A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution

Tran, Anh; Wildey, Tim; Sun, Jing; Liu, Dehao; Wang, Yan

doi:10.1115/1.4054237

A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution

Journal Article · 10 May 2022 · Journal of Computing and Information Science in Engineering

DOI:https://doi.org/10.1115/1.4054237· OSTI ID:1882900

Tran, Anh ^[1]; Wildey, Tim ^[1]; Sun, Jing ^[2]; Liu, Dehao ^[3]; Wang, Yan ^[3]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
U.S. Food and Drug Administration (FDA), Silver Spring, MD (United States). Office of Biostatistics, Office of Translational Sciences
Georgia Institute of Technology, Atlanta, GA (United States)

Integrated computational materials engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), is modeled by the Fokker–Planck equation. In this work, we discuss how to calibrate the drift and diffusion terms of the Fokker–Planck equation from the theoretical and computational perspectives. The calibrated Fokker–Planck equation can be used as a stochastic reduced-order model to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

Other Award/Contract Number:: CMMI-1306996

OSTI ID:: 1882900

Report Number(s):: SAND2022-2850J; 704066

Journal Information:: Journal of Computing and Information Science in Engineering, Journal Name: Journal of Computing and Information Science in Engineering Journal Issue: 6 Vol. 22; ISSN 1530-9827

Publisher:: ASMECopyright Statement

Country of Publication:: United States

Language:: English

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A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution

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