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2023 | OriginalPaper | Chapter

Data Assimilation for Microstructure Evolution in Kinetic Monte Carlo

Authors : Anh Tran, Yan Wang, Theron Rodgers

Published in: TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings

Publisher: Springer Nature Switzerland

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Abstract

Modeling grain growth has been a subject of interest in computational material science, as it occurs in thermal-based processing methods such as annealing and sintering. Kinetic Monte Carlo with Potts model is often used as an integrated computational materials engineering (ICME) grain growth model and can generate high-fidelity synthetic microstructures. In this paper, we offer a data-driven stochastic calculus perspective on the kinetics of grain growth and model the microstructure evolution through the lens of stochastic differential equations, based on Langevin dynamics and Fokker-Planck equation to forecast the grain size distribution. We demonstrate that our proposed approach agrees reasonably well with the hybrid Potts-phase field model using SPPARKS in forecasting the long-term evolution of grain size distribution.

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Metadata
Title
Data Assimilation for Microstructure Evolution in Kinetic Monte Carlo
Authors
Anh Tran
Yan Wang
Theron Rodgers
Copyright Year
2023
Book
TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings

Print ISBN: 978-3-031-22523-9

Electronic ISBN: 978-3-031-22524-6

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-22524-6_50

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