This repository demonstrates multiscale modeling of copper heat pipes using machine learning, integrating grain-scale data with FEA via a UMAT. It highlights grain size’s impact on stress, strain, and heat transfer for optimized material design.
machine-learning fortran abaqus umat finite-element-analysis ansys stress-strain polynomialregression multiscale-modeling heattransfer vpsc hall-petch-effect materialoptimization
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Updated
Dec 5, 2024 - Jupyter Notebook