beingujjwalraj/Multiscale-Modelling-of-Material-Using-Machine-Learning
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.
This project helps material scientists and engineers understand how tiny grain sizes in materials like copper heat pipes affect their overall strength and heat transfer properties. It takes detailed, grain-level material data and uses machine learning to predict how a larger component will behave, giving you insights into optimizing material designs for better performance. The output is a more accurate simulation of how materials respond to stress and heat, considering their microstructure.
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Use this if you need to integrate microstructural details like grain size into your finite element analysis (FEA) to improve predictions of material strength, stress distribution, and thermal performance.
Not ideal if your primary goal is general-purpose machine learning model development or if you're not working with material science, multiscale modeling, or FEA.
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Dec 05, 2024
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