0-0.1) \n", + "\n", + "# using model to predict shear stress at a new shear rate #\n", + "\n", + "test_data_set = np.hstack((gammadot_non_dim,t_non_dim))\n", + "test_shear_stress = model.predict(test_data_set) \n", + "\n", + "# making non-dimensional to dimensional\n", + "test_shear_stress1 = test_shear_stress*(del_stress) + stress_min\n" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "id": "f4369833", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting shear stress with time #\n", + "\n", + "plt.figure()\n", + "plt.plot(t_expt1,test_shear_stress1, '-r', label='PINN')\n", + "plt.plot(t_expt1,shear_stress1, '^b', label='Experiment')\n", + "plt.legend()\n", + "plt.xscale('log')\n", + "plt.xlabel('Time (s)')\n", + "plt.ylabel('Shear stress (Pa)')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "id": "0605e0af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Restoring model from model-DOW1-unification-008s-030323/model-DOW1-unification-999000.ckpt ...\n", + "\n", + "INFO:tensorflow:Restoring parameters from model-DOW1-unification-008s-030323/model-DOW1-unification-999000.ckpt\n" + ] + } + ], + "source": [ + "# Restore the saved model with the smallest training loss - Run initial steps until compilation\n", + "# enter the correct model number\n", + "model.restore(f\"model-DOW1-unification-008s-030323/model-DOW1-unification-999000.ckpt\", verbose=1) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "id": "2174c10d", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting training and test data for manuscript #\n", + "\n", + "plt.rcParams.update({'font.size': 16})\n", + "\n", + "for itrnew in range(0,5):\n", + " if itrnew == 2:\n", + " continue\n", + " valPoints = pd.read_excel('DOW1_030323.xlsx',header = None, names=['shear rate','t','shear stress'], sheet_name=itrnew,skiprows=range(155,298))\n", + " \n", + " t_expt1 = np.array(valPoints[\"t\"])\n", + " t_expt1 = t_expt1[:,np.newaxis] # Converting to a column vector\n", + "\n", + " gammadot1 = np.array(valPoints[\"shear rate\"])\n", + " gammadot1 = gammadot1[:,np.newaxis]\n", + "\n", + " shear_stress1 = np.array(valPoints[\"shear stress\"])\n", + " shear_stress1 = shear_stress1[:,np.newaxis] # experiment value\n", + "\n", + "\n", + " t_non_dim = (t_expt1-time_min)/(del_time)\n", + " gammadot_non_dim = (gammadot1-shear_min)/(del_shear_rate) \n", + "\n", + " # using model to predict shear stress at a new shear rate #\n", + " test_data_set = np.hstack((gammadot_non_dim,t_non_dim))\n", + "\n", + " test_shear_stress = model.predict(test_data_set,operator=lambda x,y: y[:,0:1]) # Predicting shear stress \n", + " \n", + " # making non-dimensional to dimensional\n", + " test_shear_stress = test_shear_stress*(del_stress) + stress_min\n", + " \n", + " if itrnew == 0:\n", + " plt.plot(t_expt1,test_shear_stress, '-r', label='PINN')\n", + " plt.plot(t_expt1,shear_stress1, '^b', label='Experiment: training data')\n", + " elif itrnew == 2:\n", + " plt.plot(t_expt1,test_shear_stress, '-k', label='PINN: prediction '+str(itrnew+1)+'/s')\n", + " plt.plot(t_expt1,shear_stress1, '^g', label='Experiment: test data '+str(itrnew+1)+'/s')\n", + " else:\n", + " plt.plot(t_expt1,test_shear_stress, '-r')\n", + " plt.plot(t_expt1,shear_stress1, '^b')\n", + "\n", + "plt.legend(loc='lower right')\n", + "plt.xscale('log')\n", + "plt.xlabel(r'$t$ (s)',fontsize=16)\n", + "plt.ylabel(r'$\\sigma$ (Pa)',fontsize=16)\n", + "plt.savefig('Shear_stress_trainingdata_DOW1_unification_1.eps',format='eps',bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "valPoints = pd.read_excel('DOW1_030323.xlsx',header = None, names=['shear rate','t','shear stress'], sheet_name=2,skiprows=range(155,298))\n", + " \n", + "t_expt1 = np.array(valPoints[\"t\"])\n", + "t_expt1 = t_expt1[:,np.newaxis] # Converting to a column vector\n", + "\n", + "gammadot1 = np.array(valPoints[\"shear rate\"])\n", + "gammadot1 = gammadot1[:,np.newaxis]\n", + "\n", + "shear_stress1 = np.array(valPoints[\"shear stress\"])\n", + "shear_stress1 = shear_stress1[:,np.newaxis] # experiment value\n", + "\n", + "\n", + "t_non_dim = (t_expt1-time_min)/(del_time) \n", + "gammadot_non_dim = (gammadot1-shear_min)/(del_shear_rate) \n", + "\n", + "# using model to predict shear stress at a new shear rate #\n", + "\n", + "test_data_set = np.hstack((gammadot_non_dim,t_non_dim))\n", + "test_shear_stress = model.predict(test_data_set,operator=lambda x,y: y[:,0:1]) # Predicting shear stress \n", + "test_shear_stress = test_shear_stress*(del_stress) + stress_min\n", + "\n", + "plt.plot(t_expt1,test_shear_stress, '-k', label='PINN')\n", + "plt.plot(t_expt1,shear_stress1, '^g', label='Experiment: test data')\n", + " \n", + "plt.legend(loc='lower right')\n", + "plt.xscale('log')\n", + "plt.xlabel(r'$t$ (s)',fontsize=16)\n", + "plt.ylabel(r'$\\sigma$ (Pa)',fontsize=16)\n", + "plt.savefig('Shear_stress_testdata_DOW1_008s_unification_1.eps',format='eps',bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "60bab74e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Compiling model...\n", + "Warning: For the backend tensorflow.compat.v1, `external_trainable_variables` is ignored, and all trainable ``tf.Variable`` objects are automatically collected.\n", + "'compile' took 0.507090 s\n", + "\n", + "Training model...\n", + "\n", + "1500091 [2.03e-06, 2.45e-06, 2.48e-04] [2.54e-06, 2.18e-06, 2.48e-04] [] \n", + "INFO:tensorflow:Optimization terminated with:\n", + " Message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n", + " Objective function value: 0.000252\n", + " Number of iterations: 1\n", + " Number of functions evaluations: 18\n", + "1500109 [2.03e-06, 2.45e-06, 2.48e-04] [2.54e-06, 2.18e-06, 2.48e-04] [] \n", + "\n", + "Best model at step 1500091:\n", + " train loss: 2.52e-04\n", + " test loss: 2.52e-04\n", + " test metric: []\n", + "\n", + "'train' took 1.818854 s\n", + "\n", + "Saving loss history to C:\\Users\\pnagrani\\loss.dat ...\n", + "Saving training data to C:\\Users\\pnagrani\\train.dat ...\n", + "Saving test data to C:\\Users\\pnagrani\\test.dat ...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Compiling model...\n", + "Warning: For the backend tensorflow.compat.v1, `external_trainable_variables` is ignored, and all trainable ``tf.Variable`` objects are automatically collected.\n", + "'compile' took 0.630434 s\n", + "\n", + "Training model...\n", + "\n", + "Step Train loss Test loss Test metric\n", + "1500109 [2.03e-06, 2.45e-06, 2.48e-05] [2.54e-06, 2.18e-06, 2.48e-05] [] \n", + "\n", + "Best model at step 1500109:\n", + " train loss: 2.93e-05\n", + " test loss: 2.95e-05\n", + " test metric: []\n", + "\n", + "'train' took 0.628264 s\n", + "\n" + ] + } + ], + "source": [ + "# If needed can perform further training using L-BFGS optimizer. We skipped as it did not improve the loss significantly #\n", + "\n", + "model.compile(\"L-BFGS-B\",\n", + " loss_weights=[1,1,1e1],\n", + " external_trainable_variables=[G, eta_s, k_plus, k_minus])\n", + "\n", + "model.train_step.optimizer_kwargs = {'options': {'maxfun': 1e5, 'ftol': 1e-20, 'gtol': 1e-20,'eps': 1e-20, 'iprint': -1, 'maxiter': 1e5}}\n", + "\n", + "variable = dde.callbacks.VariableValue([G, eta_s, k_plus, k_minus], period=10, filename=\"variables1-008s_DOWTC5622_LBFGSB.dat\")\n", + "losshistory, trainstate = model.train(callbacks = [variable])\n", + "dde.saveplot(losshistory, trainstate, issave=True, isplot=True)\n", + "\n", + "variable = dde.callbacks.VariableValue([G, eta_s, k_plus, k_minus], period=1, filename=\"dummy-4s.txt\")\n", + "model.compile(\"adam\", lr=0, external_trainable_variables=[G, eta_s, k_plus, k_minus])\n", + "losshistory2, train_state2 = model.train(epochs=0, callbacks=[variable], display_every=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "02dd3ef2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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qWsekSZN49NFHAbj//vu56667yMxsrQMSz6p68+fP54YbbgjaJiMjg9tvv53p06dz6aWX0r9/8Mn4pZde4qWXXormMCPCbHePdpx7JJgn/ux573tMNE6tSTphMmPGjLEcq3nit3ofvueNCX3KNX9iw5IHyZ73vieiSAOlFUeZ/pt5HpOTedW/ZGWux5dQYbGnwIxxndPlCtquq5GSnEi/Pj2pqqn3O9dVJvxoIT6OCFi2udir5vjSLYej2n9WVhZFRUWsWbOGrVu3ctttt3mdv+qqq7yc1eYf3wR5w4cP5/Dhw57a1U6nk+LiYoYPHx5xu+LiYj799FOuvfbagGNXSrF582ZuvPFGli9fzhlnnNGp7cjmlb1hd29vf4ad32zbN9v+zW3MmJ2+i1bkYrMb9cYTSBw4gao6LMdqTPw57/7Py6ls3Pvb1/yZz03nd+4p8QiIXz25hNdaHMfmCX7f4SrL9+frgO7OnDFuGGeMG+Z33GZ3kjkkjcYv/+r3czwJDRCNI2wMbcPR8g1yuHTUtQ6lFKNGjWLGjBksX77cYz4yiETjGDx4MBMnTiQnJ4cZM2aQk5PDpEmT8M3dFU67BQsW8MMf/pD09PSA98vPz2fs2LFcdNFFjBgxgqlTp3bqvQVeK3uXq91ah1kj0Fp7VvgFe0v9dgobrw0fwtaCIlxak/PuFyQkeK/llFLc8MhCz1gdTqfHpLRoRa7XxG84lQv2lnLtA/M9AsZq5d9Wc1FnxHe1f/b0P7GtwL8waM8eyTRZONWPN20hGsg+jjB5ee1+VhW0Cg6ApATFheMzourruOaaa0hLS4uKCSc/P5+ZM2dy5MgR0tLSWLhwocfRetlll/HEE08wefLkoO0Axo4dy4svvsgll1wS8F4333wzn3zyCX379qVPnz48/fTTTJkypd3vAaK/j6OkopZTfvyY1yTSq0cyO1Y+HjLHkO8OZSMdxdaCIpptDnqmJKGBZltr/qDEBIXTpUlMSMDpcpGYoHBpGD/6BHbuKSEhQeGKcEl/8sjB7D9c6eVUPh6RST92dOmd452F/PJ6L6EBbq0jv8zf3tkeysrKmD17dlT6Gj9+PLm5uZbn3nvvvbDaAWEVGHr11VcjH2AMCeYwNmsbBlZah5UPwdAmjLZGdFFCglu7stkd4KNpOVv+b1o1A/drwxdgFhpKKXKXPkjRnm1cdtllnDDlNmosynSYHc7xJH1AH0ubf7ikJCcy6/99m41b91lqCeY28fRDCd6I4AiTF648Neb30Frz5ZdfBoxcEsInkOO7pKKWJSu/MPkR3NjsTr84d7Pz2KU1C/+9AQ0ef8KNV5zLwuUbgdbJ36Vxhw61Ea01sx5ewBXf7E3KyVdYCo2OJJB5xyBzSBpFa/7seX3nU0t5/d8bvD7fYMLB+NwNrcHKzGT1bIT4IoKjE6GUorq6OnRDISi+ju+HbrrUo3Vkz3ufxiY7N//sO0FXsEYfZh+CWZtwOJ18f9ZzQVNat5Wde0tYUuNE9T4BpdxyKDkpAZcrNpFK0VzR527bH1Aoh2NSErNT10AEh9DtCOT4DiZQfE1b2fPe90SaGZi1Cd9Na+0lQQHK7etISkxgb5U7caGhvAS6V0KC4pdXncfnm3ezI0iKbcMXEOsVvUz8saOkqo7pT6zA5nSRkpTI3DumcseLq7E5A/8fpiQlsuz3P+GEgX2iOhYRHEK3weyoNla9NrvTIySCRVKZQ2IL95extaAoYsHgu3IPFN1jha9QQhOkOLLpOpdbCF550ZnsPliOze4MqkHIxN55KKmq4/rsd1n08I/cxZqeWAFKBRQIxZV1lNe0bqqd9fT77DxgHT5tJnvJRl64fWpUxy5RVUKnJ9xndedTS5n35ud+UUopyYn87JLJvPXhFstIKq21J8rKiIBqS6QTtC3Kx8ovEIiBqX2oa2jyamtlxgoWJSZED18twLy6NwTDc7eeH1QQnDCwDxecOZLFq90p9Hv1SKKxOXom0J4piexc8MuItQ6JqhK6PYYZCvCb8G12J+99tj1gJJXW2qSJaMs+wC0U/j331ohCecPByi9gxqxBWGkxVppRNPamCN6YNYQTBvahpKqOb9/xBqXVrVFl3/rVQjIz+pGSlMjYYWms236Y79+zNKggKK2uZ8lHOzyvoyk0wP3/H22tQwSH0OlxOl1ceOOcoDmlzGYoK1NNINv+2s272VdU6TdxJycl8osr/M09dz61NKxQ3kgItXnN7IOw0mYkEik8fCf+cK8xNIriyjoqaht4ZP5aCg9Vs6eklqqj3mFvFTWNVLSEwm0uLHUXcgpDEMRyZ75Lw8IPt/PQNedEzdchpiqh07Nu4yYu+tXrnoI+vkS6oc9MMDORVR+B/BbR3Ij217/+lTvuuIPnnnuOe+65Jyp9Hi9YaQW+E/9NP/yG3+rbyt9gXGP2K0Drhs6uRIKCX1q872CIqUrostjsDuobmi0joQzC3dBnRTAzkVUfHeFczsvLA+Dkk0+O+b26A+ZJP3vJRtbnHfaYZrKXbOSLglKv9gs/2M4X+d4RaIaAmHL7Yi44c6TfNWa6mtAAt9axcUdx1PoTwSHEHZvdwf7DVYzKzCA5OdHrdWnlUYyvaSBhEGzvAATfRd4ZN55t3+7OIzVmzJgOv3dnwkp7sHqddUIq6/MO85uXP+bfn+/CpWHeu1v57umZ/P3dbX79NjucfLXbeue9r7+hs3LG6EHk/u26uN1fTFVC3DlYUk3lkToy0voy4sSBntcDU/tw5GgD5SUHufw3OUDbHNG+tas7OyeccAJlZWU0NjbSs2fPeA8nbtw5dzV/f3cbGQN6MeqEAYwdlsYbq3f4vU4IYDpKSUrA1sVzecVTQIipqguxadMmnn/++ajVHe+s1NbWcvfdd7Nq1SpWfLgejbtWdEZaX3fNaKC61j8HkpEddsOSB72KEQUrYhRo019nxOFwUFZWRr9+/bq90AimUZRV1/Pae9vc9cJbHM6Gs9l4bZibApmOOqPQiLemEC1EcHQyJk+eHDWhUVhYyMyZM6mqqiI9PZ2FCxda2s2DtVu5ciWPPPKIJ2T1scce44orrmj32FJTU5k/fz7nfed74DFGafYfrvK8tpoO7A4XpRVHvZIMBivGFO306bHm0KFDQNf0b5j3Lfzm5TWWDmrzjuf9ZUe9opT2lx31+Bn69UrxizTqzL6F7iIQwkVMVd2YH/zgB9xwww3MmDGDxYsXM3/+fD7++OOw22mtGThwIGvXruW0005j27ZtnHvuudTW1vrVjQjEnj17uOWWW7yOXXzxxdx3333Y7A6+893v87fXlgS8PkEpkp1HOe20U72ip3r1SOaT13/D92Y+53nta8JqT7RVvPjwww+5+OKLufbaa1m8eHG8hxMU30ikb9/xBmVH6hk/Ip2Cg1VcM/UU9pfWknVCqmdzG8CEkeleO567SpTScSccgpiqpAJghJTVNnLFs59SXtsU1X4rKioYMWIEy5cv9xxbsGABV111VZv6Ky8vZ8uWLVx99dUAXH311WzZsoWKioqI2iUkJFBbWwtATU0NJ554YkCh8eGHHzJlyhQmTZrE2LFjefvttznppJNYvXq11899990HEGbVPU1tnTsu3ld7mPXb1/20CTPBoq06K0ZN+dNOO61D71tSVceF9y7z2szme/57dy3he7/OobS63rP5bV3eYR6Zv5azb1tEaXU9WsPOA1W4NOR8vJPPtx/2czb7psnoCKGRkpRIev/ITH9njB5E43/v8fwcT0IjFGKqipDnV+aTu7uK59/dSfY1k6LW76BBg5gzZw45OTlMmzaNqqoqnnzyST777DNPm6uuusozsfiyYcMGevXq5Xl96NAhMjMzPVUEExMTGTp0KIcOHfKq7heq3T//+U+mTZtGnz59OHbsGO+++67l/bXWzJo1i61btzJo0CAcDgcNDQ2WbQ0evO8e9u/bw1OPP8zMG2eTOWyEXxuXdhdFMnwV5hxU5prY5pxUhjYRKtoqGrRlU1kwduxwT7LhmKrauqHN9xpDCJQdqfeYjVDKK32GOaw1e8lG6httHiGz5KMdlhvYnOZU83HG5nCSmTGQon/+Kt5D6RaI4IiAstpGlm04gNawbP0B7v7hBAanRs+BOWnSJB599FEA7r//fu666y4yMzM95yMpHRsNHA4H2dnZLF++nHPPPZd169bx85//nB07dtC3b1+/9hkZGdx+++1Mnz6dSy+9lP79g5uDlixeENY4du6st9QefPH1YXTEngvffQPtZdu2bZDSj2dX13Lu+fVBBUK49zb7HqY98i/KjtR7XfPI/LUeIZDz8U7PhJ+9ZCMPXn02059YwVd7WsNXX125lQRTAsbOIBh8Od7MSh2NmKoi4PmV+eiWucvlguff3RnV/rOysigqKmLNmjVs3bqV2267zev8VVddxcSJEy1/Ghu9Ux8MHz6cw4cPe1KDO51OiouLGT58eNjtvvrqK4qLizn33HMBOPfcc+nTpw87d/q/b6UUmzdv5sYbb2T58uWcccYZRNN/FiqfE7RPmwhlqgl0zaJVebg0LPowL6JrA9179+7dJJ10Idv215C9ZKNfm217yj2/w723IWCufepdjzlp4QfbmXL7Yqbcvpicj1ufp9ls9PoHX3PObYv5oqDUL0Kpo4WFYTY6Y/SgoOfFrNQxiMYRJoa2YWS4tDldUdc6lFKMGjWKGTNmsHz5co/5yCASjWPw4MFMnDiRnJwcZsyYQU5ODpMmTfIyU4Vq53Q6KSoqoqCggHHjxrFz505KS0s56aST/O6Xn5/P2LFjueiiixgxYgRTp05FqdZyqoE2+CUnJ/r1ZUWstYe2aA7ZSzZiKEFOl/a7NlxTknHvp97YQElVHT1P/ZZHIBj5hYw2s55+n4KDVcx46l2abc6A9zbuP/2JFWzdW4FLw+7DRzzngm2C87Sxu/zSbXQ0vppDdxcIZbWN3DrvC1656eyw5xXjmid//g0eeONLT9LL5KQE5t86JapWEQOJqgqTB9/4kqXrDnilRk5JTODq80ZG1ddxzTXXkJaWxksvvdTuvvLz85k5cyZHjhwhLS2NhQsXMm7cOAAuu+wynnjiCSZPnhy03RtvvMGf/vQnj0P88ccf5/LLL/e7180338wnn3xC37596dOnD08//TRTpkwBAm/wM16HItYRcCVVdZzyi9dosjnplZLEjgU3hvQZmK8xMK7VWnt2NC/5aIcnusgIUzWHq2qtPf30SE6gfn8uSUMngUpCAf/vOydTVH6MrXsraA6icfVKSeKT56dzx4urPf6Jp97YwLx3t5GgOoc56Xg2H1kJBPMxjebGlzdysLKeymM2MvqlcOKAXiQnJfDnayd5CQTA67hxTa+UBBpt3prhkNQefPDbqW0SHsGiqkRwhMmFT35EXlGt3/FTh6Wy6pHopSueOnUqjz76KN/97nej1mdHYqVd5O0uwaU1CUoxbtQQCvaVeV6fNmZoSK0j1oLjzrmref2DPGwOJylJicy65LSQmoP5GgPjWq01f393m2dHc2KCwqW1J0x1XMvvX/7wG2itPf0kJoDD4UQl+H8eoSZ/BYzJTGNXi1ZxxXdO5l9rd1nuhelorD7TaNCW1XkkfZXVNnLjyxstV/C+k77vit93wi+pafQSCIDn2JDUHnx3whDe3HjQcmxWAgFg3In9KCg5FvK9/WzKCObMspz/gyKCo4vs49Bak56ezsGDBy2dz10BK+2iqqYOl3ZPfj1Skmm22T2v0weE1jpi9azMphzzat5X67hz7mpee2+bV3bRs255nR0W1dcmjBjIvtJaL00kED2TE9HgdW+ttcfE19mxEghn/2oR2/ZW+LWNhbbx4BtfsmjtPq7/7igvrT+UQPE9X1bbyMV/+Jjyo80MSe3B4jvO9VrJmzHOX/viOsqPNpPRLwXAcsUf7sQObsEfq5k4MUGx+U+XRixcJeVIF0EpRXV1dbyH0WZsdocnXYg5fYixUnZpaDRtxnNpd7sTM1LD9nUEI9LwVCPENMFnnjb7DHwd4IbP4dzTMslv0RrME+edc1ez+3BNWONttjv9Jot4CQ1z2u07565mnkVyQF9sDqdfxtVIhEOkGoOhAQDc/5NTWLR2H1rD0nX7+fpgDfNvnYJGc/EfPqbiWDN/fOdrDlU18OTPv8Ejy7Z6fg9P70Pu7ir++M7X7CmrY395HdX19pZ7NDN73hfsKasLMIZmfvnKRsqPNgN4CRZfrSBcoQGxExrg/n9+6l/b26R1BEI0DiFqBNMuAhGO1hF26VgLzSAQVj4KM8YK2cqM9eDVZ/v5RIwaDqF8EfHCGPvGHcWWGgHAKVkZbH7l+jZrDZGs9M0TvKExWNn8DbPP/Fun8JeVO1n4mTtqLjEBDHdjAuACMvql4HJpjxBIVODUraYe43dX2akeTdqidYjGIcQcQ9swaxdNzfaQKymXhvqG5nbfP5BmEAhzRFQgG7zRp+HHsDmcLPowj4Ymu180ldbaUnsJh8QExfq513L2rQsgIfBXMiUpkYRERVMbSosa2oF54jcm6uHpfXhz40Fc/XtQXttk2cZs1vG17fsKAmMlD3hF9Zg3z9Y3OTyr9qXr9rNlb7XH5n/xHz/iuxOGsGVfaxTYBU+u5mhDq7ZqLt9t/OlrVnK2/PMZmoDx+3gTGuB+z9HctCyCQ4gK7vQh3l9IpSAjDB9GNAgVGmsmkEDwFTaPzF/rCXk1sDucXnmXbA4nCz/Y7nnnbZmTnC7NrD+/Byr4tiqbw0lSQgppI7PomZzA1FG9+OeafC8HvRVmwWie+J9fmc/GXVX8b4/bPFpW2+xn0vDNlGC8/uM7X/PpjnIqjjX7CYJ3cg95Ju2L//gRH/x2Khrt2Ty7dN1+7M7WD8rm0Hx9qDXwpKy2mbd8HMW+QkGInE17omcGF8EhhEWovRfuKn3ex6KlTYQiXEFgYBYyBlbC5r//2+enMTksJIN54k5OTCCpTx96pqXTMzmBjX+8hMGpPb3CuV0OB7XFh8FkJt55sNpbcChFamYmPXskc9rwAZw0pB9vbjzoia5yOjVvrt0dUmgY48v5pJCbp53FtS+u82gFKzYf9rx3g7dzD3HT1DE8smwrv75svJcfYcveagpLjqE1vL3xkGeln/P5Pszy1SQTKKtt5pFlW9lQWIHN7r6i2eH9GVrJ2uNPJ3BjRGl2VBRnWxHBIYRFaeVR6hqaKamstdQgJpx0YhxG5SaUIPB1mufuLPGbcA1Tztf7K7n4wXdY8vBl1De5TSNmP8ZJ177qN6mZZYnd6cJ+rI6U/qm4ElN4/t2d/PqH4702jzbW1ngJDUu0prGmhoT0DLbsO8JX+4943cvu0vQeciKzpozgof93Kje+vJG8Q7U0O1woBT86M5P/tAiGcSf2o7D0GLNfzfXSCqz88E6XZva8L9hbXsct877wDNPuoxWYP+5QAWTGOI4n2jvBdwbhEAwRHEJIfKOlohUFFS2CCQLw3xUezMH7rTuXcqSmjmv/uNLSj5GclOi5V0rfviQmKJqOHfOWA6ZJf9n6A1TXNXtW2wDO5vC0MHO7QCawf244SKPNyZZ9Rzz+Fa29J2sjumdPeWtaEqcm4LLe8E/UmnwKna8kUmzoLCv6zo4IDiEk3v4L7dE62pI6JBYEEwThOs3LahuZMWctX+9yZ9ytrm1NtWH2Y5gFlK2+nsSkJEvlwZj0m+0uPtha4jVH9x+a6X8BGnc0f+QYQuI49PlGDREYkSGCQwiKVbSUoXWEMl91BsJ1mj+/Mp8vvt4f0ITUZHf6R0xpTWKPHgEEQUsT3M5fIKDPw03X2PTXGZBJPv6I4BCCYhUtBZrD5TUcOdrQIeYrp9PFhfcui7jmRbhO87LaRt74bDe2+uDZbS1rTliYnVISE5j2zUxWbD5Ms8lEZWRU9vV5GGinnZ+dM4wXfnmuZW60WDHze+59FN9/bBWFEWxasyIlMYE+PZM4Uh9eFFQs8r0JsUfSqgtBCRQt5a7K522+ihW19TaPjyISrJzmdqeT8bNeY/uB1tDE51fmc6zqiKW2kTowlRNOGk3ayCwysrJIG+n9Y6Vt2JwuVn9dhsup/Y4vW3+Ap97J86TnN6MSk/nPVxWU1zaxeW+1pdDokRz9r+yy9QfIO1TDrgiFRlqfFL9jNqeLoWm9KP6/Kzh1WGrIPmxOV1TDRIWOIW4ah1LqYuAB4BQgDagA1gOPaa13mNqlAc8AlwO9gA3A3Vrrrzt6zB3Bpk2beP7553njjTfiPRTAOlqqNXGh+3VbUofU1tZy9913s2rVKg4dOhSwnc3hpL7JFvbGPoOSqjqWfLTDz2nucGocTgc3/uVDcl+Y7kmX7wjgsK471kBS/zTAO8zUCmP1/Osfjuec336A3UJFcblg9delATUJQysJZoqJRBvxNetYXetywe2v/Y/kxATL7M+b9lRbhoYOTetF3l9+FPDeYk7qvsTTVDUQ2Az8DbfQGAE8CGxUSp2utT6g3Il7VgCjgDuAI8BDwBql1EStdVF8hh47Jk+eHDWhUVhYyMyZM6mqqiI9PZ2FCxdaliQN1u7dd9/lkUcewW63M3DgQBYsWEBiz1SszFeR+DpSU1OZP38+F1xwQdB2pVX1njtZ+SgCpbnIXrKRxmYHMy46ldV7G2m2u0jCScXBItCabYUlbD9QzeLPD6Bd3g5rw9S0fFMRNocO2+lsrJ7NBb+s2pzcMuFaxeqHswIPpI2EY/u3utbmdLG/st7y+KY91SIAugDVDTaeWb2H+y8YQ1rvZKobbDz1wS6UUjx80cmk9U6O6v06Va4qpdQ4IB+4V2v9nFJqGvBv4Ada6zUtbVKBfcBirfWdofqMdq6qaNeYjiU/+MEPuOGGG5gxYwaLFy9m/vz5fPzxx2G3O3LkCCeffDLr169n7NixLF68mMWLF/P8S/+gwZSs0KB3j2Q/DWXPnj3ccsstXscuvvhi7rvvPgAuuOACVq9e7XXe5nCyv6SWYYP6UXComvLD+7n8T+sA/8y1VhlSzXmokhITGJA5DKdKoKGqkua61uR1p5x0IrWJvWlo9t+IkNYnhdp6W8gwVKvJOtLNW/PmzePmm2/mnnvu4bnnngtxR6E7Ykz04aZDSUpM4FfnZfHS2n04XZrKeju1TQ5SeyWR0TvZ8xrg0lMGcet5WRGPqSvlqjLyVBuz0k+AYkNoAGita5VS/wGmASEFR7SJdo1pg4qKCs466yzmzp3LtGnTAFiwYAErV65sU63x8vJytmzZwqpVqwC4+uqruf3226moqPCqAhis3f79+xkyZAhjx44F3MWfrrvuOgalJpOR4W/C+vDDD7lhxhU0NTVRX19PdnY2V155pZ9gCEVpVT11jTb2l/lPvmatI1ANeLNvw+F0cbS6ml6pA2j2cX7v2FvKgMxMZv3gZL+03Of89oOAQiPUyj7SFbpRinf8+PERXSfEH9+VvUZ7Vv7G3zd/eyQvrd3n1cYsJJISE8hM7UlhRWTVFp9ds4dDR5q8jtU2Oqht9M5ltrqgkulnZkZV64i74FBKJQKJwEjgT0ApsLTl9KnAdovL8oDrlVJ9tdbW+Y9jQKSJ9CJh0KBBzJkzh5ycHKZNm0ZVVRVPPvkkn332mafNVVddxe7duy2v37BhA7169fK8PnToEJmZmZ7ys4mJiQwdOpRDhw55CQ5zO2NfxoknutuNHTuW0tJS/ve///HNb37TY0I7ePAgGRkZXvfXWjNr1iy2bt3KoEGDcDgcNDQE/yLcdttt5OfnM3v2bB544AFGjRqFzeGk6mgjGmhqdvgZxMwb+6xqwN958clekVTg3m+B1v7Ob61pqKnxKwFsZWqKZfTPtm3uFOZjxoyJet9CePiaenyP3/ztkby6/oCfKajsmM2zsr/z7e2gobbJwa/fyWNSZn/ySuu4b/kObC0OMnMbM4Xlkder9xUagXA4NUu3HG6T1hGIuAsOIBc4q+Xv3bjNUkYx5IHAfotrDCNwGuAnOJRSNwM3A4wYMSJqA40kkV5bmDRpEo8++igA999/P3fddReZma2297ZoHpFg7MtwON2TbmpqKsuWLePuu++mqamJSy+9lAEDBpCcbL1yycjI4Pbbb2f69Olceuml9O/fP+j9XnrpJb8SuaVVrV8gpRQZqb3o7exP43/v8WoXqAZ8ZUm5XyQVWmNrbLQcg7O52SN0DKEQyA8Qq+gfYzFgVctdaDuBJn2rNkP69SCvtI5fv5PHY5eM9TMBGZO/cf6x9ws44rOyN6/0jzTY+WS324BiM0VV+GoDBrF0GGiir3XE3cehlJoA9AdGA/cCQ4DztNb7lVK7gE1a66t9rrkJeBUYobUOHJJD9HwcwWpMR0vr0FozcOBA3nnnHe677z5yc3M9GgNEpnGUl5czduxYqqqqSExMxOl0kp6ezq5du/xMVWPHjqWktIz8feXYHQ6mnjuRgvwChg49weseZWVljBw5kqqqKvr08X/PdrudNWvWsGTJEtavX09BQUFYhYkamu3sKqph9Imp7D58BJfpfzJBKZJtlZx26qnuMZhSgS//XxFNzTbqKivoO2gQPZKTqS0pprnJP0LKXEvCMEWZ91iYN+d1JFprTz13p9Pp+TvQCvh4w/w5mM1Axqo/mGB4ee1+/ruzgmFpPSk60sT3Tx5I+TGbp/3N3x5pKQCGpvaguDZwWpgh/VIo62LZehVwSYS+jmA+jrjv49Ba79Ra52qtc4CpQF/c0VXg1iyswnTSWn4fsTgXE4Il0osWSilGjRrFjBkzeOWVV7yEBrg1jq+++sryxyw0AAYPHszEiRPJyckBICcnh0mTJnkJDXO7//v7PwDNB+8tZ/yEU3Aod4x+aWkpAC6Xi4cffpjZs2d7hIbN4aTwUDV2h5P8/HwSExO56KKLePDBB6mvrw+7mt2BsmM4nE72ltRYnq+pa+aKZz+lvLbJk9bbCGltrK3B2dxMY02NJ/Hfr68/n7uuO5+BWVn8+vrzafzvPV5pSaxMUYbWEUuqG2w8tGInR0w5oIqL3Wa3zMxMj9AAWLa5mB2ldSzIPeh1jVUfbblvNN9DtPqxOmZ8Dku3HPb7TBbmFrGjtI5n1+zxtKlusHHvv/K4++3trC6sROM26Whgza5qT/u80jru/Xeen9AAggoNoMsJDXBrHfllkZvDAhF3wWFGa12D21xlGHvzcPs5fDkFONiR/o1QifSixfjx47n88suZPLn9ZR5feeUV5s6dy9ixY5k7dy6vvPKK59xll12GoYm9OPevvPbq/3H5pd9n2Ruv8+Dvn6Kqph673cnvfvc7JkyYwMknn0xKSgp/+tOfPH0YTuyS6nr+8pe/MH78eM4880xuuummoGY1s8BpaLbT0JKF1uF0eWkbAC6taWx2eGpALFm7h9qSEuoamvjgoe+RYHOboBJsjSQptzRYum4/y9a3Os3La71tweGaooyJbF9lQ0STZaDJ1TwJGhga5OjRo73u91HLpPdpy2TnO1ma+wiFecK991953PfvHSEn7EDHrd6DMVkb/YYjXJZtLvaYhozP1/zeqhts/Pqt7fx3ZwUaeH9HBasLKj0CIK+0jo93VXkJhvd3VHDnm9sprGhgT1UjDouNN0Z7AHuokLluwqj03qy4+Zu8cKXVVNo24m6qMqOUGgLsAd7QWt+ilLoc+Bfwfa31py1t+uMOx12itb4jVJ9drXTs1KlTefTRR/nud7/bYfc0l3w1CFXS1eZwkrevEpfWJCjFaaMySE4Kb/PfwbKjVNY2kDGgN/VNDhqaWldwvXumMGFE6z3tDhef5n7J9QsPkKigrqqKpmPH6NWvH2NO7EfB3jJsDicJCYoeffrSc2C6ezXUUrcimFM7lDnI19QRrqpvXGeYRgwzy80527A5NSmJinlXf4O03sm89tpr/PKXv+RXv/oVp02/j//urODE/j0oPuq96jXKoyZ43ldrH8HeT3WDzXNfow/wDtE0xnvJKYP4+ZlDPX0s3XzYc/zW87LYW1nP3e/sQON9/5fX7uf9nRWeftHw/s4K0nonM+eKU73MSvdfMIYjDTZPP+BvGlLAt0elsW5fhxkUOg2j0ntHdYJvD50yHFcp9S9gC7ANOAqMBe4GHIARzL4C907xxUqp+2jdAKiApzt6zLFGa82XX37JmWee2aH3bUsRJrMTG6Ckup4Rg4M7wwGvqKmq2kY/DaOhyU5js4NePdz/mmW1Ta01IewOmlr2YTQeO8bXdcc8XkWXS9NYV0dKaiokJnmOG05zc9SUgXn17CsQqhtsnlW/sUL9qKCSS8YPDuho9b3u013VaHCvznVrriunq9XJ+uGxE+iXOYajp1/pMa34Cg1onfDNu/UX5B70CKa03sl+76e6wcbdb+d5PmPzAnt1y3t5ae0+9lW7n8d/d1RwtNHh0U7W7T3iOT4lK40/fLDLazPmnW9vJ6N3MgeOtAYerMqv8JgojzTYufPt7bx45WleGkavpAQvZ7CvaUjDcSM0OpOgiIS4aRxKqQeAnwEnASnAIeATIFtrvd/UbiDwLO6UIz1xC5J7tNZbw7lPV9M4ugJmbcMgXK3jYNlRKo82Euz/ztA67A4XO4trKTu4l5mLDlBfVYmtLrh1MqVvX/qke4cKW2kd5pW41er95bX7WVVQ6VXxLylBcWJqDy/tw3eVb3VdcgKglFe5VHCvtA/XNHKseC/9M09CoSKOrlHA908eyOGaJvZVN2J3ahTw+GVjee7jPdQ2WVdZUsCwtJ4cOtKEwjqqJzGhtbZ3ryRFo6Ntc8U3R6Sy6WBtt63q11Un/1BETeNQSp0DXAKcAwzFnTuqEigAPgX+rbUOa6mgtf4z8Ocw2lUDN7T8CGEQ6zoZvtqGQSitw6NthFisNNvcDsuy2qZWjcLhCJm9Fqyz1Vr5L5ZtLvZavZu1DkNr8C0T63Bpj/axOr+CvZUNZKb29Kzyf37m0ADXgdXUXFzbjFIJ9Bt6ErRBaBi9frqrGhetidk18KdVu2kIYsQ3a1KB7mt2A7VVaAD872DsEmDGgu4qCKJJWIJDKTUTd6jsqbjNStuAXUAj7qins4HrgJeUUv8EHtda74vJiIWQxLpORn2T3dKJXd8Y3HlszjtlYOzVMAscu8PF7tJjOF3a096q3GpKUiKzLjkt4r00voLB4dJ8ZIpzNwuVQDhcUFBez64K93tanV/B+r1H/D4XiH39bGN+N98nmNAQ/BFhERkhBYdSaiswGFgIXA98pS2WjC05pH4EXAvkKaV+obVeFuXxCiEIp8xrezWSCSPT2zS2uiabn7ahtaau0Tu8say2ifpmB+l9ezBuaH92NvRm/OBebPMxU7U1qs1KMJi1jvzyej+twRdtug7cgsR3N7Avmak9qKizeW0IA8IOWxYiQ4RB7AhH4/gH8IrWOuj+dq11LfAG8IZS6hvACcHaC7EhUJlX3zbxqNzXv29vHDR7rYwV0L9vD89ru8NFdb3b3HSkvpkhLQ7tQOVhjdDPQKkirJzYVoLB4dKeOHerycbKd2HGOJqcqDixfw8OWqSDOFzbTFIbAuDNE2B1g40bFm+NuAZ4UgJcON7tl7ntn19zqCa8dBXxRib/zklIwaG1nhNppy2O67Cc10L0CFbm1dAswtFIoo3d4eJAZb2X6clAAw3NrSt1s19D65bXQQgUGRXoeHWDjd7JCbw+Y2LAqCgrgROOFgLuvECnntiPv/70dMBf4DjCmPGDTZbLNhdHLDSM++aX1VPdYOs0QkOEQtelTeG4LRrFONxRTl5orRe2d1BC2whU5tWsWYSjkUQbX9NTIAxto3V0bq1DBZiwzaGvZh9FoOMQXKAYOYuszhsTXDiah3FPjfZzlpsjuO6//36eeeYZnnvuOe655x7L/nzJb0MyPGidpF9eu5+kBOUXLXbh+Azyy+rZV+WfmDLUBH/X23mW16UkKj+zXDj9CZ2fSKOqBgDv4o6qAu9ADgMRHHEi1H6McDSSaGNlekoOYK8xaxsGWsOxAE73QJFRgY6HEih5pXXsLKuzPG8QjuZh3NO8d8P33K3nZXmy4loV1/Kl3uZgdWEF2T8eR++Utm+/Cmama+tkLkIgvhj/GxeOHeQO1Cis4NxRA/lsTxUouGTc4Hb9z1gRaW9PAenAd4G1wP8DanGHyk4Bpkd1dEJEWJV5NROORhIJhglqZEafsISBYXoalt7bsm2DRRp1DdhMEULmxHZWkVGXjB8cMGIqlEAxjvueN+M7SVqtts3+kmC+lMLCQiC8rLibi2ooOdbMxgPVHGt2cuHYQWFPBuaJRSb52GM1kRvPy3zO/PyM4+YJ/7uj0/3+drYElyQmKM+xo80Omhwu3tx6GI2iyeHiX1+XeEodby6q4TujMyxG2nYi2gColNoDPI7bCW4Hvqm13txy7mWgj9b6+qiOsJ3IBsBWdu4pCbtyXzgUVTVQVddMet8elsLA2MCnXRqnw0FiUhIJCYrxQ1MDChrLcZuelTkNSElts+UGPd/jiQr69kyiweb02oRnmI2Wbj7Mh/kVfjXFrTYGtoXP9layo6yOU4f09XyBtdb06NEDu91OY2MjPXu2Wn19J5d6m4MlWw57Jg2AsRm9wxYgVvcPh0CTnGCN8Xn165FEYWU9YzN6U1TbRIPdxalD+nLmsAG8va2YBruLXknK85kmJigG9EymsLKeRNVa2z7Q3wZpvZIskzT6kqjg2jOHRfwMo5kd90Rgr9baCTQB/Uzn3gF+GGF/gg+bNm3i2muvjUnfE046kbNOGeH3E47QMCcmBH8TlN3C67vrUBm/v/d2Lp5yOtrlwuV0huXwDoRvGhCr1XzZ0Wa/407troPgm/TOSNvxUWGl35fSOB9uMsF6m4Pl20tosDm8jr2zrZiCcncYcX5Fved8eXm5p467WWhAq3axuajG89o3rKCwssGrje84KuubPb8LWvwi5vuH815yDxzxu4fV+2wL0eon3H7bez/f641n++bWw6afYkqONVNY2aJRVjZ49tPkldWx7Ksiz+tGh6aqwU5Vg53yOhu7Wq4x/x8G+tsgHKFhXOv7f9JeIl1GlAIDWv4+gNs89UnLaylfFgUmT57sqbTXXgoLC5k5cyZVVVWkp6ezcOFCS3v6vffey9tvv83+/fv5+uuvOe200/zamDPhjhjcPywTVFKPPjz+9FxuvuZyAFxOFwmJ2iuKKhLMpibDoRsq6aA5tYjvd8/h0mw6WBtws5/ZrOSL72rcPNmfOWyAZ+VZXt+6R8Xl0ry1rZirzhjKrl27ABg5cqSfmaKqwX1NfkU9E4b0o6C8PuAYjTbr9lVz7qiBvLezjAa7i492VXCk0cFHuyo8Qkdr7TF1nTtqIOv2VVuaU4z3UnrMvTDIK6vjlCH9SO/Tw+t9mrWXSLUTox/j8wjnmkAmIPPfgcZnvt9lE4Z4vfcP8sstTUBmDa/e5qTJ4fJcb3zOkWCzzv4CxH6TaH55HWcNGxA1zTHSXj7HLSxWAouAR5VSWbgTE87EnZRQ6CTMnj2b2267jRkzZrB48WJuueUWPv74Y792l19+OXfddRff+c53LPvxTUyYkdrbK/rp4IF93PzwPfTpkYSxl+3iiy/mvvvu42DZUU8/CQrSeieFlQzRl1C7vY02vqG0oYRNoIigUJE/Zp9DTaPDa7K3OzUlx5opOeafvK/B7iL3wBEOtKRTnzBhgqcvY7I3cLo0qwrK/bQNM0ab2mYnqwrLPZOZ0Y+5P5d2r4IBz73e2lbMsNReXkLP0FDMd/2wsJwklUBVS6CCWWCZJ2xjYjU7Zo3J2fzauIfxeXxrZJpfG98JfUDPZM89jDGb//58XxV7q90JF/PK6hg1sDdfHKzB7nJ5PocGu4tVBRXUNju8fAJmfJ+DmQa7iw/yy7vcznxD64iWryNSH8dJwFCt9VqlVDLuGuE/B3oD/wXu0FpXRWVkUSLaPo6Silquf/AfLPrzDZyQEfkEGIiKigrOOuss5s6dy7Rp0wBYsGABK1eubFPJ2HArAJrJyspi5cqVfhrHwbKjVB11Z7LNXfcpr859lqbmZhobG7jz/ke44NKfoICBfXswOLUH+0tqGXViKhrI21fJ7Jk/5W8L/gmEnwzRTF7eDp7e0syxZofXPghfQWBOD244vQ1twyCQ3yKSFbOVz8HAKszQiqbqUp69axYP/nUxPQZkWJoiOgpzksORA3pyIIx9Hgr3QsCpYfTAXp4JGyC1RxK1LVrlqUP6ooEdZW5zXe/kBIal9mJXpXf6mZ5JCZ4J3GhjmHysxhnO8UDhwMcr6b2T+ek3MkM3bCFqSQ611ntw18tAa20HftPyc9yQPe991n+5h+x57/PCQz+PWr+DBg1izpw55OTkMG3aNKqqqnjyySf57LPPPG0iKR176NAhMjMzPVUEExMTGTp0KIcOHQooOKwwtA2X1mitefSBu1j6n4/IGHIiDqeTpkb3CtbYyFda5fCYtNCQ/diD7N+7m6d+fz8zb7qd4SNGhp2C3aDe5rRcAZpNSVahtqFSi5gJZOKwwsrnYBDuNNUjbQi/eGwOSf3TwxIaPZISaA5n92AbMN8+HKFhXGOM2yw0AI/QAPfK35xQpcHu8hMagNeqv8Hu8hMavuMM57gIDTeRCoxwiHQfRwZQFyr9SHelpKKWRStycWnNouUbeeimS6OqdUyaNIlHH30UgPvvv5+77rqLzMzWB94WzaO9+GbCHZA2kD8//jBX/exnXD/9Snr2bBVCRrp1w6TVIyWRBx7N5oFHsz1twkmGaMbhdNHU4pAPFuVkFWobaM9C7v4aZn7L4RXzbnYg+9qCfW3rwXwO4aKUYujocRBmniqHS3P9WcN4d2cZVW0o25raM5GjTc64pDa3CrEW2k4sBEGkhJPkMBF4BPg17igqp1LqP8CNLaVejxuy572Pq6XwuNPlirrWkZWVRVFREWvWrGHr1q28+uqrXucj0TiGDx/O4cOHcTqdHlNVcXExw4cPj2hM5ky4SikWv/MBm3LX8f7K5Tz71OMUFBR4kvT5Cpm+vVI4ZWRom6rD6aL0WDMn9OtBUqJ3oF91g701tXoAbSGQ/+OFK0/lPZOd3Ixba8DjWzA7kAM5VjcX1WB3asv+Yo0xrp9+IzOoqSwQgepyCPGhM0z+7SEcjWM28Hvc0VP/A0bj3vh3FPhFzEbWyTC0DZvd/QW02Z1R1zqUUowaNYoZM2awfPlyj5nJIBKNY/DgwUycOJGcnBxmzJhBTk4OkyZNishMBa2ZcO0OFx9v+JLvn/0Nzj51OlO/PZGpU6d6hIbZpAVuzaKqtpETB/YJ6c+obrDTZHdR3WBncL/WhIcOp4tjTa2bAq0c4hA42+0r6w4wPN0vKw4AO8tbTSi+DmRzFFG9zeGljSS1IZGtVQw+RJYV16VbtaFgpjKh4+nqQqAthCM4bgLmaa1vMQ4opW4B/qqUukVrbQt8affBrG0YxELrGD9+PFOmTGHyZEufVES88sorzJw5kyeeeIK0tDQWLmzNBnPZZZfxxBNPMHnyZO68807eeecdSktLueCCC0hPTycvL8+rr7LaJl59+UVunbWeAan96NOnj5cgC1bcaWh634AahVk4HGtyMLB3sqdNdYPdb3q00joCmaQOHmkkc6C14Ahlalq9q4IfnXICb28r9kzSLpcm1D97sEnks72V5JfXWd47QcGEwe7PKZApytA6yo75p5Yx3/vNrYfbZM6yGlNyYux8K52N41EAtJVwBMdo3EWczCwDXgZG4i7o1O3J3bbfo20Y2OxONm7dF9X7lJWVMXv27Kj0NX78eHJzcy3Pvffee56/X3zxRV588cWA/Rib/X6fPYcEheXO72DFnQJpFOAtHHTLa6NNk8NluffCd2+FETYbLDIqUvPOkUYH6/ZVeYVdmq80JvpIwhsDTfjgFmSlx5o9E5fV5O/bJhC+560EVjiCyqWhb0oiv/jmCM+xcIRSj0SF3aXb7QcC78k8EoEoQiC2hAzHVUq5gHO01l+YjiXiTjkyWWu9JbZDbB9dKeWI1pr09HQOHjxI3759O/z+Dc12dhXVMHZYGr16tE68RVUNVNe5920YYbdBU4xoPAJGKThQ3YgLd5qCkQN7eTQKh9PlOWfg2wbCf1bBUmsEW+2De7ypPZOobXLg0oFDPM0kJiiunZQZ8aaqhx9+mOzsbB577DFPMEQsCTThxmpyDXS/QCY7meQ7J9EIx81USo02vU40Ha8xN9Ra7418iAK4bd7V1dWhG8aIA2XHcDid7C87yoQR7qSHgVKdW2W5tdpNnpKSGFCjsDJF+bYJhXnXdbDIqGCrfXCvrs2+jnAWy1aO9HAwsuKOHTs2ouvaSkdPyiIEuj/hCo5AXtl/WxyLbVUgISY0NNtpaHKvEhua7DQ2O+jVIylgqnPfFCNWAqa6vplezmRPyKmvH8PKFKWBRruLoppGS5+IL+Zd18Eio0JNZqE0EisM01GkRJIVVxA6I+EIjuMmcqor0N564YE4UHYMc4EnQ+sIlOrcN9+UlYBBg93uJNm08jdrFCPSemFF+bFmjjY5Qmoe5ogn38goK60jGKE0krb4NKzQWlNUVATAmDGS3k3omoRTOvb1jhiIEB7RqBduczg9aUGSkxK9tA0DQ+sYG6Rin1f7AALG6dQk+xzzzQ1kxirKKhBBd3BHaEYyayTBHNPtpaqqisbGRnr16sXAgR1X710Qookk2e9ChFsv3Fcw+OKb6dZb2zDQXr6OYJv0gLAFTCisoqxcWrN8e4lXtJShbYSKUmoLsbTRGxs4hw0bFrN7CEKsCVmPQym1XCk1KdwOlVI9lVL3KKWiE1MaQyJJ8NgZsKoXbtnOJBh88c10a3c4aQ5Qo8B83BxS214cTrcPw+F0+R03b/jTwNFGG80Ol19dCCttI0G5k+rNnpLFdWcNIyUxIer1HtqLkU69oxzjghALwtE4DgIblVJf4a789zmwTWvt+UYqpYYC3wJ+DFwBHMZdTrbT0rNnT0+dikh28MaLcOuF+woG353bvhv1SqrrmThmcNB7B9uk1xYC7evw0ja0RjsdHKuuZP9RJyR6+y2sfBJmLSOSpIUdSUFBAQBnnHFGnEciCG0nHB/HHUqpObhzVT0GpAJaKXUUaAbSgGTcoe9ftLRbpLXu1NtNhw0bRlFRERUVFfEeSlhU19ZT1+BvevmitoyBqX1a2x1toq7JhrHp4osjxQzs59497XS6KK6qw6xoVRTD0Yq+JCYEFgTHmhw02Z2efRxVRYn069k2K6fLpalqsKE1FCuo6J1CQoJbcFc32D1aiAaanbC5zEn+Mfj2GO9CRD+cMCSg49s3TUg0C9i0FyMUd9y4cXEeiSC0nbC+TS3p1O9QSv0GdyGns4GhQE+gCsgHPtNaH4jVQKNNcnIyo0aNivcwwubs6X9iW0GR3/Ezxg0jd+mDAJRU1fHtB16jyVRqrFdKEjsW3MgJA/tw59zVvP5BHjZH6/mUpERmXXIaL9w+1fK+1Q02HvGraeFg3tXj21SL++W1+1lV0IDDpVvqaQzg+m8Nc+/4Pn0Qm4pqPGGxCkgfAt8e4r7WXIgomCZhNmO1da9FrDA0DgnFFboykdbjsAGftvwIHYghHIKRvWQjPum0cLo02Us28sLtU8ndWeIlNMBt2tq4ozhgn5HUtADrKnzmc1ZZbLPSe3qq6e2pavTcL5gHKpAm4es0b0tobiyRUFyhO9B2Q7XQ6QglGHL/dh37l93KCWNGkzYyixPHjObAsl+R+7frAvYZKIFgoFrcyzYXs6O0jqVbDluesxJC7+1wmwsLKxvCzvpqaBK+WDnNA7XtaKqrq2loaCAlJYUhQ4bEeziC0GbivwQTokYwAWDw/Mp8DO+TywXPv7uT7GsCB80Fq7vti1UVPrPWEUgIHalvjdQKlPX1sglDvJIUBtIkQjnN44kRUTV06NAuEZAhCIEQwdENKatt5NZ5X/DKTWczOLWn1/FlGw5ga3FA25wulq0/wN0/nODVrq1YVeEzm7NeuPJUryy1CcrtxzAnvguUOPCzvZUBNYlIUovEE2MPh5iphK6OmKq6Ic+vzCd3dxXPv7vT77hvrJuhdYBbY3hoxU6OhLlXw9w+kP/Cty+zKcml/bOlBjIrdWZNIlyMHFUSiit0dSKtOX438J7WuiBG4xHaiaFVaI2fNrF5b7VH2zCwOV1s2uPOyGv2T1g5vn3x8mdofzOTr9YRare3cY2VMOjMmkS4bN26FXDXSRGErkykpqrngAFA7IsICG0imA9j1SPWIbcQ2j8Rqv0J/XuEdKIH2u0djeSBXQEJxRW6C23xcVzTkk4kDSjFnVr9Va319mgOTIic9vgwQvknQrU/9cR+/PWnpwcfXzcwN7WHQ4cOoZQSH4fQ5WmLjyMLd9qRZ4DVwE+B/3WF3FSdmZKKWi68cU5LPqrAlNU2csWzn1Je2+R3LpQPIxDh+ifa2r7e5mD59hJ+OGEIs6dk+f10BzNUKI4cOUJDQwOJiYmS4FDo8rRFcDyutb5Sa/1brfUNQCbwJPBXpdRl0R3e8UP2vPdZ/+Uesue9H7RdIMc3hPZhBCLYJr9otDfnjTpeMSKqhgwZQkKQ9C6C0BWI1FRlBzaYD7TkpHpKKZUJPAC8F6WxHTeUVNSyaEUuLq1ZtHwjD910KSdk+KcpD+b4huA+jGBEuskvkvadOW9UR2IIjtGjR4doKQidn0i/wQeBs4CPLM4tB2a2e0THIdnz3sfVkivE6XKRPe99Xnjo537tIt28Fy6RbPIz2pv3YwTaewGdO29UR1JQUIDWmtNPD+4HEoSuQKQ68zvA75RSP7A4dxLQqTPidkYMbcNmd6cKsdmdLFq+0c/XEcjxbeXr6AisBIIvgfJGdbYaGR2BkRV3woQJcR6JILSfSAXH47hNVR8qpd5XSt2plPqpUuoR4M/Ax+F2pJS6Sin1tlLqgFKqUSlVoJTKVkr182mXppT6u1KqUilVr5RarZTqNss2s7ZhYGgdZtrq+I4F4QqEzpw3qqMpKCiQiCqh2xCR4NBaN2itLwbuBNKBOcAy3AJlF3B7BN3dCziBh4FLgJeBW4FVSqkEAOVO6LOi5fwdwJW4a3+sUUp1+tCUcCKlcrft92gbBja7k41b93kda6vjOxKM6KdQGkG4AuF4D781c/DgQRISEkRwCN0C1Z7yqUqpwcBIoEZrvSvCawdprSt8jl0PvA5M1Vp/rJSahnufyA+01mta2qQC+4DFWus7Q91n8uTJetOmTZEMLWrc+dRSXntrHb/86XmWPot4ESj1+Wd7K9lRVsepQ/w35NXbHO6aGWMH8e7OMqosQm/TeycfF6G1kVJTU0NGhvvzbGxsJDk58jomgtDRKKU2a60nW51rV3iL1rocKG/jtVal9/7X8tuYfX4CFBtCo+W6WqXUf4BpuDWfTkm4kVKxIFhNDLBOLRIq+skcUhtMOJgFzPEYPWXFnj17AEhPTxehIXQLOltA+fdafhuG+1MBqx3pecAIpVTfDhlVG7CKlOoogtXE8E0VYmzaC+bs9hUqwUxZsmfDn127duFyubpUxUlBCEanERwt+0CeAFZrrQ3b0kDgiEVzw7Cf1hFji5RwI6Xa1HdVHRfeu4zSaus9FoEEg4FVapFQzu5wIqggMgFzPOFyudBac95558V7KIIQFTqF4GjRHJYDDuAX5lNYVxANWgVHKXWzUmqTUmpTRYWVRSy2hBsp1aa+l2xkfd5hspdstDxvJRgMAqUK+WxP4FoXkYTUhitgjjeuvvpqampqePrpp+M9FEGICnEXHEqpnrgjp0YDF2uti0ynq3FrHb4YmoaVNoLW+lWt9WSt9eRBgwZFdbzhEG6kVKSUVNWxcFUeLg0LP9zup3WEyiEVKFXIRwVVAaOfwo2gkj0bgVFKkZqaKqlGhG5DXL2XSqlk4G3gW8AFWuuvfZrkARdZXHoKcFBrXRfjIbaJ3KUPxqTf7CUbcbRUPrI7NdlLNvLC7a1pRoLlkLr1vKyAqULsTpg9Jcvynm9uPRxWSG0wAXM87hQXhO5M3ARHy16NN4CpwA+11la2lxXAL5RS39Naf9pyXX/gx8CSDhtsJ8DQNpwtezmcThcLP9zOQ9ecwwkD+wChc0hFmloEwi+gJHs2BOH4IZ4ax0u4U7L/EahXSp1jOlfUYrJagXun+mKl1H24TVMP4fZxdCuDcUlVHddnv8uih3/kEQRmzNqGga/W0RbBEC1k/4YgHD/E0+h6acvv3+IWDuafX4In8+6PgFXA34B/4d5tfr7W+lBHDziW/H7BOj7ffpjfL/jc8vz6vGKPtmHgdLpYt906lbkgCEKsiJvGobXOCrNdNXBDy0+3pKSqjpyP3VtXcj7ayROzzvPTOm689rusKqj0MkUlJSguHB+5/0A26QmC0B4kzKMT8PsF6zzahMPpstQ6Iq2ZEQzZpCcIQnuQ5WacMWsbBlZaRyT+i2AahRRWEgShvYjGEWfM2oZBIK0jXIJpFLJJTxCE9iKCI858sOmA5fH//s/6eCiCpf2QTXqCIEQDsVHEmXGnnISjqNb/+LDUNvUXrFSrbNITBCEaiOCIM6semRq6UZgE0igMP4Zs0hMEIRqI4OhGhNIoZJOeIAjRQHwc3QjRKARB6AhE44ghodKIRBvRKARB6AhE44ghoWpnCIIgdEVEcMSIkqo6FrXUzlj0YV7Ain2CIAhdDREcMSJ7yUaMIoBOl46a1lFvc7B8e4nsvRAEIW6I4IgBhrZhc7TUHHc4Q2odRTWN3Lx0K8U1jUH7ljxTgiDEGxEcMcCsbRiE0jr+b90BSo/aeGVd4B3jwXaFC4IgdBQiOGJA7s4Sj7ZhYHM42bij2LJ9UU0jXxcfA2Bb8bGAWofkmRIEoTMggiMG5P7tOu667nyGjB5N2sgshoweza+vP5/cv11n2f7/1h1At+y/0BpLrUPyTAmC0FkQwREDymobWbbhALaWrLc2p4tl6w9QXtvk19bQNox9exprrSPYrnBBEISORARHDHh+ZT7ax8fhcsHz7+70a2vWNgystA7ZFS4IQmdBdo7HgM17qz3ahoHN6WLTnmq/tgePNProEW6t48ARb41DdoULgtBZUNp3udvNmDx5st60aVObri2pqOX6B//Boj/fwAkZ/aM8MkEQhM6LUmqz1nqy1TkxVQUhe977rP9yD9nz3o/3UARBEDoNIjgCUFJRy6IVubi0ZtHyjZRWHo24j+oGGw+t2MmRBnsMRigIghAfRHAEIHve+7hadvE5Xa42aR3LNhezo7SOpVsOR3t4giAIcUMEhwWGtmGzt6QMsTsj1jqqG2x8VFiJBj4qqBStQxCEboMIDgvM2oZBpFrHss3FXpv1ROsQBKG7IILDgtxt+z3ahoHN7mTj1n1hXW9oG44WyeFwadE6BEHoNsg+Dgtylz7YruvN2oaBoXXcel5Wu/oWBEGIN6JxxID88nqPtmHgcGnyy6SYkyAIXR/ROGLAC1eeGu8hCIIgxAzROARBEISIEMERhJKqOi68d5nUCxcEQTAhgiMI2Us2sj7vcNTqhQuCIHQHRHAEwKgb7tKErBceiHqbg+XbS6TYkiAI3QoRHAHIXrIRZ0tklNPlapPWsbmohpJjzVJsSRCEboUIDgsMbcPuaKng53BFrHUYpV5BSrwKgtC9EMFhgVnbMIhU6zCXepUSr4IgdCdEcFiQu7PEo20Y2BwuNu4oDut6Q9sw56oSrUMQhO6CbAC0YEX2VZzz2w9otrcKj57JCfznj5eEdb1Z2zAwtI7vjM6I6lgFQRA6GtE4LHh+ZT7aW+HA5YLn390Z1vVlx5otc1WVHmuO0ggFQRDih2gcFmzeW43N6WOqcrrYtKc6rOt/+o3MWAxLEAShUyCCw4JVj0yN9xAEQRA6LXE1VSmlhiml5iqlNiilGpRSWimVZdEuTSn1d6VUpVKqXim1Wil1ehyGLAiCcNwTbx/HGOBnwBFgrVUDpZQCVgCXAHcAVwLJwBql1LAOGqcgCILQQrwFx2da6yFa68uANwO0+QlwHnCd1jpHa/3flmMJwP0dNE5BEAShhbgKDq19Y5cs+QlQrLVeY7quFvgPMC1WYwN3CdiHVuyUkq+CIAgm4q1xhMOpwHaL43nACKVU31jdeNnmYnaU1rF0y+FY3UIQBKHL0RUEx0DcPhBfjNjYtFjctLrBxkeFlWjgo4JK0ToEQRBa6AqCQ4HPNuzW49YXKHWzUmqTUmpTRUVFm266bHOxV8oQ0ToEQRDcdAXBUY1b6/DF0DT8tBGt9ata68la68mDBg2K/IYt2oajRXI4XFq0DkEQhBa6guDIw+3n8OUU4KDWui7aNzRrGwaidQiCILjpCoJjBZCplPqecUAp1R/4ccu5qJNfXu/RNgwcLk1+mdQeFwRBiHvKEaXUVS1/ntXy+1KlVAVQobX+FLdw2AAsVkrdh9s09RBuH8fTsRjTC1eeSr3NwZIth3FqTWKC4tpJmfROifvHJQiCEHc6w0zou/Hvby2/PwW+r7V2KaV+BDzbcq4nbkFyvtb6UKwGZVWISVKiC4IgdALBobUOGB1lalMN3NDyE3MCFWI6a9gA0ToEQTju6Qo+jg4nWCEmQRCE4x0RHBZIISZBEITAiN3FAinEJAiCEBjROARBEISIEMEhCIIgRIQIDkEQBCEiRHAIgiAIESGCQxAEQYgIERyCIAhCRIjgEARBECJCBIcgCIIQESI4BEEQhIgQwSEIgiBEhAgOQRAEISJEcAiCIAgRIYJDEARBiAgRHIIgCEJEiOAQBEEQIkIEhyAIghARIjgEQRCEiFBa69CtujBKqQrgQBsuTQVq23jbSK8Nt32odsHOBzoX6HgGUBnGmDqK9jyPWPUbi+cci2cc6Fxne8YQm+fc2Z5xOO06w3d5pNZ6kOUZrbX8WPwAr3bUteG2D9Uu2PlA54Ic3xTvZxCt59GVnnMsnnGgc53tGcfqOXe2Zxyr59yR32UxVQXmPx14bbjtQ7ULdj7Qufa8z44kVuPsbM85Fs843Ht3BmIxzs72jMNp16m/y93eVCW0DaXUJq315HiPQ4gd8oyPD2LxnEXjEALxarwHIMQcecbHB1F/zqJxCIIgCBEhGocgCIIQESI4BEEQhIgQwSFEBaXUw0qpAqWUSyl1ebzHI7QfpdRJSqnPlVKFSqkvlVLiSO9mtPV7K4JDiBYfAZcBn8V7IELUeAVYoLUeC9wPvKGUUnEekxBd2vS9FcHRDVFKDVNKzVVKbVBKNSiltFIqK0Db4Uqpt5RStUqpo0qpd5RSIyK9p9Y6V2u9p92DF9pMNJ+7UmoQcA7wOoDWelXLqbNi/T6EwET7u93W760Iju7JGOBnwBFgbaBGSqnewMfAeGAmcB1wMrBGKdWnA8YpRJdoPvcRQLHW2m669EDLcSF+dIrvdlJ7OxA6JZ9prYcAKKV+CVwUoN1NwGhgnNZ6d0v7bcAu4BbgLy3HthB4wpiktT4UxbELbSeqz90CMVPFn1g/47AQjaMborV2hdn0J8BG4x+r5dp9wDpgmunYmVrrjAA/IjQ6CVF+7geBoUqpZNN1I1uOC3Ei2t/ttiKC4/jmVGC7xfE84JQOHovQcYR87lrrCuALYBaAUupC3BrH5o4ZotBOYvrdFsFxfDMQt63Ul2ogLZKOlFK/U0oVAVOAvyulipRSJ0RhjEL0Cfe5zwZ+oZQqBJ4BrtWSaqKrENYzbuv3VnwcgtVEELEtW2v9B+AP7R+O0EGEfO5a613AtztmOEIMCOcZt+l7KxrH8c0R3CsTX9KwXq0I3QN57t2fmD5jERzHN3m4baG+nALs6OCxCB2HPPfuT0yfsQiO45sVwDlKqdHGgZbNROe2nBO6J/Lcuz8xfcaSVr2bopS6quXPqbidnL8CKoAKrfWnLW36AFuBRuB3uG2iTwL9gDO01nUdPW6hfchz7/50hmcsgqObopQK9GA/1Vp/39RuBPA8YIRbfgT8Wmu9P9ZjFKKPPPfuT2d4xiI4BEEQhIgQH4cgCIIQESI4BEEQhIgQwSEIgiBEhAgOQRAEISJEcAiCIAgRIYJDEARBiAgRHIIgCEJEiOAQBEEQIkIEhyAIghARIjgEIQoopforpR5TSk0Is/1cpdR/Iuj/bqXUNqWUfGeFuCP/hIIQHSYDjwLJoRoqpU4CbgEej6D/V4DBwMw2jU4QoogIDkGIDpOAZsKrdfBrYKvWelO4nWutG4GFwL1tGp0gRBERHILQTpRSO4FngR6AXSmllVJvBWjbA5gBLPE5PlYp9S+lVLlSqkkpdVAp9aZSylzeeSlwilJKyrkKcUVqjgtC+7ke96SeBzzVcqwkQNtzgAHAWp/jK4Ea4FagEsgELsN7cfcVcBS4BFjf7lELQhsRwSEI7WcrMAyYq7XeGKLtObiL6mwzDiilMoCTgWlaa3N1Ni+tRGvtUkpta+lDEOKGCA5BaD+nAinAljDaDgWOaq1tpmNVwF7gT0qpIcAnWutdAa6vAMa2Z7CC0F7ExyEI7edM3FrEV2G07Ynbie5Bu6upXQhsArKBQqXUXqXUrRbXNwK92jVaQWgnIjgEof1MAvZorY+G0bYKSPM9qLXeq7W+HhjU0t/HwN+UUpf6NB2I2wciCHFDBIcgtJ9TCC8MFyAfSFZKDbM6qd18BdzTcug0nyajgIK2DFIQooX4OASh/dQAZyqlLgZqgV1a66oAbT9r+f0toAhAKXUG8AKwDNgNJAKzAAduzYOWdgNw+zeejfYbEIRIEI1DENrP74Ey4N/ABiBg2hGt9X7gC+DHpsOlwEHcWsYKIAe3E/1HWuvNpnY/BGzAv6I3dEGIHOX2ywmC0FEopWbh1jBO1Fo3RHDd+0Cl1vq6WI1NEMJBBIcgdDBKqUTga2C+1joss5NSaiKwEThNa707hsMThJCIqUoQOhittRO4AQhb2wBOAH4hQkPoDIjGIQiCIESEaByCIAhCRIjgEARBECJCBIcgCIIQESI4BEEQhIgQwSEIgiBEhAgOQRAEISJEcAiCIAgR8f8BazFKTJrsq78AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Generating plot of experimental data - Not a part of PINN process #\n", + "\n", + "plt.rcParams.update({'font.size': 16})\n", + "cmap = plt.colormaps['Blues']\n", + "\n", + "for itr in range(0,5): \n", + " \n", + " t_expt = []\n", + " gammadot = []\n", + " shear_stress = []\n", + " \n", + " startUpFlow = pd.read_excel('DOW1_030323.xlsx',header = None, names=['shear rate','t','shear stress'], sheet_name=itr, skiprows=range(155,298)) \n", + " # Reading data from sheet number - itr\n", + " t_expt = np.array(startUpFlow[\"t\"])\n", + " shear_stress = np.array(startUpFlow[\"shear stress\"])\n", + " \n", + " if itr==0:\n", + " plt.plot(t_expt,shear_stress,color=cmap(itr+100), label='$\\dot\\gamma = 0.06~s^{-1}$',marker='^',linestyle = 'None')\n", + " elif itr==1:\n", + " plt.plot(t_expt,shear_stress,color=cmap(itr+150), label='$\\dot\\gamma = 0.07~s^{-1}$',marker='^',linestyle = 'None')\n", + " elif itr==2:\n", + " plt.plot(t_expt,shear_stress,color=cmap(itr+200), label='$\\dot\\gamma = 0.08~s^{-1}$',marker='^',linestyle = 'None')\n", + " elif itr==3:\n", + " plt.plot(t_expt,shear_stress,color=cmap(itr+230), label='$\\dot\\gamma = 0.09~s^{-1}$',marker='^',linestyle = 'None')\n", + " else:\n", + " plt.plot(t_expt,shear_stress,color=cmap(itr+300), label='$\\dot\\gamma = 0.1~s^{-1}$',marker='^',linestyle = 'None')\n", + "\n", + "#lt.arrow(0.5,10,-0.1,15,fc=\"r\",ec='r',head_width = 0.2,head_length=0.2,width=0.02) \n", + "plt.arrow(0.5,20,0.2,24, fc=\"k\",ec='k',width=0.01,length_includes_head='True',head_length=5,head_width=0.2)\n", + "plt.legend(loc='upper left',prop={'size': 11})\n", + "plt.xscale('log')\n", + "plt.ylim([5, 45])\n", + "plt.xlabel(r'$t$ (s)',fontsize=16)\n", + "plt.ylabel(r'$\\sigma$ (Pa)',fontsize=16)\n", + "plt.savefig('Shear_stress_experiment_DOW1_030323.eps',format='eps',bbox_inches='tight')\n", + "plt.show()\n", + "\n", + " \n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1071ce1e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consistency Index: 104.02946042671768\n", + "Power law exponenet: 0.8844988835126537\n", + "R squared: 0.9965229133378861\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Generating plot of experimental data - Not a part of PINN process #\n", + "from scipy.optimize import curve_fit\n", + "from sklearn.metrics import r2_score\n", + "\n", + "def hb_func(x,k,n):\n", + " return 9.5 + k*(x**n) \n", + "\n", + "plt.rcParams.update({'font.size': 16})\n", + "cmap = plt.colormaps['Blues']\n", + " \n", + "startUpFlow = pd.read_excel('DOW1_030323.xlsx',header = None, names=['shear rate','shear stress'], sheet_name=6)#, skiprows=range(0,10))\n", + "gamma_expt = np.array(startUpFlow[\"shear rate\"])\n", + "shear_stress = np.array(startUpFlow[\"shear stress\"])\n", + "\n", + "popt, pcov = curve_fit(hb_func,gamma_expt,shear_stress)\n", + "\n", + "k = popt[0]\n", + "print('Consistency Index: ' + str(k))\n", + "n = popt[1]\n", + "print('Power law exponenet: ' + str(n))\n", + "\n", + "fit_y = hb_func(gamma_expt,k,n)\n", + "r2 = r2_score(shear_stress, fit_y)\n", + "print('R squared: ' + str(r2))\n", + "\n", + "plt.plot(gamma_expt,shear_stress,'^b',label='Experiment')\n", + "plt.plot(gamma_expt,fit_y,'-k',label='HB fit')\n", + "plt.xscale('log')\n", + "plt.xlabel(r'$\\dot\\gamma$ (1/s)',fontsize=16)\n", + "plt.ylabel(r'$\\sigma$ (Pa)',fontsize=16)\n", + "plt.legend(loc='upper left',prop={'size': 16})\n", + "plt.savefig('DOW1_FlowCurve_HBFit.eps',format='eps',bbox_inches='tight')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44a2aecc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}