{ "cells": [ { "cell_type": "markdown", "id": "d27176ab-c0a7-44ea-82c3-ed9566c50cfc", "metadata": {}, "source": [ "# Using UniaxialExtension" ] }, { "cell_type": "code", "execution_count": 1, "id": "2cc565b9-52bf-4f30-ae39-99774c9edc70", "metadata": {}, "outputs": [], "source": [ "import pymecht as pmt\n", "import numpy as np\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "id": "aa8b391a-b2ba-4745-af3d-fda6b26165a9", "metadata": {}, "source": [ "The `pymecht.UniaxialExtension` module simulates a specimen made up of any material under uniaxial extension conditino. It can calculate deformation given a force, or force given a deformation. " ] }, { "cell_type": "code", "execution_count": 2, "id": "e9a38f88-af0f-4f36-bc7d-bef07cabfc46", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mInit signature:\u001b[0m\n", "\u001b[0mpmt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUniaxialExtension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mmat_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mdisp_measure\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'stretch'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mforce_measure\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'cauchy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "For simulating uniaxial extension of a sample\n", "\n", "+-----------+-----------------------+\n", "| Parameter | Description (default) |\n", "+===========+=======================+\n", "| L0 | Length of sample (1) |\n", "+-----------+-----------------------+\n", "| A0 | Cross-section area (1)|\n", "+-----------+-----------------------+\n", "\n", "Parameters\n", "----------\n", "mat_model: MatModel\n", " A material model object of type MatModel\n", "\n", "disp_measure: str\n", " The measure of displacement with the following options:\n", " \n", " * 'stretch' : The stretch ratio (default)\n", " * 'strain' : The Green-Lagrange strain\n", " * 'deltal' : The change in length\n", " * 'length' : The length\n", " \n", "force_measure: str\n", " The measure of force with the following options:\n", "\n", " * 'force' : The force per unit area\n", " * 'cauchy' : The Cauchy stress (default)\n", " * '1pk' or '1stpk' or 'firstpk' : The first Piola-Kirchhoff stress\n", " * '2pk' or '2ndpk' or 'secondpk' : The second Piola-Kirchhoff stress\n", " \n", "\u001b[0;31mFile:\u001b[0m /usr/local/lib/python3.9/site-packages/pymecht/SampleExperiment.py\n", "\u001b[0;31mType:\u001b[0m type\n", "\u001b[0;31mSubclasses:\u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pmt.UniaxialExtension?" ] }, { "cell_type": "markdown", "id": "4260c212-792d-4a6d-b6e4-26940d76dbad", "metadata": {}, "source": [ "For details about the material options, please refer to the [MatModel module](https://pymecht.readthedocs.io/en/latest/matmodel.html). The default settings for displacement and force are `stretch` and `cauchy` stress respectively. For details about the description of different options in displacement or force, please refer to the [pymecht.SampleExperiment.UniaxialExtension](https://pymecht.readthedocs.io/en/latest/sampleexperiment.html#pymecht.SampleExperiment.UniaxialExtension)." ] }, { "cell_type": "markdown", "id": "ec89997d-adbe-478c-8fc8-f198cc86466e", "metadata": {}, "source": [ "## Displacement controlled" ] }, { "cell_type": "markdown", "id": "7eb52d6f-bd75-4db3-babe-ca70c8b1b294", "metadata": {}, "source": [ "Firstly, we begin by calculating the force given a deformation. The `pymecht.UniaxialExtension` module offers four displacement measurement options: stretch, strain, length, and deltal. The default setting is `disp_measure = 'stretch'`. We can change the displacement measurement setting using the `disp_measure` option. \n", "\n", "For calculating the force measure at a given deformation, we use `pmt.UniaxialExtension.disp_controlled`. \n", "\n", "***pmt.UniaxialExtension.disp_controlled(self, inp, params=None)***\n", "\n", "It has two inputs: *inp* and *params*. *inp* represents the given deformation data, *params* represents the parameter values of sample. The process is shown below." ] }, { "cell_type": "markdown", "id": "16e2658b-2970-4050-b186-fe23a6c953b6", "metadata": {}, "source": [ "The whole process of simulating uniaxial experiment can be divided into 5 steps: \n", "1. Define material \n", "2. Set sample and measurement option \n", "3. Set parameter values \n", "4. Give measurements value (force or displacement) \n", "5. Simulate \n", "\n", "In this example, the material properties are defined as a combination of the Neo-Hookean model and the Gasser-Ogden-Holzapfel model. The displacement measurement option is set to `disp_measure = 'length'`, and the force measurement remains at the default `force_measure = 'force'`. For the uniaxial extension simulation, the fiber direction is set along the extension direction using `pmt.specify_single_fiber`. For more details about fiber direction, please read [Setting fiber directions](https://pymecht.readthedocs.io/en/latest/Examples/matmodel-example.html#Setting-fiber-directions)." ] }, { "cell_type": "code", "execution_count": 3, "id": "c4ea1435-bdc1-49c4-9628-d264b1d7419f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fiber directions set to 0 degrees ( 0.0 radians)\n", "An object of type UniaxialExtensionwith length as input, cauchy as output, and the following material\n", "Material model with 2 components:\n", "Component1: NH with fiber direction(s):[array([1., 0., 0.])]\n", "Component2: GOH with fiber direction(s):[array([1., 0., 0.])]\n", "\n", "------------------------------------------------------------------\n", "Keys Value Fixed? Lower bound Upper bound \n", "------------------------------------------------------------------\n", "L0 1.00 No 1.00e-04 1.00e+03 \n", "A0 0.10 No 1.00e-04 1.00e+03 \n", "mu_0 5.00 No 1.00e-04 1.00e+02 \n", "k1_1 5.00 No 0.10 30.00 \n", "k2_1 15.00 No 0.10 30.00 \n", "k3_1 0.10 No 0.00 0.33 \n", "------------------------------------------------------------------\n", "\n" ] } ], "source": [ "#define material\n", "mat = pmt.MatModel('nh', 'goh')\n", "\n", "#set sample and measurement option\n", "sample = pmt.UniaxialExtension(mat, disp_measure = 'length')\n", "pmt.specify_single_fiber(sample)\n", "\n", "#set parameter values\n", "sample_params = sample.parameters\n", "sample_params.set('mu_0',5)\n", "sample_params.set('A0',0.1)\n", "sample_params.set('L0',1)\n", "sample_params.set('k1_1',5)\n", "sample_params.set('k2_1',15)\n", "sample_params.set('k3_1',0.1)\n", "print(sample)\n", "print(sample_params)" ] }, { "cell_type": "code", "execution_count": 4, "id": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#give applied displacement\n", "deformed_length = np.linspace(1, 1.2, 10)\n", "\n", "#calculating force\n", "force = sample.disp_controlled(deformed_length,sample_params)\n", "\n", "#post-processing\n", "plt.plot(deformed_length,force,'-o')\n", "plt.ylabel('Cauchy stress')\n", "plt.xlabel('Deformed length')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ffdd11de-89e7-4ba0-9066-e550d0439730", "metadata": {}, "source": [ "### Other types of deformation option (`'stretch', 'strain'` and `'deltal'`)" ] }, { "cell_type": "markdown", "id": "e841f4ba-f7a9-4de9-a558-e0131fb92ab6", "metadata": {}, "source": [ "It also offers other displacement measurement options. Following the steps outlined above, examples of these alternative options are provided below. To simplify the code, the displacement values are generated and stored in `label_disp`, which is a dictionary. These values are then used in a loop. The force measurement remains at the default setting of cauchy stress." ] }, { "cell_type": "code", "execution_count": null, "id": "8ba42115-e791-4f49-8c5f-ec5ce9871143", "metadata": {}, "outputs": [ { "data": { "image/png": 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2mEZA2AEAoIUL5c7JEmEHAIAWL1RHTnYj7AAA0IIdKHFoz+EjMpmqL2OFIsIOAAAtmPuW865J8bJGRQS4msZB2AEAoAXz9NcJ0UtYEmEHAIAWLdT760iEHQAAWqyKKpe+318sibADAABC0ObcYlVUuZQYG6mOrWMCXU6jIewAANBCZXvG10mQyRR6gwm6EXYAAGihjk3+GbqXsCTCDgAALZJhGMe17BB2Gs2cOXN03nnnKT4+XklJSRo3bpy2b9/utU15ebmmTJmi1q1bKy4uTuPHj1dBQYHXNnv27NHYsWMVExOjpKQk3XfffaqqqmrKQwEAoFnJLS5Xgd2hMLNJ/donBLqcRhXQsLNixQpNmTJFX3/9tZYsWaLKykqNGDFCZWVlnm3uueceLVq0SG+//bZWrFih3NxcXX311Z71TqdTY8eOVUVFhVatWqWXX35ZCxYs0MMPPxyIQwIAoFlwt+r0SrMqOjIswNU0LpNhGEagi3A7cOCAkpKStGLFCl1yySUqLi5W27ZttXDhQl1zzTWSpG3btqlHjx5avXq1zj//fH3yySe64oorlJubq+TkZEnSc889pwceeEAHDhxQZGTkaT/XbrfLZrOpuLhYVqu1UY8RAIBg8MhHm7Vg1Y/69QUd9cgvegW6nAap7+93UPXZKS6uvtc/MTFRkpSdna3KykoNHz7cs0337t3VoUMHrV69WpK0evVq9enTxxN0JGnkyJGy2+3avHlzrZ/jcDhkt9u9HgAAtCQtpXOyFERhx+Vyadq0abrwwgvVu3dvSVJ+fr4iIyOVkJDgtW1ycrLy8/M92xwfdNzr3etqM2fOHNlsNs8jPT3dz0cDAEDwOlJRpc251f+jH8qDCboFTdiZMmWKNm3apDfeeKPRP2vGjBkqLi72PPbu3dvonwkAQLD4fl+xnC5DKdYopdmiAl1OowsPdAGSNHXqVC1evFgrV65U+/btPctTUlJUUVGhoqIir9adgoICpaSkeLZZu3at1/u579Zyb3Mii8Uii8Xi56MAAKB5OHYJK7QHE3QLaMuOYRiaOnWq3n//fS1btkydOnXyWj9w4EBFRERo6dKlnmXbt2/Xnj17lJmZKUnKzMzUxo0bVVhY6NlmyZIlslqt6tmzZ9McCAAAzci6FjK+jltAW3amTJmihQsX6sMPP1R8fLynj43NZlN0dLRsNpsmTZqk6dOnKzExUVarVXfddZcyMzN1/vnnS5JGjBihnj176uabb9bjjz+u/Px8zZw5U1OmTKH1BgCAExiGoXV7iiS1jP46UoDDzvz58yVJQ4cO9Vr+0ksv6de//rUk6cknn5TZbNb48ePlcDg0cuRIzZs3z7NtWFiYFi9erMmTJyszM1OxsbGaOHGiHn300aY6DAAAmo0fDx3R4bIKRYab1SvNFuhymsQZj7Njt9u1bNkydevWTT169PBXXU2KcXYAAC3FO9n7dO/b3+ncjFZ6Z/IFgS7njDTaODvXXnutnn32WUnS0aNHde655+raa69V37599e677za8YgAA0OjcIye3lEtYUgPCzsqVK3XxxRdLkt5//30ZhqGioiI9/fTT+tOf/uT3AgEAgP94OicTdupWXFzsGeE4KytL48ePV0xMjMaOHasdO3b4vUAAAOAf9vJK/VBYIqnl3IklNSDspKena/Xq1SorK1NWVpZGjBghSfr5558VFRX6AxMBANBcbdhTJMOQOiTGqG18y7lj2ee7saZNm6YJEyYoLi5OGRkZnjupVq5cqT59+vi7PgAA4Cctsb+O1ICw87vf/U6DBg3S3r17dfnll8tsrm4c6ty5M312AAAIYp6RkzskBLaQJtagcXbOPfdcnXvuuZIkp9OpjRs36oILLlCrVi0rKQIA0Fw4XYY21Awm2JI6J0sN6LMzbdo0/fvf/5ZUHXSGDBmic845R+np6Vq+fLm/6wMAAH6wo7BEJY4qxUaGqVtyfKDLaVI+h5133nlH/fr1kyQtWrRIOTk52rZtm+655x499NBDfi8QAACcOXd/nf4dEhQeFtCpMZucz0d78OBBz2ziH3/8sX71q1+pa9euuu2227Rx40a/FwgAAM7cup+KJLWsW87dfA47ycnJ2rJli5xOp7KysnT55ZdLko4cOaKwsDC/FwgAAM6cp3NyC+uvIzWgg/Ktt96qa6+9VqmpqTKZTBo+fLgkac2aNerevbvfCwQAAGfmUKlDOQfLJEnnpBN2TuuRRx5R7969tXfvXv3qV7+SxVI9KFFYWJgefPBBvxcIAADOzPqau7C6JMXJFhMR2GICoEG3nl9zzTWSpPLycs+yiRMn+qciAADgV9k1l7AGtsD+OlID+uw4nU499thjateuneLi4rR7925J0h//+EfPLekAACB4tNSRk918Djt//vOftWDBAj3++OOKjIz0LO/du7defPFFvxYHAADOTKXTpe/3FUlqmZ2TpQaEnVdeeUUvvPCCJkyY4HX3Vb9+/bRt2za/FgcAAM7M1jy7yitdskVHqHOb2ECXExA+h539+/erS5cuJy13uVyqrKz0S1EAAMA/3JewzumQILPZFOBqAsPnsNOzZ0998cUXJy1/5513NGDAAL8UBQAA/KOl99eRGnA31sMPP6yJEydq//79crlceu+997R9+3a98sorWrx4cWPUCAAAGsh923lLHDnZzeeWnauuukqLFi3SZ599ptjYWD388MPaunWrFi1a5BlNGQAABF5e8VHtLzoqs0nql54Q6HICxqeWnaqqKv3lL3/RbbfdpiVLljRWTQAAwA/c82H1SLUq1tKgofVCgk8tO+Hh4Xr88cdVVVXVWPUAAAA/ob9ONZ8vYw0bNkwrVqxojFoAAIAfeSb/bMH9daQGdFAePXq0HnzwQW3cuFEDBw5UbKz3Pfu/+MUv/FYcAABomPJKpzbnFkuiZcfnsPO73/1OkvTEE0+ctM5kMsnpdJ55VQAA4Ixs3F+sSqehtvEWtW8VHehyAsrnsONyuRqjDgAA4EfrjhtM0GRqmYMJujVougiHw3HS8oqKCr3yyit+KQoAAJwZOicf43PYufXWW1VcXHzS8pKSEt16661+KQoAADScYRiezsmEnQaEHcMwam0O27dvn2w2m1+KAgAADbfn8BEdLK1QZJhZvdL4ba53n50BAwbIZDLJZDJp2LBhCg8/tqvT6VROTo5GjRrVKEUCAID6c7fq9GpnVVREWICrCbx6h51x48ZJkjZs2KCRI0cqLi7Osy4yMlIdO3bU+PHj/V4gAADwjae/TgsfX8et3mFn1qxZkqSOHTvq+uuvl8ViabSiAABAw2XXTBNBf51qPvfZueyyy3TgwAHP67Vr12ratGl64YUX/FoYAADwXamjStvz7ZKkcwg7khoQdm688UZ9/vnnkqT8/HwNHz5ca9eu1UMPPaRHH33U7wUCAID6+25vkVyG1C4hWsnWqECXExR8DjubNm3SoEGDJElvvfWW+vTpo1WrVum1117TggUL/F0fAADwAePrnMznsFNZWenpr/PZZ5955sLq3r278vLy/FsdAADwCWHnZD6HnV69eum5557TF198oSVLlnhuN8/NzVXr1q39XiAAAKgfl8vQemY6P4nPYeevf/2rnn/+eQ0dOlQ33HCD+vXrJ0n66KOPPJe3AABA09t1oFT28ipFR4Spe2p8oMsJGj5PBDp06FAdPHhQdrtdrVodS4133nmnYmJi/FocAACoP/clrH7pNkWE+dyeEbJ8DjuSFBYW5hV0pOrxdwAAQOCs4xJWrYh9AACECDon146wAwBACPi5rEK7DpRJkgbQsuOFsAMAQAhYv7e6Vadz21glxkYGuJrg4nPY2b17d2PUAQAAzsC6mvmw6K9zMp/DTpcuXXTppZfq1VdfVXl5+Rl9+MqVK3XllVcqLS1NJpNJH3zwgdf6X//61zKZTF4P97g+bocPH9aECRNktVqVkJCgSZMmqbS09IzqAgCguaG/Tt18Djvr1q1T3759NX36dKWkpOg3v/mN1q5d26APLysrU79+/TR37tw6txk1apTy8vI8j9dff91r/YQJE7R582YtWbJEixcv1sqVK3XnnXc2qB4AAJqjKqdLG/YWSSLs1MbnW8/79++vf/7zn/rHP/6hjz76SAsWLNBFF12krl276rbbbtPNN9+stm3b1uu9Ro8erdGjR59yG4vFopSUlFrXbd26VVlZWfrmm2907rnnSpKeeeYZjRkzRn//+9+VlpZW634Oh0MOh8Pz2m6316teAACC0bb8Eh2tdCo+Klxd2sYFupyg0+AOyuHh4br66qv19ttv669//at27type++9V+np6brlllv8Nk/W8uXLlZSUpG7dumny5Mk6dOiQZ93q1auVkJDgCTqSNHz4cJnNZq1Zs6bO95wzZ45sNpvnkZ6e7pdaAQAIBPf4OgM6tJLZbApwNcGnwWHn22+/1e9+9zulpqbqiSee0L333qtdu3ZpyZIlys3N1VVXXXXGxY0aNUqvvPKKli5dqr/+9a9asWKFRo8eLafTKUnKz89XUlKS1z7h4eFKTExUfn5+ne87Y8YMFRcXex579+4941oBAAgUT38dOifXyufLWE888YReeuklbd++XWPGjNErr7yiMWPGyGyuzk2dOnXSggUL/DKi8vXXX+953qdPH/Xt21dnnXWWli9frmHDhjX4fS0Wi2fmdgAAmjs6J5+azy078+fP14033qiffvpJH3zwga644gpP0HFLSkrSv//9b78V6da5c2e1adNGO3fulCSlpKSosLDQa5uqqiodPny4zn4+AACEkkJ7ufb9fFQmU/WcWDiZzy07O3bsOO02kZGRmjhxYoMKOpV9+/bp0KFDSk1NlSRlZmaqqKhI2dnZGjhwoCRp2bJlcrlcGjx4sN8/HwCAYOPur9MtOV7xUREBriY4NWgi0KKiIq1du1aFhYVyuVxe62655ZZ6v09paamnlUaScnJytGHDBiUmJioxMVGzZ8/W+PHjlZKSol27dun+++9Xly5dNHLkSElSjx49NGrUKN1xxx167rnnVFlZqalTp+r666+v804sAABCCZewTs/nsLNo0SJNmDBBpaWlslqtMpmO9fo2mUw+hZ1vv/1Wl156qef19OnTJUkTJ07U/Pnz9f333+vll19WUVGR0tLSNGLECD322GNe/W1ee+01TZ06VcOGDZPZbNb48eP19NNP+3pYAAA0S+v2FEli5ORTMRmGYfiyQ9euXTVmzBj95S9/UUxMTGPV1aTsdrtsNpuKi4tltVoDXQ4AAPXiqHKqz6z/qsLp0vJ7h6pjm9hAl9Sk6vv77XMH5f379+vuu+8OmaADAEBztWm/XRVOl1rHRiqjNb/LdfE57IwcOVLffvttY9QCAAB8sO6nY4MJHt+tBN7q1Wfno48+8jwfO3as7rvvPm3ZskV9+vRRRIR3z+9f/OIX/q0QAADUyn0nFp2TT61eYWfcuHEnLXv00UdPWmYymTyjGwMAgMZjGIa+5U6seqlX2Dnx9nIAABBY+34+qgMlDoWbTerbnsEET6XBc2MBAIDAcV/C6pVmVVREWICrCW4+h52777671nFsnn32WU2bNs0fNQEAgNNwd04+h0tYp+Vz2Hn33Xd14YUXnrT8ggsu0DvvvOOXogAAwKll0zm53nwOO4cOHZLNdvK1QavVqoMHD/qlKAAAULcjFVXamlciiZGT68PnsNOlSxdlZWWdtPyTTz5R586d/VIUAACo23d7i+V0GUq1RSktITrQ5QQ9n+fGmj59uqZOnaoDBw7osssukyQtXbpU//jHP/TUU0/5uz4AAHACd+dk+uvUj89h57bbbpPD4dCf//xnPfbYY5Kkjh07av78+T5NAgoAABrGM9M5l7DqxeewI0mTJ0/W5MmTdeDAAUVHRysuLs7fdQEAgFoYhkHLjo8aFHbc2rZt6686AABAPew+WKaiI5WyhJvVM7Xumb5xjM9hp1OnTqecbGz37t1nVBAAAKib+xJWv/YJigxnbOD68DnsnDhwYGVlpdavX6+srCzdd999/qoLAADUwjPTeUZCYAtpRnwOO7///e9rXT537lx9++23Z1wQAACom2emczon15vf2r9Gjx6td999119vBwAATlB8tFI/FJRKonOyL/wWdt555x0lJib66+0AAMAJ1te06nRsHaM2cZYAV9N8+HwZa8CAAV4dlA3DUH5+vg4cOKB58+b5tTgAAHDMuj1Fkpgiwlc+h51x48Z5vTabzWrbtq2GDh2q7t27+6suAABwAmY6bxifw86sWbMaow4AAHAKTpfhuYzFTOe+OaNBBcvLy1VRUeG1zGplgCMAAPzth4ISlVU4FWcJV9fk+ECX06z43EG5rKxMU6dOVVJSkmJjY9WqVSuvBwAA8D/3YIL90xMUZq57cF+czOewc//992vZsmWaP3++LBaLXnzxRc2ePVtpaWl65ZVXGqNGAABaPPrrNJzPl7EWLVqkV155RUOHDtWtt96qiy++WF26dFFGRoZee+01TZgwoTHqBACgRcumv06D+dyyc/jwYXXu3FlSdf+cw4cPS5IuuugirVy50r/VAQAAHSx16KdDRyRVX8aCb3wOO507d1ZOTo4kqXv37nrrrbckVbf4JCQk+LU4AABw7BJW1+Q42aIjAlxN8+Nz2Ln11lv13XffSZIefPBBzZ07V1FRUbrnnnuYCBQAgEbAJawz43OfnXvuucfzfPjw4dq2bZuys7PVpUsX9e3b16/FAQAAaf1PRZKkAYyc3CBnNM6OJGVkZCgjI8MftQAAgBNUVLn03b4iSbTsNFS9L2MtW7ZMPXv2lN1uP2ldcXGxevXqpS+++MKvxQEA0NJtybPLUeVSQkyEOreJDXQ5zVK9w85TTz2lO+64o9YRkm02m37zm9/oiSee8GtxAAC0dO7BBAd2aOU1ETfqr95h57vvvtOoUaPqXD9ixAhlZ2f7pSgAAFBt3R4GEzxT9Q47BQUFioio+3a38PBwHThwwC9FAQCAap6Rk+mc3GD1Djvt2rXTpk2b6lz//fffKzU11S9FAQAAKbfoqPKKyxVmNqlfui3Q5TRb9Q47Y8aM0R//+EeVl5eftO7o0aOaNWuWrrjiCr8WBwBAS+a+hNUjNV4xkWd8A3WLVe8zN3PmTL333nvq2rWrpk6dqm7dukmStm3bprlz58rpdOqhhx5qtEIBAGhpju+cjIard9hJTk7WqlWrNHnyZM2YMUOGYUiSTCaTRo4cqblz5yo5ObnRCgUAoKVhpnP/8KlNLCMjQx9//LF+/vln7dy5U4Zh6Oyzz1arVvxLAADAX5wuQ1/uPKCN+4slMfnnmTIZ7iaaFsxut8tms6m4uLjWcYQAAGgqWZvyNHvRFuUVH+sjm2KL0iNX9tSo3twIdLz6/n77PBEoAABoHFmb8jT51XVeQUeSCorLNfnVdcralBegypo3wg4AAEHA6TI0e9EW1Xa5xb1s9qItcrpa/AUZnxF2AAAIAmtzDp/UonM8Q1JecbnW5hxuuqJCREDDzsqVK3XllVcqLS1NJpNJH3zwgdd6wzD08MMPKzU1VdHR0Ro+fLh27Njhtc3hw4c1YcIEWa1WJSQkaNKkSSotLW3CowAA4MwVltQddBqyHY4JaNgpKytTv379NHfu3FrXP/7443r66af13HPPac2aNYqNjdXIkSO9BjacMGGCNm/erCVLlmjx4sVauXKl7rzzzqY6BAAA/CIpPsqv2+GYoLkby2Qy6f3339e4ceMkVbfqpKWl6Q9/+IPuvfdeSVJxcbGSk5O1YMECXX/99dq6dat69uypb775Rueee64kKSsrS2PGjNG+ffuUlpZW62c5HA45HA7Pa7vdrvT0dO7GAgAEjNNlKHPOUhWWOGpdb1L1XVlfPnCZwszMfi6FwN1YOTk5ys/P1/Dhwz3LbDabBg8erNWrV0uSVq9erYSEBE/QkaThw4fLbDZrzZo1db73nDlzZLPZPI/09PTGOxAAAOrBJKlNnKXOdZI068qeBJ0GCNqwk5+fL0knjcqcnJzsWZefn6+kpCSv9eHh4UpMTPRsU5sZM2aouLjY89i7d6+fqwcAwDf//jJHW/LsiggzqU1cpNe6FFuU5t90DuPsNFCLnFXMYrHIYqk9PQMA0NS+31ekxz/dJkma/Yveuu68dK3NOazCknIlxUdpUKdEWnTOQNCGnZSUFElSQUGBUlOPJdmCggL179/fs01hYaHXflVVVTp8+LBnfwAAglmpo0p3vb5elU5Do3un6IZB6TKZTMo8q3WgSwsZQXsZq1OnTkpJSdHSpUs9y+x2u9asWaPMzExJUmZmpoqKipSdne3ZZtmyZXK5XBo8eHCT1wwAgK/++MEm/XToiNolROt/r+4rk4kWHH8LaMtOaWmpdu7c6Xmdk5OjDRs2KDExUR06dNC0adP0pz/9SWeffbY6deqkP/7xj0pLS/PcsdWjRw+NGjVKd9xxh5577jlVVlZq6tSpuv766+u8EwsAgGDx3rp9en/9fplN0j+v7y9bTESgSwpJAQ073377rS699FLP6+nTp0uSJk6cqAULFuj+++9XWVmZ7rzzThUVFemiiy5SVlaWoqKOjTHw2muvaerUqRo2bJjMZrPGjx+vp59+usmPBQAAX+QcLNPMDzZJkqYN76pzOyYGuKLQFTTj7AQSs54DAJpSRZVLV8//Spv22zW4U6IW3nE+HZAboNmPswMAQKj626fbtGm/XQkxEXrq+v4EnUZG2AEAoAl9vr1Q//oiR5L0t2v6KdUWHeCKQh9hBwCAJlJYUq573/pOkjQxM0OX90w+zR7wB8IOAABNwOUyNP3N73SorELdU+I1Y0yPQJfUYhB2AABoAi98sVtf7jyoqAiznr1xgKIiwgJdUotB2AEAoJFt2Fukv3+6XZL0yJW91CUpPsAVtSyEHQAAGlFJeaXufn29qlyGxvZN1XXnpQe6pBaHsAMAQCMxDEMPvb9Jew5XTwfxl1/2YTqIACDsAADQSN7J3qePvstVmNmkp28YIFs000EEAmEHAIBGsOtAqWZ9tFmSNP3yrhqY0SrAFbVchB0AAPzMUeXU3a+v15EKpzI7t9Zvh5wV6JJaNMIOAAB+9tdPtmtzrl2tmA4iKBB2AADwo2XbCvSfr6qng/j7r/op2RoV4IpA2AEAwE8K7OW69+3vJUm3XthRw3owHUQwIOwAAOAHTpehe97coMNlFeqZatWDo7sHuiTUIOwAAOAHz63YpVW7Dik6IkzP3DhAlnCmgwgWhB0AAM7Quj0/64klP0iSZl/VS2e1jQtwRTgeYQcAgDNgr5kOwukydGW/NP1qYPtAl4QTEHYAAGggwzD0P+9t1L6fjyo9MVp//mVvpoMIQoQdAAAa6O1v92nx93kKN5v09PUDZI1iOohgRNgBAKABdhaWeKaD+MOIbhrQgekgghVhBwAAH5VXOnXX6xt0tNKpi7q00W8u6RzoknAKhB0AAHz0v59s09Y8u1rHRuqJa/vJzHQQQY2wAwCADz7bUqAFq36UVD0dRBLTQQQ9wg4AAPWUX1yu+975TpI06aJOurR7UoArQn0QdgAAqAeny9C0N9fr5yOV6t3OqvtHdQt0Sagnwg4AAPUwf/lOfb37sGIiw/T09UwH0ZwQdgAAOI3snw7ryc92SJIevaq3OjMdRLNC2AEA4BSKj1bq7tc3yOkyNK5/msaf0y7QJcFHhB0AAOrgng5if9FRZbSO0WPjmA6iOSLsAABQhze+2av/23hsOoh4poNolgg7AADUYkdBiWYvqp4O4r6R3dQvPSGwBaHBCDsAAJygejqI9SqvdOnis9vojouZDqI5I+wAAHCCv3y8VdvyS9QmLlL/YDqIZo+wAwDAcT7dnK9XVv8kSfrHtf2VFM90EM0dYQcAgBq5RUd1/zvfS5LuvKSzhnRtG+CK4A+EHQAA5J4OYoOKj1aqb3ub7h3BdBChgrADAICkZ5ft1Nqcw4qtmQ4iMpyfyFDBv0kAQIv3zY+H9c+lP0iS/vTL3urYJjbAFcGfCDsAgBat6EiFfv/6erkM6eoB7fTLAe0DXRL8jLADAGixDMPQg+9uVG5xuTq2jtGj43oHuiQ0AsIOAKDFWrh2j7I25ysizKRnbjhHcZbwQJeERkDYAQC0SNvzS/Tooi2SpAdGdVef9rYAV4TGQtgBALQ41dNBrJOjyqUhXdvqtgs7BbokNKKgDjuPPPKITCaT16N79+6e9eXl5ZoyZYpat26tuLg4jR8/XgUFBQGsGADQHDy2eIt+KChVmziL/v4rpoMIdUEddiSpV69eysvL8zy+/PJLz7p77rlHixYt0ttvv60VK1YoNzdXV199dQCrBQAEu6xNeXptzR5J0pPX9VPbeEuAK0JjC/qeWOHh4UpJSTlpeXFxsf79739r4cKFuuyyyyRJL730knr06KGvv/5a559/flOXCgAIcvuPmw7iN0M66+KzmQ6iJQj6lp0dO3YoLS1NnTt31oQJE7RnT3Uaz87OVmVlpYYPH+7Ztnv37urQoYNWr159yvd0OByy2+1eDwBAaKtyujTtjfWyl1epX3oC00G0IEEddgYPHqwFCxYoKytL8+fPV05Oji6++GKVlJQoPz9fkZGRSkhI8NonOTlZ+fn5p3zfOXPmyGazeR7p6emNeBQAgGDwzLKd+ubHnxVnCdfT1/dXRFhQ/wTCj4L6Mtbo0aM9z/v27avBgwcrIyNDb731lqKjoxv8vjNmzND06dM9r+12O4EHAELY17sP6ZllOyRJf/5lb2W0ZjqIlqRZxdqEhAR17dpVO3fuVEpKiioqKlRUVOS1TUFBQa19fI5nsVhktVq9HgCA0PRzWYXueXODXIZ0zcD2uqp/u0CXhCbWrMJOaWmpdu3apdTUVA0cOFARERFaunSpZ/327du1Z88eZWZmBrBKAECwMAxD97/7vfKKy9W5Taxm/6JXoEtCAAT1Zax7771XV155pTIyMpSbm6tZs2YpLCxMN9xwg2w2myZNmqTp06crMTFRVqtVd911lzIzM7kTCwAgSXr165+0ZEuBIsPMevqGAYplOogWKaj/re/bt0833HCDDh06pLZt2+qiiy7S119/rbZtq28VfPLJJ2U2mzV+/Hg5HA6NHDlS8+bNC3DVAIBAcboMrc05rMKScpVXOvXo4prpIEZ3V+92TAfRUpkMwzACXUSg2e122Ww2FRcX038HAJqprE15mr1oi/KKy72W925n1aKpF8lkYpTkUFPf3+9m1WcHAIDaZG3K0+RX150UdCRp0367Pt186iFJENoIOwCAZs3pMjR70RbVdZnCJGn2oi1yulr8hYwWi7ADAGjW1uYcrrVFx82QlFdcrrU5h5uuKAQVwg4AoFnbWVhSr+0KS+oORAhtQX03FgAAddlfdFTPr9ilhTUzmJ9OUnxUI1eEYEXYAQA0Kz8dKtP85bv07rp9qnRW98OJCDN5np/IJCnFFqVBnRKbsEoEE8IOAKBZ2FlYqnmf79SH3+V6OhtfcFZr3XXZ2So6UqHfvbZOkrw6KrtvNp91ZU+Fmbn1vKUi7AAAgtrWPLue/XynPt6YJ/fIcEO7tdVdl3XRwIxjrTXzbzrnpHF2UmxRmnVlT43qndrUZSOIEHYAAEHp+31FembZTi3ZUuBZNqJnsu667Gz1aX/yaMijeqfq8p4pnhGUk+KrL13RogPCDgAgqHz742E9s2ynVvxwQJJkMklj+6Rq6mVd1D3l1KPch5lNyjyrdVOUiWaEsAMACDjDMLR61yE9vWyHvt5dPR5OmNmkcf3b6XeXnqWz2sYFuEI0Z4QdAEDAGIah5T8c0LPLdir7p58lVd9Zdc3A9po8pIs6tI4JcIUIBYQdAECTc7kMLdlaoGeX7dTG/cWSJEu4WTcM6qA7L+mstIToAFeIUELYAQA0GafL0Mcb8zT3853all898nF0RJhuzszQ7Rd3YuA/NArCDgCg0VU5XfpwQ67mLt+p3QfKJElxlnBNvCBDky7qrMTYyABXiFBG2AEANBpHlVPvrduvect3au/ho5IkW3SEJl3USRMzO8oWExHgCtESEHYAAH5XXunUm9/s1XMrdnkG+WsdG6nbL+6smzMzFGfh5wdNh782AIDflDmqtHDNHr3wxW4dKHFIkpKtFv3mkrN0w6AOio4MC3CFaIkIOwCAM2Yvr9T/W/2TXvxit34+UilJapcQrclDz9I1A9srKoKQg8Ah7AAAGqzoSIX+89WPWvBVjuzlVZKkjq1j9LtLu+iXA9opIswc4AoBwg4AoAEOljr0ry9269XVP6mswilJOjspTlMv66KxfVIVTshBECHsAADqLb+4XC+s3K2Fa39SeaVLktQz1aq7Luuikb1SZGbSTQQhwg4AQE6XccrZwvf9fETzl+/S29/uU4WzOuT0S0/Q3Zd10WXdk2QyEXIQvAg7ANDCZW3K0+xFWzy3iEtSqi1Ks67sqW4pVs37fKfeX79fVS5DkjSoY6LuGtZFF3VpQ8hBs0DYAYAWLGtTnia/uk7GCcvzisv121fXySR51l3UpY3uuqyLBndu3cRVAmeGsAMALZTTZWj2oi0nBZ3jGZIu69ZWU4edrXM6tGqq0gC/ors8ALRQa3MOe126qssdl5xF0EGzRssOALQAhmEot7hcm/YXa/P+Ym3cX6xvf/q5XvsWlpw+EAHBjLADACHGMAztPXxUm3KrQ82m/cXanGvX4bKKBr1fUnyUnysEmhZhBwCaMZfL0I+HyrSxJtBsqgk37tGMjxduNqlrcrx6t7OqdzubeqRaddfCdSqwO2rtt2OSlGKrvg0daM4IOwDQTDhdhnYdKK0JNPaaFptizwjGx4sMM6t7arx6pdnUp51NvdtZ1TU5/qQ5qh75RS9NPuGuK6k66EjSrCt7eo23AzRHhB0ACEKVTpd2FNQEm9zq1poteXbPqMXHi4owq0eqtTrUpNnUqybY1GdeqlG9UzX/pnNOGmcnpWacnVG9U/16XEAgEHYA4AycbuTh+nBUObU9v6S6taYm2GzLL1FF1cnBJjYyTL1qAk3vNJv6tLepc5vYM5qLalTvVF3eM+WMjwMIVoQdAGigU408XFeLyNEKp7bm2z13RG3ab9cPBSWe0YmPFx8Vrt5pNk8fm97tbOrUOrZR5p8KM5uUeRaDBSI0EXYAoAHqGnk4v7hck19dp/k3naOLz26rLXl2bdxXfSlq8367dh4olbOWYNMqJsITaNwBp0NiDNMxAH5A2AEAH51q5GH3sikL19caaiSpTZxFfWpaa3rVXIpKs0URbIBGQtgBgFM4UlGlgyUVOlBargMlFTpY6tD6PT+fduRhd9BJsUbVtNjUdCBuZ1NSvIVgAzQhwg6AgPBHx96GOlrh1MFShw6UOnSgxKGDpQ5PoKn+p3uZo9bbuuvrT+N666bzM/xYOYCGIOwAaHIN6dh7OuWVNQGmxKGDpRXHQsxxgca9rtRx8oB7pxIVYVabOIvaxlvUJs4il2Fo6dbC0+53Vtu4Bh0LAP8i7ADNTCBbRPyhPh173YHHUeXUwdIKHSxx1BJeKrxCTImPASYy3Ky2cRa1ibeobZxFbeMjvQKN+59t4iIVZwn3uuzkdBm66K/LlF9czsjDQDNA2AGakcZoEWkKhmHoSIVT9qOV+uMHm0/Zsffu19erXattOlRaUeuUB6cSGWZWm7hIT4A5Floi1TY+6ti6eIviTwgwvggzmzTryp6MPAw0EybDMGq/XaAFsdvtstlsKi4ultVqDXQ5aATNvTVEqrtFxH0Ux7eInCmny9CRiiqVOZwqdVTpSEWVSh3Vr8scVSqrqFKZo0qlDqeO1Lx2Py+teX2kZt8yR5WOVDrV0G+acLPphNByYstL9fO2cRZZoxseYBqiuYZPIFTU9/eblh2cUqiEhOb+g3S6W51NkmZ9tFm90mwqr3QeCyY1oaQ6oDhrAkpNEKlZ5wklFcfCzNHKhnfK9Yepl56lq/q3U9t4i2zREUF75xIjDwPNAy07apyWHUJCcGjK1hCp+nKNo8pV83DKUXnc8ypXzWvnsW0qq5+XVzpPuV9eUbnW7fnZb3XWV5jZpNjIMMVawo89al7HWcIVExmmuJrlxz+PtYQpNvK4fSzV6zbsKdKNL6457ee+fsf5jOYL4LRaXMvO3Llz9be//U35+fnq16+fnnnmGQ0aNCggtYRySKitE2mgGYYhp8tQpdNQhdOlyppHeYVLMz/YdMr+IQ++u1GFJQ5VOg2fwkldoaS2uYyaUpjJpPjocMVG1oSRmpARG3nc8+MCS2zNOncYianZL9ZSvd4SbvZrq8rgzq2VaouiYy+AJhUSLTtvvvmmbrnlFj333HMaPHiwnnrqKb399tvavn27kpKSTru/P1t2mroloTFUOV266K+fK99e96BpbeIseuHmgXIZ7oBhqLKqOmR4XteEjooql6pcx68/tq56vfdrr32Pe1/3Ok+gqXKp0lW9bTD+FZtMUlR4mCwRZlnCzbKEh1X/M+K45+7lp9lmf9FR/euLnNN+ZnNoEXH/NyLV3rG3Ofw3AiA41Pf3OyTCzuDBg3Xeeefp2WeflSS5XC6lp6frrrvu0oMPPnja/f0Vdty3o55qZNU2cZGaN2GgDMOoDgBOl6qchqpc1T/knn96PXd5bVvpqtmnZrnXsjr3qVl2wv7HL69yHdu/uYsIM8kkqaIex9K3vVUdW8fVEjLqEVQiwhRVyzJLuFnhZpPfWkXqe6vzlw9c1iwul4ZC6yeAwGsxl7EqKiqUnZ2tGTNmeJaZzWYNHz5cq1evrnUfh8Mhh8PheW232/1Sy9qcw6cdQv5gaYWufb72upqbVjERSoiJVESYSRFhZkWEmRUZZlb48a/Djz2vXl/zOrxmmdnkeR553H4R4Se8DjN7f85J72tWRM0yd8hYveuQbvjX16c9jhmjewZ9a0io3epMx14ATanZh52DBw/K6XQqOTnZa3lycrK2bdtW6z5z5szR7Nmz/V5LYcmpg45b69hI2WIiFGGuDgbhNT/67pAQbq5ZFmZSeM027m0jwswKc2973LJa96l57r3Me9sw87FlETVB5bu9RfptzWWGU5k3YWBQh4RBnRJDqn/IqN6pmn/TOSe1iKQ00xaRMLMpqP9+AISOZh92GmLGjBmaPn2657Xdbld6evoZv29SfFS9tnv2xnOC+ks+KT4qJEJCqLWGSLSIAEBDmANdwJlq06aNwsLCVFBQ4LW8oKBAKSkpte5jsVhktVq9Hv7gbkmo62fHpOp+Cc0lJEg66ViaW0hwt4ak2LyDaIotqtl2hHW3iFzVv50yz2rdLP49AEAgNfuWncjISA0cOFBLly7VuHHjJFV3UF66dKmmTp3apLWEUktCKF0yoTUEAFq2kLgb680339TEiRP1/PPPa9CgQXrqqaf01ltvadu2bSf15amNvwcVDKU7TUJhcEQAQGhqMXdjSdJ1112nAwcO6OGHH1Z+fr769++vrKysegWdxhBKLQl0IgUANHch0bJzppgIFACA5qe+v9/NvoMyAADAqRB2AABASCPsAACAkEbYAQAAIY2wAwAAQhphBwAAhDTCDgAACGmEHQAAENIIOwAAIKSFxHQRZ8o9iLTdbg9wJQAAoL7cv9unmwyCsCOppKREkpSenh7gSgAAgK9KSkpks9nqXM/cWJJcLpdyc3MVHx8vk8l/k3Xa7Xalp6dr7969zLl1Gpwr33C+6o9zVX+cq/rjXNVfY54rwzBUUlKitLQ0mc1198yhZUeS2WxW+/btG+39rVYr/zHUE+fKN5yv+uNc1R/nqv44V/XXWOfqVC06bnRQBgAAIY2wAwAAQhphpxFZLBbNmjVLFosl0KUEPc6Vbzhf9ce5qj/OVf1xruovGM4VHZQBAEBIo2UHAACENMIOAAAIaYQdAAAQ0gg7AAAgpBF26mnlypW68sorlZaWJpPJpA8++OC0+yxfvlznnHOOLBaLunTpogULFpy0zdy5c9WxY0dFRUVp8ODBWrt2rf+LD4DGOF+PPPKITCaT16N79+6NcwBNyNdzlZeXpxtvvFFdu3aV2WzWtGnTat3u7bffVvfu3RUVFaU+ffro448/9n/xTawxztWCBQtO+ruKiopqnANoQr6eq/fee0+XX3652rZtK6vVqszMTH366acnbReK31mNca74vqr25Zdf6sILL1Tr1q0VHR2t7t2768knnzxpu8b+uyLs1FNZWZn69eunuXPn1mv7nJwcjR07Vpdeeqk2bNigadOm6fbbb/f6D+LNN9/U9OnTNWvWLK1bt079+vXTyJEjVVhY2FiH0WQa43xJUq9evZSXl+d5fPnll41RfpPy9Vw5HA61bdtWM2fOVL9+/WrdZtWqVbrhhhs0adIkrV+/XuPGjdO4ceO0adMmf5be5BrjXEnVI7se/3f1008/+avkgPH1XK1cuVKXX365Pv74Y2VnZ+vSSy/VlVdeqfXr13u2CdXvrMY4VxLfV5IUGxurqVOnauXKldq6datmzpypmTNn6oUXXvBs0yR/VwZ8Jsl4//33T7nN/fffb/Tq1ctr2XXXXWeMHDnS83rQoEHGlClTPK+dTqeRlpZmzJkzx6/1Bpq/ztesWbOMfv36NUKFwaM+5+p4Q4YMMX7/+9+ftPzaa681xo4d67Vs8ODBxm9+85szrDB4+OtcvfTSS4bNZvNbXcHI13Pl1rNnT2P27Nme1y3hO8tf54rvq7r98pe/NG666SbP66b4u6Jlp5GsXr1aw4cP91o2cuRIrV69WpJUUVGh7Oxsr23MZrOGDx/u2aYlOd35ctuxY4fS0tLUuXNnTZgwQXv27GnKMpuN+p5PVCstLVVGRobS09N11VVXafPmzYEuKeBcLpdKSkqUmJgoie+sUznxXLnxfXWy9evXa9WqVRoyZIikpvu7Iuw0kvz8fCUnJ3stS05Olt1u19GjR3Xw4EE5nc5at8nPz2/KUoPC6c6XJA0ePFgLFixQVlaW5s+fr5ycHF188cUqKSkJRMlBra7z2RL/tk6nW7du+s9//qMPP/xQr776qlwuly644ALt27cv0KUF1N///neVlpbq2muvlSS+s07hxHMl8X11ovbt28tisejcc8/VlClTdPvtt0tqur8rZj1HszF69GjP8759+2rw4MHKyMjQW2+9pUmTJgWwMjRnmZmZyszM9Ly+4IIL1KNHDz3//PN67LHHAlhZ4CxcuFCzZ8/Whx9+qKSkpECXE9TqOld8X3n74osvVFpaqq+//loPPvigunTpohtuuKHJPp+w00hSUlJUUFDgtaygoEBWq1XR0dEKCwtTWFhYrdukpKQ0ZalB4XTnqzYJCQnq2rWrdu7c2RQlNit1nc+W+Lflq4iICA0YMKDF/l298cYbuv322/X22297XVpo06YN31knqOtc1aalf1916tRJktSnTx8VFBTokUce0Q033NBkf1dcxmokmZmZWrp0qdeyJUuWeP4PMjIyUgMHDvTaxuVyaenSpV7/l9lSnO581aa0tFS7du1SampqY5fX7DTkfKKa0+nUxo0bW+Tf1euvv65bb71Vr7/+usaOHeu1ju8sb6c6V7Xh++oYl8slh8MhqQn/rvzW1TnElZSUGOvXrzfWr19vSDKeeOIJY/369cZPP/1kGIZhPPjgg8bNN9/s2X737t1GTEyMcd999xlbt2415s6da4SFhRlZWVmebd544w3DYrEYCxYsMLZs2WLceeedRkJCgpGfn9/kx+dvjXG+/vCHPxjLly83cnJyjK+++soYPny40aZNG6OwsLDJj8+ffD1XhmF4th84cKBx4403GuvXrzc2b97sWf/VV18Z4eHhxt///ndj69atxqxZs4yIiAhj48aNTXps/tYY52r27NnGp59+auzatcvIzs42rr/+eiMqKsprm+bI13P12muvGeHh4cbcuXONvLw8z6OoqMizTah+ZzXGueL7qtqzzz5rfPTRR8YPP/xg/PDDD8aLL75oxMfHGw899JBnm6b4uyLs1NPnn39uSDrpMXHiRMMwDGPixInGkCFDTtqnf//+RmRkpNG5c2fjpZdeOul9n3nmGaNDhw5GZGSkMWjQIOPrr79u/INpAo1xvq677jojNTXViIyMNNq1a2dcd911xs6dO5vmgBpRQ85VbdtnZGR4bfPWW28ZXbt2NSIjI41evXoZ//d//9c0B9SIGuNcTZs2zfPfYHJysjFmzBhj3bp1TXdQjcTXczVkyJBTbu8Wit9ZjXGu+L6q9vTTTxu9evUyYmJiDKvVagwYMMCYN2+e4XQ6vd63sf+uTIZhGP5oIQIAAAhG9NkBAAAhjbADAABCGmEHAACENMIOAAAIaYQdAAAQ0gg7AAAgpBF2AABASCPsAACAkEbYAYDTMJlM+uCDDwJdBoAGIuwACGq//vWvNW7cOL+8148//iiTyaQNGzb45f0ANA+EHQAhobKyMtAlAAhShB0AQeGdd95Rnz59FB0drdatW2v48OG677779PLLL+vDDz+UyWSSyWTS8uXLPS00b775poYMGaKoqCi99tprkqQXX3xRPXr0UFRUlLp376558+Z5PqNTp06SpAEDBshkMmno0KGedf/5z3/Uq1cvWSwWpaamaurUqV71HTx4UL/85S8VExOjs88+Wx999FHjnxQA/uHXaUUBoAFyc3ON8PBw44knnjBycnKM77//3pg7d65RUlJiXHvttcaoUaOMvLw8Iy8vz3A4HEZOTo4hyejYsaPx7rvvGrt37zZyc3ONV1991UhNTfUse/fdd43ExERjwYIFhmEYxtq1aw1JxmeffWbk5eUZhw4dMgzDMObNm2dERUUZTz31lLF9+3Zj7dq1xpNPPumpT5LRvn17Y+HChcaOHTuMu+++24iLi/PsDyC4EXYABFx2drYhyfjxxx9PWjdx4kTjqquu8lrmDjtPPfWU1/KzzjrLWLhwodeyxx57zMjMzPTab/369V7bpKWlGQ899FCd9UkyZs6c6XldWlpqSDI++eST+hwegAALD1ybEgBU69evn4YNG6Y+ffpo5MiRGjFihK655hq1atXqlPude+65nudlZWXatWuXJk2apDvuuMOzvKqqSjabrc73KCwsVG5uroYNG3bKz+rbt6/neWxsrKxWqwoLC093aACCAGEHQMCFhYVpyZIlWrVqlf773//qmWee0UMPPaQ1a9accr/Y2FjP89LSUknSv/71Lw0ePPik969LdHR0vWqMiIjwem0ymeRyueq1L4DAooMygKBgMpl04YUXavbs2Vq/fr0iIyP1/vvvKzIyUk6n87T7JycnKy0tTbt371aXLl28Hu6OyZGRkZLk9X7x8fHq2LGjli5d2jgHBiDgaNkBEHBr1qzR0qVLNWLECCUlJWnNmjU6cOCAevToofLycn366afavn27WrdufcpLUrNnz9bdd98tm82mUaNGyeFw6Ntvv9XPP/+s6dOnKykpSdHR0crKylL79u0VFRUlm82mRx55RL/97W+VlJSk0aNHq6SkRF999ZXuuuuuJjwLABoLLTsAAs5qtWrlypUaM2aMunbtqpkzZ+of//iHRo8erTvuuEPdunXTueeeq7Zt2+qrr76q831uv/12vfjii3rppZfUp08fDRkyRAsWLPC07ISHh+vpp5/W888/r7S0NF111VWSpIkTJ+qpp57SvHnz1KtXL11xxRXasWNHkxw7gMZnMgzDCHQRAAAAjYWWHQAAENIIOwAAIKQRdgAAQEgj7AAAgJBG2AEAACGNsAMAAEIaYQcAAIQ0wg4AAAhphB0AABDSCDsAACCkEXYAAEBI+/8bjN1qImXrpgAAAABJRU5ErkJggg==\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "label_disp = {'stretch': np.linspace(1,1.3,10), 'strain': np.linspace(0,0.09,10),'deltal': np.linspace(0, 0.3,10)} #the different types of disp measurements\n", "\n", "\n", "for key, value in label_disp.items():\n", " \n", " #create the sample and measurement option\n", " sample = pmt.UniaxialExtension(mat, disp_measure = key )\n", " \n", " #given applied displacement\n", " applied_disp = value #applied displacement\n", " \n", " #calculating stress\n", " stress = sample.disp_controlled(applied_disp, sample_params)\n", " \n", " #post-processing\n", " plt.plot(applied_disp,stress,'-o')\n", " plt.ylabel('Cauchy stress')\n", " plt.xlabel(key)\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "1302b344-55ed-4b17-8b56-5c088c1efd28", "metadata": {}, "source": [ "## Force controlled" ] }, { "cell_type": "markdown", "id": "dc8e4042-c55c-4019-a0fa-511a184249b5", "metadata": {}, "source": [ "Similar to the displacement measurement options, the force measurement in `pymechT` also offers several alternatives. The available options for force measurement are: `force`, `cauchy`: Cauchy stress, `1pk`: 1st PK stress, and `2pk`: 2nd PK stress. We can change the force measurement setting using the `force_measure` option. The first example uses the `force` option, and the displacement measurment is `stretch`. The process is same as the steps as above.\n", "\n", "For calculation of force given deformation, we use `pmt.UniaxialExtension.force_controlled`\n", "\n", "***pmt.UniaxialExtension.force_controlled(self, forces, params=None, x0=None)***\n", "\n", "It has three inputs: *forces*, *params* and *x0*. *forces* represents the given force data, *params* represents the parameter values of sample, *x0* represents the initial guess for the displacement.\n", "\n", "When using `force_controlled` simulation, an iterative solution is involved, for which an initial guess is required. There is a default value, which may not be reasonable for different `disp_measure`s. Thus, we explicitly specify the initial guess using `x0` argument." ] }, { "cell_type": "code", "execution_count": 6, "id": "1de88eab-df5e-4326-bdc3-2d7521d3daa1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fiber directions set to 0 degrees ( 0.0 radians)\n", "An object of type UniaxialExtensionwith stretch as input, force as output, and the following material\n", "Material model with 2 components:\n", "Component1: NH with fiber direction(s):[array([1., 0., 0.])]\n", "Component2: GOH with fiber direction(s):[array([1., 0., 0.])]\n", "\n", "------------------------------------------------------------------\n", "Keys Value Fixed? Lower bound Upper bound \n", "------------------------------------------------------------------\n", "L0 10.00 No 1.00e-04 1.00e+03 \n", "A0 0.10 No 1.00e-04 1.00e+03 \n", "mu_0 5.00 No 1.00e-04 1.00e+02 \n", "k1_1 5.00 No 0.10 30.00 \n", "k2_1 15.00 No 0.10 30.00 \n", "k3_1 0.10 No 0.00 0.33 \n", "------------------------------------------------------------------\n", "\n" ] } ], "source": [ "#define material\n", "mat = pmt.MatModel('nh', 'goh')\n", "\n", "#set sample\n", "sample = pmt.UniaxialExtension(mat, force_measure='force')\n", "pmt.specify_single_fiber(sample)\n", "\n", "#set parameter value\n", "sample_params = sample.parameters\n", "sample_params.set('mu_0',5)\n", "sample_params.set('A0',0.1)\n", "sample_params.set('L0',10)\n", "sample_params.set('k1_1',5)\n", "sample_params.set('k2_1',15)\n", "sample_params.set('k3_1',0.1)\n", "print(sample)\n", "print(sample_params)" ] }, { "cell_type": "code", "execution_count": 7, "id": "8ed5bf92-04ca-4aa5-abb5-4afc0370fab1", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#apply force\n", "applied_force = np.linspace(1,5,10)\n", "\n", "#calculating deformation\n", "deformation = sample.force_controlled(applied_force, sample_params, x0 = [0.1])\n", "\n", "#post-processing\n", "plt.plot(applied_force,deformation,'-o')\n", "plt.ylabel('Resulting stretch')\n", "plt.xlabel('Applied force')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "4e4dbf5d-890b-4e64-b609-d203b108da63", "metadata": {}, "source": [ "## Different `force_measure`s" ] }, { "cell_type": "markdown", "id": "f537c997-eeeb-42f6-a528-5c65f446ed5f", "metadata": {}, "source": [ "The other options for `force_measure` are demonstrated below." ] }, { "cell_type": "code", "execution_count": 8, "id": "f6fd0d2e-ea24-4ef0-8a98-dc69a61f66bb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fiber directions set to 0 degrees ( 0.0 radians)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Fiber directions set to 0 degrees ( 0.0 radians)\n" ] }, { "data": { "image/png": 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\n", 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tewcNY5V6JKqAMLPzzexLM9thZkVmttPMimJUUz7QqdL7zPA2kZj6cMUWRt//Hm8vK+DcAe1JS93/45GWmsKEUT0Dqk4k/qLtpL4HOMfdl8aymLD5wNFm1pVQMFwMXBqH40o9tbesgj+/sZyH3l9F19aH8egVx9KvY8v9RjF1SE9jwqieWhta6pVoA2JTTcPBzKYDI4HWZpYH3AmkArj7ZDNrB2QDLYAKM7sZ6OPuRWb2c2AOoWGuU9x9cU2OLRKtlZt3cfMzn5Kbv4NLh3XmjrN607RR6GMxZlBHBYLUawcMCDM7P/wy28yeBWYBe/a1u/vM6r7W3S850Pd2942Ebh9FansVePVAXy9yKNydZ+ev47cvL6FxagM9DS0SwcGuIM6p9Ho3cHql9w5UGxAiiWr713uZODOX1xdvZMRRrfjLDwfStkWToMsSSTgHDAh3/0m8ChGJh49WbOGW5z5j69d7mHhGL64+sRsNGmghH5FIoh3F9ISZpVd6f3h4Kg2ROmFvWQV3v7aUHz86l6aNUnhx/AiuPam7wkHkAKLtpO7v7oX73rj7djMbFJuSRGrXqs27uCncEX3J0M7819n/6YgWkepF+ylpYGaHu/t2ADM7ogZfKxKIqh3Rky8bwuh+6ogWiVa0v+T/DHxsZs+H318E/E9sShI5dIW7Qx3Rry3ayPHdQx3R7VqqI1qkJqIKCHefambZwCnhTedrdlVJVB+t3MItz6ojWuRQRRUQZvaku18OLImwTSQh7C2r4C9vfsE/3ltJ11aH8fDYERyT2TLoskTqrGhvMfWt/Ca8ZsOQ2i9H5LvZvyO6E/91dh91RIscooM9ST0RuB1IC0/Ot+86fS/wUIxrEzkod+e57HXcNXtfR/RgRvdrH3RZIknhYA/K3Q3cbWZ3u/vEONUkEhV1RIvEVrTX4L82s8uAru7+ezPrBLR393kxrE2kWvs6orfs2sNtZ/TiGnVEi9S6aAPi70AFoVFMvwd2hbcdG6O6RCLaW1bBfW99weR3Qx3RL45XR7RIrEQbEMPcfbCZ5cA3T1I3imFdIt/y1ZavuemZHD7P28HFx3biN+eoI1oklqL9dJWGRy45gJllELqiEIk5d+f57DzuenkxqSkNePDHgznjGHVEi8RatAHxN+BFoI2Z/QG4ELgjZlVJvVZ5Jbd2LZuQ0bwRn+cVMbxbK/7yowG0b6l1oUXi4aABYWYNgK+AXwHfJzTUdUyclh+VemZWTj4TZ+ZSXFoOwIYdJWzYUcLZ/dtz/8WDSFFHtEjcHDQg3L3CzP7u7oOAZXGoSeqxSXOWfxMOleWsLVQ4iMRZVOtBAP8yswvMTJ9Qian1hcU12i4isRNtQFwLPA/sMbMiM9sZfrJapNas2fp1tVcJHdLV7yASb9HO5to81oVI/fb+l5v5+dM5pKYYDczYW/6fQXJpqSlMGNUzwOpE6qdolxz9VzTbRGrK3Xnk/VVcMWUe7Vo04fWbv8c9F/anY3oaBnRMT+Pu849hzKCOQZcqUu8cbLK+JkBToLWZHc5/JutrAegTK4ekpLSc22fmMjMnn1F92/KXHw7ksMYNObLVYQoEkQRwsFtM1wI3Ax2ABYQCwoGdwP/GtDJJaht3lHDtk9l8lreDX5zagxtOOUpzKYkkmAPeYnL3+929K/AHYGD49WPAKuDjONQnSWjBmm2c88AHrCjYxT8uH8JNpx6tcBBJQNGOYrrQ3YvM7ARCE/Y9AjwYu7IkWT0zby0XP/QJTRul8OL1IxjVt13QJYlINaINiH1PLp0FPOzu/wQ0WZ9ErbS8gt+8tIjbZuZyXLdWvHT9CHq01eA4kUQW7VxM+Wb2D+A04E9m1pjow0Xqua279jD+qYXM/WobV5/Ylf83uhcNU/TjI5Loog2IHwKjgXvdvdDM2gMTYleWJIvF63dwzdQFbN61h/t+NIAfDMoMuiQRiVK0D8rtBmZWer8B2BCroiQ5vPzZeibM+Iz0tEbMuG44/TPTgy5JRGogptf5ZjbFzArMbFE17WZmfzOzFWb2uZkNrtR2j5ktNrOl4X00zKWOKK9w7nl9GTdMz6Fvh5bMvmGEwkGkDor1jeDHCd2aqs4ZwNHhf9cQHhllZscDI4D+QD9CS5ueFMtCpXYUlZRy9dRs/u/fK7lkaCeevnoYbZo3CbosEfkOYrpeo7u/Z2ZdDrDLecBUd3fgEzNLD/dvONCE0EgpA1KBTbGsVQ7dys27uHpqNmu37ub3Y/px2bDO6MJPpO4KekHfjsC6Su/zgI7u/rGZvUOon8OAB6pboMjMriF09UHnzp1jXK5U551lBdw4PYfUhg146qfDGNatVdAlicghSsixhmZ2FNAbyCQUIqeY2YmR9nX3h9w9y92zMjIy4lmmEJps7//+vYIrn5hPpyOaMvvnIxQOIkki6CuIfKBTpfeZ4W2XAZ+4+y4AM3sNGA68H/cKpVrFe8uZMOMzXvl8A2f3b8+kCweQ1igl6LJEpJYEfQUxGxgbHs10HLAjPIR2LXCSmTU0s1RCHdRaAzuB5G3fzQUPfsQ/czfw/0b34n8vGaRwEEkyMb2CMLPpwEhC04XnAXcS6nDG3ScDrwJnAiuA3cBPwl86g9CcT7mEOqxfd/eXY1mrRO+TVVsZ/9RCSssrmHLFsZzcq03QJYlIDMR6FNMlB2l34PoI28sJTTUuCcTdmfbJGn778hI6t2rKw2Oz6J7RLOiyRCRGgu6DkDpiT1k5d760mGfmr+OUXm3468UDadEkNeiyRCSGFBByUAU7S/jZtIUsWLOd60/uzi2n9SRF6zeIJD0FhBzQ53mFXDN1ATuKS3ng0kGc3b9D0CWJSJwoIOQbs3LymTRnOesLi+mQnsbInq15fkE+Gc0aM+Nnw+nboWXQJYpIHCkgBAiFw8SZuRSXhtaGyi8s5qm56+iecRjPXTucVs0aB1yhiMRb0M9BSIKYNGf5N+FQWXFpucJBpJ5SQAgA6wuLI27fUFgS50pEJFEoIASAjOaRrxI6pKfFuRIRSRQKCGHh2u3sLCn91va01BQmjOoZQEUikggUEPXc28s2cenDn9CmRRPuOKs3HdPTMKBjehp3n38MYwZ1DLpEEQmIRjHVY89nr+O2mbn0bt+cx8YNJaN5Y356YregyxKRBKGAqIfcnQffXck9ry/nhKNaM/nyITRrrB8FEdmffivUMxUVzu9eWcLjH63m3AEduPeiATRqqDuNIvJtCoh6ZE9ZOb98LrTAz5UjunLHWb1poDmVRKQaCoh6YmdJKddNW8CHK7Zy2xm9uPZ73TBTOIhI9RQQ9cDmnXsY99g8lm3cyZ8vGsAFQzKDLklE6gAFRJJbveVrxk6Zx+ade3jkiixO7qnV30QkOgqIJLYofwfjHptHeYXz9NXDGNT58KBLEpE6RAGRpD74cgvXPplNetNGTL1qqJYGFZEaU0AkodmfreeXz31K94xmPHHlUNq2aBJ0SSJSBykgksyUD77id68sYWjXI3h4bBYt07RutIh8NwqIJOHu/On15Ux+dyWj+7bjrxcPpElqStBliUgdpoBIAqXlFdz2Qi4vLMzjx8M687vz+pGiB+BE5BApIOq43XvLuP6phbyzfDO/OLUHN37/KD0AJyK1QgFRh237ei9XPj6fz/MK+Z8fHMOlwzoHXZKIJBEFRB2Vt303Y6fMI297MQ9eNoRRfdsFXZKIJBkFRB20bGMRV0yZR/HecqZdNYyhXY8IuiQRSUIKiDpm7qqt/HRqNk0bpfD8dcfTs13zoEsSkSSlgKhDXl+0kRufyaHT4Wk8ceVQMg9vGnRJIpLEYrZSjJlNMbMCM1tUTbuZ2d/MbIWZfW5mgyu1dTazN8xsqZktMbMusaqzrpj2yRrGP7WAvh1aMOO64xUOIhJzsVxK7HFg9AHazwCODv+7BniwUttUYJK79waGAgUxqjHhuTv3vfkFd8xaxMiebXjqp8M4/LBGQZclIvVAzG4xuft7B/nL/zxgqrs78ImZpZtZe+BwoKG7vxn+PrtiVWOiK69w7pi1iOnz1nLhkEzuPv8YUlO0PKiIxEeQfRAdgXWV3ueFt2UChWY2E+gKvAXc5u7lkb6JmV1D6AqEzp2T5zmAktJybnomhzmLNzF+ZHcmjOqpB+BEJK4S8c/RhsCJwK3AsUA3YFx1O7v7Q+6e5e5ZGRkZ8akwxnYUlzL20Xm8sWQTd57Th1+N7qVwEJG4C/IKIh/oVOl9ZnhbQ+BTd18FYGazgOOAR+NdYDzNysln0pzlrC8sJqWBUeHO3y4exDkDOgRdmojUU0FeQcwGxoZHMx0H7HD3DcB8IN3M9l0OnAIsCarIeJiVk8/EmbnkFxbjQFmF07BBA8orPOjSRKQei+Uw1+nAx0BPM8szs6vM7Dozuy68y6vAKmAF8DAwHiDc13Ar8C8zywUs3J60Js1ZTnHp/l0se8srmDRneUAViYjEdhTTJQdpd+D6atreBPrHoq5EtL6wuEbbRUTiIRE7qeuVBWu2VdvWIT0tjpWIiOxPARGguau2MvbRebRq1ogmDff/X5GWmsKEUT0DqkxERAERmI9WbGHcY/Np17IJ/7zxRP54QX86pqdhQMf0NO4+/xjGDOoYdJkiUo9psr4AvPfFZq6ems2RrZry1E+PI6N5Y8YM6qhAEJGEooCIs3eWFXDttAV0z2jGtKuG0qpZ46BLEhGJSAERR28u2cT4pxbQs11zpl01jPSmmnRPRBKXAiJOXsvdwA3Tc+jbsSVTrxxKy7TUoEsSETkgdVLHwcufrefn03MY0CmdJ69SOIhI3aCAiLEXc/K46ZkchnQ+nCeuHEqLJgoHEakbdIsphp7PXsevXvic47q24tFxWTRtpNMtInWHriBi5Om5a5kw43NOOKo1U8Ydq3AQkTpHv7ViYOrHq/nNS4s5uWcGD142hCapKUGXJCJSYwqIWvboB1/x+1eWcFqftjxw6SAaN1Q4iEjdpICoRf94dyV3v7aMM/q14/6LB9Gooe7giUjdpYCoJQ+8/SX3vvEF5wzowH0/HEDDFIWDiNRtCohD5O789a0vuf9fX/KDQR2ZdGF/hYOIJAUFxCFwd+59Yzl/f2clFw3J5I8X9CelgQVdlohIrVBAfEfuzt2vLeOh91ZxydDO/GFMPxooHEQkiSggvgN353evLOGxD1dzxfAjuevcvpgpHEQkuSggaqiiwvnN7EVM+2QtV53QlTvO6q1wEJGkpICogYoK5/YXc3lm/jquPakbt43upXAQkaSlgIhSeYXzqxmf88LCPG445ShuOa2HwkFEkpoCIgpl5RX88vnPeOnT9fzi1B7cdOrRQZckIhJzCoiDKC2v4OZnP+Wfn29gwqieXH/yUUGXJCISFwqIA9hbVsEN0xcyZ/Emfn1mb67+XregSxIRiRsFRDX2lJVz/VMLeWtpAXed04dxI7oGXZKISFwpICIoKS3n2icX8O4Xm/nvMf247Lgjgy5JRCTuFBBVFO8t5+qp2Xy4cgt/uuAYfnRs56BLEhEJhAKikq/3lHHVE/OZ99U27r1wABcMyQy6JBGRwNT7gJiVk8+kOctZX1hMakoD9pZXcP/FAzlvYMegSxMRCVRM56U2sylmVmBmi6ppNzP7m5mtMLPPzWxwlfYWZpZnZg/Eor5ZOflMnJlLfmExDuwtryA1xXCPxdFEROqWWC9c8Dgw+gDtZwBHh/9dAzxYpf33wHsxqQyYNGc5xaXl+20rLXcmzVkeq0OKiNQZMQ0Id38P2HaAXc4DpnrIJ0C6mbUHMLMhQFvgjVjVt76wuEbbRUTqk6CXPusIrKv0Pg/oaGYNgD8Dtx7sG5jZNWaWbWbZmzdvrtHBO6Sn1Wi7iEh9EnRAVGc88Kq75x1sR3d/yN2z3D0rIyOjRgeZMKonaakp+21LS01hwqieNfo+IiLJKOhRTPlAp0rvM8PbhgMnmtl4oBnQyMx2uftttXnwMYNCI5X2jWLqkJ7GhFE9v9kuIlKfBR0Qs4Gfm9kzwDBgh7tvAH68bwczGwdk1XY47DNmUEcFgohIBDENCDObDowEWptZHnAnkArg7pOBV4EzgRXAbuAnsaxHRESiF9OAcPdLDtLuwPUH2edxQsNlRUQkjhK1k1pERAKmgBARkYgUECIiEpF5Ek08ZGabgTXf8ctbA1tqsZzaorpqRnXVjOqqmWSs60h3j/gQWVIFxKEws2x3zwq6jqpUV82orppRXTVT3+rSLSYREYlIASEiIhEpIP7joaALqIbqqhnVVTOqq2bqVV3qgxARkYh0BSEiIhEpIEREJKJ6FRCHukZ2gHWNNLMdZvZp+N9v4lRXJzN7x8yWmNliM7spwj5xP2dR1hX3c2ZmTcxsnpl9Fq7rtxH2aWxmz4bP11wz65IgdY0zs82VztdPY11XpWOnmFmOmb0SoS3u5yvKugI5X2a22sxyw8fMjtBeu59Hd683/4DvAYOBRdW0nwm8BhhwHDA3QeoaCbwSwPlqDwwOv24OfAH0CfqcRVlX3M9Z+Bw0C79OBeYCx1XZZzwwOfz6YuDZBKlrHPBAvH/Gwse+BXg60v+vIM5XlHUFcr6A1UDrA7TX6uexXl1B+CGskR1wXYFw9w3uvjD8eiewlNAysZXF/ZxFWVfchc/BrvDb1PC/qqNAzgOeCL+eAXzfzCwB6gqEmWUCZwGPVLNL3M9XlHUlqlr9PNargIhCxDWyA6qlquHhWwSvmVnfeB88fGk/iNBfn5UFes4OUBcEcM7CtyU+BQqAN9292vPl7mXADqBVAtQFcEH4tsQMM+sUoT0W/gr8Cqiopj2Q8xVFXRDM+XLgDTNbYGbXRGiv1c+jAqJuWEhovpQBwP8Cs+J5cDNrBrwA3OzuRfE89oEcpK5Azpm7l7v7QELL5w41s37xOO7BRFHXy0AXd+8PvMl//mqPGTM7Gyhw9wWxPlZNRFlX3M9X2AnuPhg4A7jezL4Xy4MpIPZX3RrZgXL3on23CNz9VSDVzFrH49hmlkrol/BT7j4zwi6BnLOD1RXkOQsfsxB4Bxhdpemb82VmDYGWwNag63L3re6+J/z2EWBIHMoZAZxrZquBZ4BTzGxalX2COF8HrSug84W754f/WwC8CAytskutfh4VEPubDYwNjwQ4jv+skR0oM2u3776rmQ0l9P8t5r9Uwsd8FFjq7n+pZre4n7No6grinJlZhpmlh1+nAacBy6rsNhu4Ivz6QuBtD/cuBllXlfvU5xLq14kpd5/o7pnu3oVQB/Tb7n5Zld3ifr6iqSuI82Vmh5lZ832vgdOBqiMfa/XzGNMlRxONJega2VHUdSHwMzMrA4qBi2P9IQkbAVwO5IbvXwPcDnSuVFsQ5yyauoI4Z+2BJ8wshVAgPefur5jZ74Bsd59NKNieNLMVhAYmXBzjmqKt60YzOxcoC9c1Lg51RZQA5yuauoI4X22BF8N/9zQEnnb3183sOojN51FTbYiISES6xSQiIhEpIEREJCIFhIiIRKSAEBGRiBQQIiISkQJCpBZYFDPMVtn/LjO7NV71iXwX9eo5CJEYKgN+6e4Lww8zLTCzN919SdCFiXxXuoIQqQXVzTBrZv82s/stNH//ovBT3fsxs6vDEwqmxbtukQPRFYRILYsww2xTdx8YnlhtCtCv0r4/JzT1xZhKc/uIJAQFhEgtqjrDbHhahOkQWvfDzFrsmxcJGEtoauYx7l4aRL0iB6JbTCK15AAzzFadz2bf+1ygC6EZN0USjgJCpBYcZIbZH4X3OYHQ7Jo7wttzgGuB2WbWIW7FikRJt5hEakd1M8wClJhZDqEZeq+s/EXu/kF4uOs/zew0d98Sr4JFDkazuYrEkJn9G7jV3bODrkWkpnSLSUREItIVhIiIRKQrCBERiUgBISIiESkgREQkIgWEiIhEpIAQEZGI/j/qoGWkcpHyxQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#define material\n", "mat = pmt.MatModel('nh', 'goh')\n", "\n", "label_force = {'cauchy': np.linspace(1,5,10), '1pk': np.linspace(1,5,10),'2pk': np.linspace(1,5,10)} #different types of force measurements used in for loop\n", "\n", "for key, value in label_force.items():\n", " sample = pmt.UniaxialExtension(mat, force_measure = key ) \n", " pmt.specify_single_fiber(sample)\n", "\n", " #force setting\n", " applied_force = value\n", " \n", " #calculating deformation (strech)\n", " disp = sample.force_controlled(applied_force, sample_params, x0 = [0.5])\n", " \n", " #post-process\n", " plt.plot(applied_force,disp,'-o')\n", " plt.ylabel('stretch')\n", " plt.xlabel(key)\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "c32b2947-082b-4c9a-9706-1a957dcf6bc1", "metadata": {}, "source": [ "## Combining different `'disp_measure'` and `'force_measure'`" ] }, { "cell_type": "markdown", "id": "a83bbe4c-4af0-4913-bf4d-b3225ac886b1", "metadata": {}, "source": [ "One can also freely combine different options for `disp_measure` and `force_measure`. For example, we used `disp_measure = 'strain'` and `force_measure = 'cauchy'`." ] }, { "cell_type": "code", "execution_count": 9, "id": "864d276b-b946-417d-8ce7-63e99b0371ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fiber directions set to 0 degrees ( 0.0 radians)\n" ] }, { "data": { "image/png": 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W8WD6VzRvEs/kS4/h7D4dol2W1GHhBEeumf0dOAuYYGaJaKgSkXrh2007ufXlhXyxejND+xzKvT/pR9tDEqNdltRx4QTHxcAw4EF3zzezw4CxkS1LRCJpz1Su97y+lDgzHrpoACMHpWj4cwlLOHdV7TSz9cCPgBVASfCniMSg9VuLuG3aIj7I2sAJR7Zh4kUDSNHYUlIN4Qw58kfgNmBcsCiBMJ8aN7NhZpZlZtlmdnsl6xPN7KVg/edm1iVY3sbMPjCz7Wb2WIV9jjGzzGCfv5h+RRIJ2xuL1nL2pI/5dOUmxp/fmxeuGqLQkGoLp6/iJ8AFwA4Ad88Dmu9vJzOLAx4HzgF6Az81s94VNrsK2OLu3YBHgAnB8iLgTuDWSg79JHA10D34GhZGG0QatIKdxdwwZT7X/ftLDm/dlDd+dxKXn9hV83/LAQknOHa7uwMOYGbNwjz2YCDb3Ve5+25gCj98cHA48Gzw+hXgDDMzd9/h7p9Q4Qn1oH+lhbt/FtT0HDAizHpEGqSPv9rA2ZM+4o1Fa7n5rB5M+80JdGt/SLTLkhgWTuf41OCuqmQzuxq4Evh/YeyXApQfsD+HHz77sXcbdy8xswKgDbBxH8fMqXDMSkdXM7NrgGsAUlNTwyhXpH7ZubuE/529jBc++5bu7Q/hqV8cS79OLaNdltQD+wyOoP/gJaAXsBXoCdzl7u/UQm0Hxd0nA5MB0tLSPMrliNSqed9s4ZapC/hm806uPqkrt5ytIUOk5uwzONzdzWy2u/cDqhsWuUDncu87Bcsq2ybHzOKBlsCm/Ryz036OKdJg7S4pY9K7X/G3j1ZyWMskXrz6OI47ok20y5J6JpxLVV+a2bHuPreax54LdDezroQ+3McAP6uwzSzgMmAOMAp4P+i7qJS7rzWzrWZ2HPA58Avgr9WsS6RemDk/l4npWeTlF9IxOYmfDUnl9UVrWbZ2K6PTOvOH846iuYYMkQgIJziGAD83s28I3VllhE5G+u9rp6DP4nogHYgDnnH3JWZ2N5Dh7rOAp4HnzSwb2EwoXAAws9VAC6CxmY0gNKHUUuBa4J9AEvBm8CXSoMycn8u46ZkUFodGBMrNL2RiehaHJMbx1C/SOLP3oVGuUOqzcIJj6IEe3N1nA7MrLLur3Osi4KIq9u1SxfIMoO+B1iRSH0xMz9obGuU1b5Kg0JCIC+d23D+7+zflvwgNqy4iUZKXX1jp8nUFmmNNIi+c4OhT/k3wYN8xkSlHRPZn847dJCZU/qPbUU+BSy2oMjjMbJyZbQP6Bx3SW4P364FXa61CEdnr4682MGzSx+wuKSO+wlPfSQlxjB3aM0qVSUNSZXC4+33u3hyY6O4tgq/m7t7G3cdVtZ+I1Lyi4lLGz1rCL575gpZJCbz+25N4MBic0ICU5CTuG9mPEQMrfR5WpEaF0zn+upk1c/cdZnYJoaljHw36OkQkwpbmbeXGl+bz1XfbueLELtw2rBdNEuLo3bGFgkKiIpzgeBIYYGYDgFuApwiNEXVKJAsTaejKypyngpn5WjZN4NkrB3NKj3bRLkskrOAoCZ4gHw485u5Pm9lVkS5MpCHLyy/klqkLmbNqE0P7HMp9I/vTulnjaJclAoQXHNvMbBxwCXCymTUiNCeHiETAawvzuGNGJiVlzgMX9ueitE6amU/qlHCCYzShoUKucvd1ZpYKTIxsWSINz9aiYv746hJmzM9lYGoyk0YfzeFtwp3FQKT2hDN17Drg4XLvvyXUxyEiNeSLrzdz00sLWLe1iBvP7M71p3UjPi6cx6xEal84ZxwiEiF7RrN98qOVpLZuysu/Pp5Bqa2iXZbIPik4RKIke/12bnppAZm5BYxO68xd5/emWaJ+JKXu2+//UjM7H3jD3ctqoR6Res/deeHzb7n3jaUkJcTxt0uOYVjfDtEuSyRs4XaOTzKzaYSGRl8e4ZpE6q0N23Zx27RFvL98PSf3aMeDo/rTvkWTaJclUi3hdI5fYmYtgJ8C/zQzB/4BvOju2yJdoEh98e7S77ht2iK27yph/Pm9ueyELrrNVmJSWLdtuPtW4BVgCnAY8BNCMwP+NoK1idQLO3eX8D8zMvnlcxm0b9GE1377Iy4/satCQ2JWOH0cFwBXAN0I3YY72N3Xm1lTYCmaulWkSoty8rlxygK+3rSDX51yBDef1YPE+LholyVyUMLp47gQeMTdPy6/0N13augRkcqVljlPfpjNpHdX0K55Iv/65RBOOLJttMsSqRH7vVTl7pdVDI1y697b175mNszMssws28xur2R9opm9FKz/3My6lFs3LlieZWZDyy2/ycyWmNliM3vRzNSzKHXKms07Gf33OTz49lec0+8w3rrhZIWG1Cv7DQ4zG2lmK8ysYM9kTma2NYz94oDHgXOA3sBPzax3hc2uAra4ezfgEWBCsG9vYAyh2QeHAU+YWZyZpQC/A9LcvS8QF2wnEnXuzrR5OZzz6H/IWreNSaOP5q8/HUjLphraTeqXcC5VPQCc7+7LqnnswUC2u68CMLMpwHBC/SJ7DAfGB69fAR6zUI/hcGCKu+8Cvjaz7OB43wY1J5lZMdAUyKtmXSI1Ln/nbu6YsZg3MtcyuGtrHr54AJ1aNY12WSIREU5wfHcAoQGQAqwp9z4HGFLVNu5eYmYFQJtg+WcV9k1x9zlm9iChACkE3nb3tyv75mZ2DXANQGpq6gGUL1K5mfNzmZieRV5+IR2Tkxh+dEemf5nLxu27+P2wnvzq5COJa6Q7pqT+qjI4zGxk8DLDzF4CZgK79qx39+mRLa3SmloROhvpCuQDL5vZJe7+QsVt3X0yMBkgLS3Na7NOqb9mzs9l3PRMCotLAcjNL+SJD1fSvnljZl53In1TWka5QpHI29cZx/nlXu8Ezi733oH9BUcu0Lnc+07Bssq2yTGzeKAlsGkf+54JfO3uGwDMbDpwAvCD4BCJhInpWXtDo7z4Ro0UGtJgVBkc7n7FQR57LtDdzLoS+tAfQ2hej/JmAZcBc4BRwPvBbIOzgH+b2cNAR6A78AVQBhwXPENSCJwBZBxknSJhy8svrHT52oKiWq5EJHrCuavqWTNLLve+lZk9s7/93L0EuB5IB5YBU919iZndHTxUCPA00Cbo/L4ZuD3YdwkwlVBH+lvAde5e6u6fE+pE/xLIDOqfHG5jRQ7Gd1uLaBxf+Y9Mx+SkWq5GJHrMfd+X/81svrsP3N+yuiwtLc0zMnRiIgfurcXrGDd9EduKijEzikv/7+cmKSGO+0b2Y8TAlChWKFLzzGyeu6dVXB7OWFWNgk7pPQdqjebxkAZix64SbntlEb9+YR6dWjUl/aZTmDhqACnJSRiQkpyk0JAGJ5wAeAiYY2YvA0aoL+LeiFYlUgcsWJPPjVPm883mnVx76pHceGYPGsc34sh2hygopEELZ1j158wsAzg9WDTS3Zfuax+RWFZSWsYTH67k0fdW0KFFE6ZcfRxDjmgT7bJE6oxwRsdNBbYTugNq7zJ3/zaShYlEw5rNO7nppQVkfLOF4Ud35O7hfWmZpCFDRMoL51LVG4Se2wBIIvTwXRahcaRE6gV3Z/qXufxx1hIMmDT6aF2OEqlCOJeq+pV/b2aDgGsjVpFILSvYWcz/zMzkjUVrGdylNQ9dPIDOrTXOlEhVqn13lLt/aWYVx5wSiUmfrtzILVMXsmHbLsYO7cmvT9E4UyL7E04fx83l3jYCBqERaSXG7S4p46F3spj88Sq6tmnG9GtPoH+n5GiXJRITwjnjaF7udQmhPo9pkSlHJPKy12/jhikLWJK3lZ8OTuXO846iaWM9miQSrnD6OP5UG4WIRJq788Jn3/DnN5bRLDGeyZcew9l9OkS7LJGYE86lqnbA7wndRbV3mlZ3P73KnUTqmA3bdnHbtEW8v3w9p/Rox8SL+tO+uWYdFjkQ4Zyf/wt4CTgP+DWh0Ww3RLIokZr03rLv+P0ri9i2q4Tx5/fmshO6EJpoUkQORDjB0cbdnzazG9z9I+AjM5sb6cJEDlbh7lLunb2UFz77ll4dmvPiNcfR49Dm+99RRPYpnOAoDv5ca2bnErqjqnXkShI5eItzC7hhynxWbtjB1Sd15dahPUmMj4t2WSL1QjjB8WczawncAvwVaAHcFNGqRA5QaZkz+eNVPPxOFm2aJfKvXw7hxG5to12WSL0Szl1VrwcvC4DTIluOyIHLzS/k5pcW8PnXm/lxvw7870/6kdy0cbTLEql3IjYDoEhtmrUwj2GTPmZxbgETR/Xn8Z8NUmiIREg4l6r6u3v+njfuvsXMYmb2P6nfthYV88dXlzBjfi4DU5OZNPpoDm/TLNplidRr4QRHIzNr5e5boHozAJrZMOBRIA54yt3vr7A+EXgOOAbYBIx299XBunHAVUAp8Dt3Tw+WJwNPAX0Jjdp7pbvPCaceiW0z5+cyMT2LvPxCOiYnceExKUybl8u6rUXceGZ3rj+tG/Fx4UxqKSIHo7ozAAJcRBgzAJpZHPA4cBaQA8w1s1kVJoG6Ctji7t3MbAwwARhtZr2BMYQeOuwIvGtmPdy9lFAQveXuo8ysMaBhTBuAmfNzGTc9k8LiUiDUn/GX97Jp3SyBqb86nmMOb7WfI4hITdnvr2fu/hwwEvgu+Brp7s+HcezBQLa7r3L33cAUYHiFbYYDzwavXwHOsNCTWcOBKe6+y92/BrKBwcHdXScDTwe17S5/GU3qr4npWXtDo7zE+DiFhkgtC+uSU3CWUN3pYlOANeXe5wAVh2Pfu427l5hZAdAmWP5ZhX1TgEJCT63/w8wGAPOAG9x9RzVrkxiTl19Y6fJ1BUW1XImIxNoF4XhCw7o/6e4DgR3A7ZVtaGbXmFmGmWVs2KARUmJZXn4hjeMr/6/aMTmplqsRkUgGRy7Qudz7TsGySrcxs3igJaFO8qr2zQFy3P3zYPkrhILkB9x9srunuXtau3btDrIpEg2h6VxzGDrpY8rcSYj7/vhSSQlxjB3aM0rViTRckQyOuUB3M+sadGKPAWZV2GYWoUETAUYB77u7B8vHmFmimXUFugNfuPs6YI2Z7fm0OIPqX0KTGLBp+y5+88KX3Dx1Ib06NOfdm09h4qgBpCQnYUBKchL3jeynecFFoiBis9cEfRbXA+mEbsd9xt2XmNndQIa7zyLUyf28mWUDmwmFC8F2UwmFQglwXXBHFcBvgX8FYbQKuCJSbZDoeGfpd4ybvoithSWMO6cXvzzpCOIaGYe3aaagEKkDLPQLfv2WlpbmGRkZ0S5D9mNbUTH3vL6UqRk5HHVYCx4ZPYBeHVpEuyyRBsvM5rl7WsXlmi9T6oQ5Kzdx68sLWVtQyHWnHckNZ/SoskNcRKJLwSFRVVRcysT0LJ7+5Gu6tm3Gy78+Qc9liNRxCg6JmsycAm6auoDs9dv5xfGHc/s5vWjaWP8lReo6/ZRKrSsuLeOJD1by1/dX0PaQRJ67cjAn99At0yKxQsEhtSp7/XZunrqARTkFjDi6I3+6oC8tmyZEuywRqQYFh9SKsjLnn5+uZsJby2naOI4nfj6IH/c7LNplicgBUHBIxOVs2cnYlxcxZ9UmzujVnvsu7Ef75k2iXZaIHCAFh0SMu/PKvBz+9NpS3J0JF/bj4rTOhAZAFpFYpeCQiNi4fRfjpmfyztLvGNy1NQ9dNIDOrTV1ikh9oOCQGvfW4nXcMSOTbbtK+MO5R3HliV1p1EhnGSL1hYJDaszWomLGz1rC9C9z6ZvSghcvPpoehzaPdlkiUsMUHFIj/pu9kbEvL+S7bbv43Rnd+e3p3UjQ/N8i9ZKCQw5K4e5SJry1nH9+upoj2jZj2m9O4OjOydEuS0QiSMEhB2zBmnxunrqAVRt2cPkJXbhtWC+SGsdFuywRiTAFh4Rl5vxcJqZnkZdfyGHJTejXsQXvLt/Aoc0T+dcvh3Bit7bRLlFEaomCQ/Zr5vxcxk3PpLA4NJdWXn4ReflFHHt4K56+4lhaNNGQISINiXovZb8mpmftDY3y8gqKFBoiDZCCQ/YrL7+wWstFpH7TpSqpUlFxKY++t4KqJhfumJxUq/WISN0Q0TMOMxtmZllmlm1mt1eyPtHMXgrWf25mXcqtGxcszzKzoRX2izOz+Wb2eiTrb8g+WbGRoZM+5skPVzKkayuaJHz/v0pSQhxjh/aMUnUiEk0RCw4ziwMeB84BegM/NbPeFTa7Ctji7t2AR4AJwb69gTFAH2AY8ERwvD1uAJZFqvaGbPOO3dw8dQGXPP05jcz499VDeOlXJ3D/yP6kJCdhQEpyEveN7MeIgSnRLldEoiCSl6oGA9nuvgrAzKYAw4Gl5bYZDowPXr8CPGahoVOHA1PcfRfwtZllB8ebY2adgHOBe4GbI1h/g+LuzJifyz2vL2VbUQm/Pb0b153WjSYJobweMTBFQSEiQGSDIwVYU+59DjCkqm3cvcTMCoA2wfLPKuy751NrEvB7YJ+DIJnZNcA1AKmpqQfUgIbim007uGPGYj7J3sig1GTuG9mfnh00xpSIVC6mOsfN7DxgvbvPM7NT97Wtu08GJgOkpaVV1b/boBWXlvHUf75m0rtfkRDXiHtG9OXng1M1kq2I7FMkgyMX6FzufadgWWXb5JhZPNAS2LSPfS8ALjCzHwNNgBZm9oK7XxKZJtRfC9bkc/u0RSxft41hfTow/oI+dGipWflEZP8iGRxzge5m1pXQh/4Y4GcVtpkFXAbMAUYB77u7m9ks4N9m9jDQEegOfOHuc4BxAMEZx60KjerZvquEB9OzeHbOag5t3oS/X3oMQ/t0iHZZIhJDIhYcQZ/F9UA6EAc84+5LzOxuIMPdZwFPA88Hnd+bCYULwXZTCXWklwDXufsPH12Wanl36Xfc+epi1m0t4tLjDmfs0J4015PfIlJN5l7/L/+npaV5RkZGtMuImvVbixj/2hJmZ66j56HNue/CfgxKbRXtskSkjjOzee6eVnF5THWOS/WUlTkvzv2W+99czq6SMsYO7ck1Jx+hCZZE5KAoOOqpFd9tY9z0TDK+2cIJR7bh3p/0o2vbZtEuS0TqAQVHPVNUXMoTH67kyQ+zaZYYz4MXDeDCQSmEnqsUETl4Co565PNVmxg3I5NVG3bwk4Ep/OHco2hzSGK0yxKRekbBUQ8U7CzmvjeXMWXuGjq3TuK5Kwdzco920S5LROopBUcMc3deX7SWP722lC07d/OrU47gxjN6aN5vEYkoBUeMytmykztnLuaDrA3079SSZ688lj4dW0a7LBFpABQcMaaktIx/frqah97+CjO467zeXHZCF+I0vpSI1BIFRx03c34uE9OzyMsvpG3zRBLjGpGTX8jpvdpzz4i+pGgWPhGpZQqOOmzm/FzGTc+ksDg02sqGbbsAuOz4wxl/QR/dYisiUaFHiOuwB9KX7w2N8t5dtl6hISJRozOOOsjd+SBrPXn5RZWuz8svrOWKRET+j4Kjjpn3zRYmvLmcL1ZvJq6RUVr2w0EoO6pfQ0SiSMFRR6z4bhsPpGfxztLvaHtIIveM6EvThEb8YeaS712uSkqIY+zQnlGsVEQaOgVHlOXlF/LIO18x7cscmjWO59aze3Dlj7rStHHonyauUaO9d1V1TE5i7NCejBiYsp+jiohEjoIjSrbs2M0TH2bz7JxvwOHKE7ty3WndaNWs8fe2GzEwRUEhInWKgqOW7dxdwjOffM3fP1rFjt0ljBzUiZvO6qHnMUQkZig4aklxaRlT5q7hL++tYMO2XZzV+1DGDu1Jj0ObR7s0EZFqiWhwmNkw4FFCc44/5e73V1ifCDwHHANsAka7++pg3TjgKqAU+J27p5tZ52D7QwEHJrv7o5Fsw8EqK3PeyFzLQ29nsXrTTgZ3ac3fLhnEMYe3jnZpIiIHJGLBYWZxwOPAWUAOMNfMZrn70nKbXQVscfduZjYGmACMNrPewBigD9AReNfMegAlwC3u/qWZNQfmmdk7FY5ZZ/xnxQYmvLWcxblb6dWhOc9cnsZpPdvr4T0RiWmRPOMYDGS7+yoAM5sCDAfKf8gPB8YHr18BHrPQp+pwYIq77wK+NrNsYLC7zwHWArj7NjNbBqRUOGbULVyTzwPpy/lv9iZSkpN4+OIBDD86RQMRiki9EMngSAHWlHufAwypaht3LzGzAqBNsPyzCvt+79YiM+sCDAQ+r+ybm9k1wDUAqampB9qGalm1YTsPvp3F7Mx1tG7WmLvO683Pj0slMV7zY4hI/RGTneNmdggwDbjR3bdWto27TwYmA6Slpf3w8esa9N3WIia9u4KpGWtoEt+IG87oztUnH8EhiTH51ysisk+R/GTLBTqXe98pWFbZNjlmFg+0JNRJXuW+ZpZAKDT+5e7TI1N6eAoKi/nbRyv5x3+/prTMufS4w7n+9G601TzfIlKPRTI45gLdzawroQ/9McDPKmwzC7gMmAOMAt53dzezWcC/zexhQp3j3YEvgv6Pp4Fl7v5wBGvfp6LiUp79dDVPfLiSrUXFDB/QkZvP6klqm6bRKklEpNZELDiCPovrgXRCt+M+4+5LzOxuIMPdZxEKgeeDzu/NhMKFYLuphDq9S4Dr3L3UzH4EXApkmtmC4Fv9j7vPjlQ7yispLWPalzk88s4K1m0t4tSe7fj90F707tiiNr69iEidYO4RvfxfJ6SlpXlGRka19ik/817H5Cac3edQPv5qIys37GBgajK3DevFcUe0iVDFIiLRZ2bz3D2t4nL13lai4sx7uflF/OO/39C+eSJ/v/QYzu59qJ7FEJEGS8FRiYnpWZXOvBcfZwzt0yEKFYmI1B2aOrYSVc2wt7aKGflERBoSBUclqpphTzPviYgoOCo1dmhPkhK+/7S3Zt4TEQlRH0cl9kycpJn3RER+SMFRBc28JyJSOV2qEhGRalFwiIhItSg4RESkWhQcIiJSLQoOERGplgYxyKGZbQC+OcDd2wIba7CcWKA2NwwNrc0Nrb1wcG3eCODuwyquaBDBcTDMLKOy0SHrM7W5YWhobW5o7YXItVmXqkREpFoUHCIiUi0Kjv2bHO0CokBtbhgaWpsbWnshQm1WH4eIiFSLzjhERKRaFBwiIlItDTY4zOwZM1tvZourWG9m9hczyzazRWY2qNy6y8xsRfB1We1VfXAOtM1mdrSZzTGzJcHy0bVb+YE7mH/nYH0LM8sxs8dqp+KDd5D/t1PN7G0zW2ZmS82sS60VfoAOsr0PBP+vlwXbWO1VfuDCaHOv4Gd2l5ndWmHdMDPLCv4+bj+gAty9QX4BJwODgMVVrP8x8CZgwHHA58Hy1sCq4M9WwetW0W5PhNvcA+gevO4IrAWSo92eSLa53PpHgX8Dj0W7LbXRZuBD4Kzg9SFA02i3J1LtBU4A/gvEBV9zgFOj3Z4aanN74FjgXuDWcsvjgJXAEUBjYCHQu7rfv8Gecbj7x8DmfWwyHHjOQz4Dks3sMGAo8I67b3b3LcA7wA+erKyLDrTN7v6Vu68IjpEHrAfaRb7ig3cQ/86Y2THAocDbka+05hxom82sNxDv7u8Ex9nu7jtroeSDchD/xg40IfQBmggkAN9Fut6asL82u/t6d58LFFdYNRjIdvdV7r4bmELo76daGmxwhCEFWFPufU6wrKrl9cF+22Zmgwn9oK2sxboiqdI2m1kj4CHg1kr3im1V/Tv3APLNbLqZzTeziWYWV+kRYkul7XX3OcAHhM6g1wLp7r4sCvXVphr5/FJwSNiC39KeB65w97Jo1xNh1wKz3T0n2oXUonjgJEJheSyhyxmXR7OgSDKzbsBRQCdCH56nm9lJ0a0qNig4qpYLdC73vlOwrKrl9UGVbTOzFsAbwB3B6X59UVWbjweuN7PVwIPAL8zs/tovLyKqanMOsCC4jFECzCR0HT3WVdXenwCfBZfkthPqBzk+CvXVphr5/FJwVG0WoQ8LM7PjgAJ3XwukA2ebWSszawWcHSyrDypts5k1BmYQuk78SnRLrHGVttndf+7uqe7ehdBv4M+5+4HdgVL3VPV/ey6h6/9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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#define material\n", "mat = pmt.MatModel('nh', 'goh')\n", "\n", "#set sample\n", "sample = pmt.UniaxialExtension(mat, disp_measure = 'strain ', force_measure = 'cauchy' )\n", "sample_params = sample.parameters\n", "pmt.specify_single_fiber(sample)\n", "\n", "#set parameter\n", "sample_params.set('mu_0',5)\n", "sample_params.set('A0',0.1)\n", "sample_params.set('L0',10)\n", "sample_params.set('k1_1',5)\n", "sample_params.set('k2_1',15)\n", "sample_params.set('k3_1',0.1)\n", "\n", "#calculating displacement (force)\n", "applied_strain = np.linspace(1,1.1,10)\n", "\n", "#calculating force (displacement)\n", "cauchy = sample.force_controlled(applied_force, sample_params, x0 = None)\n", "\n", "#post-process\n", "plt.plot(applied_strain,cauchy,'-o')\n", "plt.ylabel('cauchy stress')\n", "plt.xlabel('strain')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "7b20fa4c-34a3-4c0d-82ab-93ed2b369f24", "metadata": {}, "source": [ "## Fiber direction warning" ] }, { "cell_type": "markdown", "id": "c1fa1894-5b80-440b-a5ee-3612df10412a", "metadata": {}, "source": [ "We use `[1, 0, 0]` as a single fibre direction via `pmt.specify_single_fiber` in this example. One can manually specify fiber direction in the material models before using them to create a UniaxialExtension sample, as follows." ] }, { "cell_type": "code", "execution_count": 10, "id": "3bc7ebfd-df22-4bf1-a3b6-644859c9cdfb", "metadata": {}, "outputs": [], "source": [ "mat = pmt.MatModel('goh','nh')\n", "mat_models = mat.models\n", "mat_models[0].fiber_dirs = np.array([1,0,0])\n", "sample = pmt.UniaxialExtension(mat)" ] }, { "cell_type": "markdown", "id": "cb430744-27fa-49d8-8c1e-394d691fe81c", "metadata": {}, "source": [ "However, the `UniaxialExtension` assumes that fibers are aligned along the first direction such that the material is transversely isotropic. If this assumption is not satisfied, it can result in erroneous results. Thus, in such situations, a warning will be generated." ] }, { "cell_type": "code", "execution_count": 11, "id": "33bb818f-7f3b-4407-8eea-1edc6ac2cd8d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/pymecht/SampleExperiment.py:314: UserWarning: The UniaxialExtension assumes that fibers are aligned along the first direction. This is not satisfied and the results may be spurious.\n", " warnings.warn(\"The UniaxialExtension assumes that fibers are aligned along the first direction. This is not satisfied and the results may be spurious.\")\n" ] } ], "source": [ "mat = pmt.MatModel('goh','nh')\n", "mat_models = mat.models\n", "mat_models[0].fiber_dirs = np.array([1,1,0])\n", "sample = pmt.UniaxialExtension(mat)" ] } ], "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": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 5 }