Data-driven optimization study of the multi-relaxation-time lattice Boltzmann method for solid-liquid phase change

doi:10.1007/s10483-023-2946-7

Applied Mathematics and Mechanics (English Edition) ›› 2023, Vol. 44 ›› Issue (1): 159-172.doi: https://doi.org/10.1007/s10483-023-2946-7

• 论文 • 上一篇

Data-driven optimization study of the multi-relaxation-time lattice Boltzmann method for solid-liquid phase change

Yanlin REN, Zhaomiao LIU, Zixiao KANG, Yan PANG

Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China

收稿日期:2022-07-19 修回日期:2022-09-09 出版日期:2023-01-01 发布日期:2022-12-24
通讯作者: Zhaomiao LIU, E-mail: lzm@bjut.edu.cn
基金资助:
the National Natural Science Foundation of China (Nos. 12172017 and 12202021)

Data-driven optimization study of the multi-relaxation-time lattice Boltzmann method for solid-liquid phase change

Yanlin REN, Zhaomiao LIU, Zixiao KANG, Yan PANG

Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China

Received:2022-07-19 Revised:2022-09-09 Online:2023-01-01 Published:2022-12-24
Contact: Zhaomiao LIU, E-mail: lzm@bjut.edu.cn
Supported by:
the National Natural Science Foundation of China (Nos. 12172017 and 12202021)

摘要/Abstract

摘要： Sharp phase interfaces and accurate temperature distributions are important criteria in the simulation of solid-liquid phase changes. The multi-relaxation-time lattice Boltzmann method (MRT-LBM) shows great numerical performance during simulation; however, the value method of the relaxation parameters needs to be specified. Therefore, in this study, a random forest (RF) model is used to discriminate the importance of different relaxation parameters to the convergence, and a support vector machine (SVM) is used to explore the decision boundary of the convergent samples in each dimensional model. The results show that the convergence of the samples is consistent with the sign of the decision number, and two types of the numerical deviations appear, i.e., the phase mushy zone and the non-physical heat transfer. The relaxation parameters chosen on the decision boundary can further suppress the numerical bias and improve numerical accuracy.

关键词: solid-liquid phase change, lattice Boltzmann method (LBM), relaxation parameter, random forest (RF), support vector machine (SVM)

Abstract: Sharp phase interfaces and accurate temperature distributions are important criteria in the simulation of solid-liquid phase changes. The multi-relaxation-time lattice Boltzmann method (MRT-LBM) shows great numerical performance during simulation; however, the value method of the relaxation parameters needs to be specified. Therefore, in this study, a random forest (RF) model is used to discriminate the importance of different relaxation parameters to the convergence, and a support vector machine (SVM) is used to explore the decision boundary of the convergent samples in each dimensional model. The results show that the convergence of the samples is consistent with the sign of the decision number, and two types of the numerical deviations appear, i.e., the phase mushy zone and the non-physical heat transfer. The relaxation parameters chosen on the decision boundary can further suppress the numerical bias and improve numerical accuracy.

Key words: solid-liquid phase change, lattice Boltzmann method (LBM), relaxation parameter, random forest (RF), support vector machine (SVM)

中图分类号:

76D17

Yanlin REN, Zhaomiao LIU, Zixiao KANG, Yan PANG. Data-driven optimization study of the multi-relaxation-time lattice Boltzmann method for solid-liquid phase change[J]. Applied Mathematics and Mechanics (English Edition), 2023, 44(1): 159-172.

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Data-driven optimization study of the multi-relaxation-time lattice Boltzmann method for solid-liquid phase change

Data-driven optimization study of the multi-relaxation-time lattice Boltzmann method for solid-liquid phase change

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