{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Elasticity in 2D for 3 Phases\n", "\n", "## Introduction\n", "\n", "This example provides a demonstration of using PyMKS to compute the linear strain field for a three-phase composite material. It demonstrates how to generate data for delta microstructures and then use this data to calibrate the first order MKS influence coefficients. The calibrated influence coefficients are used to predict the strain response for a random microstructure and the results are compared with those from finite element. Finally, the influence coefficients are scaled up and the MKS results are again compared with the finite element data for a large problem.\n", "\n", "PyMKS uses the finite element tool [SfePy](http://sfepy.org) to generate both the strain fields to fit the MKS model and the verification data to evaluate the MKS model's accuracy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Elastostatics Equations and Boundary Conditions\n", "\n", "The governing equations for linear elasticity and the boundary conditions used in this example are the same as those provided in the [Linear Elastic in 2D example](./elasticity.ipynb). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modeling with MKS\n", "\n", "### Calibration Data and Delta Microstructures\n", "\n", "The first order MKS influence coefficients are all that is needed to compute a strain field of a random microstructure as long as the ratio between the elastic moduli (also known as the contrast) is less than 1.5. If this condition is met we can expect a mean absolute error of 2% or less when comparing the MKS results with those computed using finite element methods [[1]](#References). \n", "\n", "Because we are using distinct phases and the contrast is low enough to only need the first-order coefficients, delta microstructures and their strain fields are all that we need to calibrate the first-order influence coefficients [[2]](#References). \n", "\n", "The `generate_delta` function can be used to create the two delta microstructures needed to calibrate the first-order influence coefficients for a two phase microstructure. This function uses the Python module [SfePy](http://sfepy.org/doc-devel/index.html) to compute the strain fields using finite element methods." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import dask.array as da\n", "import numpy as np\n", "from sklearn.pipeline import Pipeline\n", "\n", "from pymks import (\n", " generate_delta,\n", " plot_microstructures,\n", " solve_fe,\n", " PrimitiveTransformer,\n", " LocalizationRegressor,\n", " coeff_to_real,\n", " \n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x_delta = generate_delta(n_phases=3, shape=(21, 21)).persist()\n", "\n", "plot_microstructures(*x_delta[:3], titles=['[0]', '[1]', '[2]'], cmap='gray')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using delta microstructures for the calibration of the first-order influence coefficients is essentially the same, as using a unit [impulse response](http://en.wikipedia.org/wiki/Impulse_response) to find the kernel of a system in signal processing. Any given delta microstructure is composed of only two phases with the center cell having an alternative phase from the remainder of the domain. The number of delta microstructures that are needed to calibrated the first-order coefficients is $N(N-1)$ where $N$ is the number of phases, therefore in this example 6 delta microstructures are required." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generating Calibration Data\n", "\n", "In this example, the microstructures have three phases with elastic moduli values of 80, 100 and 120 and Poisson's ratio values all equal to 0.3. The macroscopic imposed strain is 0.02.\n", "\n", "A helper function `strain_xx` is created to solve the finite element problem and return the $\\varepsilon_{xx}$ component of the strain. The length of the values for the `elastic_modulus` and `poissons_ratio` parameteres indicate the number of phases." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#PYTEST_VALIDATE_IGNORE_OUTPUT\n", "\n", "strain_xx = lambda x: solve_fe(\n", " x,\n", " elastic_modulus=(80, 100, 120),\n", " poissons_ratio=(0.3, 0.3, 0.3),\n", " macro_strain=0.02\n", " )['strain'][...,0]\n", "\n", "y_delta = strain_xx(x_delta).persist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observe strain field." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_microstructures(y_delta[0], titles=[r'$\\mathbf{\\varepsilon_{xx}}$'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calibrating First-Order Influence Coefficients\n", "\n", "Calibrate the influence coefficients by creating a model pipeline using the `PrimitiveTransformer` and the `LocalizationRegressor`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "model = Pipeline(steps=[\n", " ('discretize', PrimitiveTransformer(n_state=3, min_=0.0, max_=2.0)),\n", " ('regressor', LocalizationRegressor())\n", "])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, pass the delta microstructures and their strain fields into the `fit` method to calibrate the first-order influence coefficients." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "model.fit(x_delta, y_delta);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observe the influence coefficients." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "to_real = lambda x: coeff_to_real(x.steps[1][1].coeff).real\n", "\n", "coeff = to_real(model)\n", "plot_microstructures(\n", " coeff[..., 0],\n", " coeff[..., 1],\n", " coeff[..., 2],\n", " titles=['Influence coeff [0]', 'Influence coeff [1]', 'Influence coeff [2]']\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Predict of the Strain Field for a Random Microstructure\n", "\n", "Use the calibrated `model` to compute the strain field for a random two phase microstructure and compare it with the results from a finite element simulation. The `strain_xx` helper function is used to generate the strain field." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "#PYTEST_VALIDATE_IGNORE_OUTPUT\n", "\n", "da.random.seed(99)\n", "\n", "x_data = da.random.randint(2, size=(1,) + x_delta.shape[1:]).persist()\n", "y_data = strain_xx(x_data).persist()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_microstructures(\n", " y_data[0],\n", " titles=[r'FE - $\\mathbf{\\varepsilon_{xx}}$']\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note that the calibrated influence coefficients can only be used to reproduce the simulation with the same boundary conditions that they were calibrated with.**\n", "\n", "Now to get the strain field from the model, pass the same microstructure to the `predict` method." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "y_predict = model.predict(x_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finall, compare the results from finite element simulation and the MKS model." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_microstructures(\n", " y_data[0],\n", " y_predict[0],\n", " titles=[\n", " r'$\\mathbf{\\varepsilon_{xx}}$ - FE',\n", " r'$\\mathbf{\\varepsilon_{xx}}$ - MKS'\n", " ]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the difference between the two strain fields." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_microstructures(\n", " (y_data - y_predict)[0],\n", " titles=['FE - MKS']\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The MKS model is able to capture the strain field for the random microstructure after being calibrated with delta microstructures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resizing the Coefficeints to use on Larger Microstructures \n", "\n", "The influence coefficients that were calibrated on a smaller microstructure can be used to predict the strain field on a larger microstructure though spectral interpolation [[3]](#ref3), but accuracy of the MKS model drops slightly. To demonstrate how this is done, generate a new larger random microstructure and its strain field." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#PYTEST_VALIDATE_IGNORE_OUTPUT\n", "\n", "new_shape = tuple(np.array(x_delta.shape[1:]) * 3)\n", "x_large = da.random.randint(2, size=(1,) + new_shape).persist()\n", "y_large = strain_xx(x_large).persist()\n", "\n", "plot_microstructures(y_large[0], titles=[r'$\\mathbf{\\varepsilon_{xx}}$ - FE (large)'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The influence coefficients that have already been calibrated need to be resized to match the shape of the new larger microstructure that we want to compute the strain field for. This can be done by passing the shape of the new larger microstructure into the `coeff_resize` method." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "model.steps[1][1].coeff_resize(x_large[0].shape);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observe the resized influence coefficients." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "coeff_large = to_real(model)\n", "plot_microstructures(\n", " coeff_large[..., 0],\n", " coeff_large[..., 1],\n", " coeff_large[..., 2],\n", " titles=['Influence coeff [0]', 'Influence coeff [1]', 'Influence coeff [2]']\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resized coefficients will only work with the large microstructure now." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_large_predict = model.predict(x_large).persist()\n", "\n", "plot_microstructures(\n", " y_large[0],\n", " y_large_predict[0],\n", " titles=[\n", " r'$\\mathbf{\\varepsilon_{xx}}$ - FE (large)',\n", " r'$\\mathbf{\\varepsilon_{xx}}$ - MKS (large)'\n", " ]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the difference between the two strain fields." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_microstructures(\n", " (y_large - y_large_predict)[0],\n", " titles=['FE - MKS']\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results from the strain field computed with the resized influence coefficients are not as accurate as they were before they were resized. This decrease in accuracy is expected when using spectral interpolation [[4]](#References)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## References\n", "\n", "\n", "[1] Binci M., Fullwood D., Kalidindi S.R., A new spectral framework for establishing localization relationships for elastic behavior of composites and their calibration to finite-element models. Acta Materialia, 2008. 56 (10) p. 2272-2282 [doi:10.1016/j.actamat.2008.01.017](http://dx.doi.org/10.1016/j.actamat.2008.01.017).\n", "\n", "\n", "[2] Landi, G., S.R. Niezgoda, S.R. Kalidindi, Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n", "\n", "\n", "[3] Marko, K., Kalidindi S.R., Fullwood D., Computationally efficient database and spectral interpolation for fully plastic Taylor-type crystal plasticity calculations of face-centered cubic polycrystals. International Journal of Plasticity 24 (2008) 1264–1276 [doi:10.1016/j.ijplas.2007.12.002](http://dx.doi.org/10.1016/j.ijplas.2007.12.002).\n", "\n", "\n", "[4] Marko, K. Al-Harbi H. F. , Kalidindi S.R., Crystal plasticity simulations using discrete Fourier transforms. Acta Materialia 57 (2009) 1777–1784 [doi:10.1016/j.actamat.2008.12.017](http://dx.doi.org/10.1016/j.actamat.2008.12.017)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "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": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 1 }