Accelerating phase-field simulation of multi-component alloy solidification by shallow artificial neural network

doi:10.1016/j.commatsci.2024.113594

IMR OpenIR

	Accelerating phase-field simulation of multi-component alloy solidification by shallow artificial neural network
	Gong, Tongzhao 1; Hao, Weiye 1,2; Fan, Weiqi 1,2; Chen, Yun 1; Chen, Xing-Qiu 1; Li, Dianzhong 1
通讯作者	Gong, Tongzhao(tzgong15s@imr.ac.cn) ; Chen, Yun(chenyun@imr.ac.cn)
	2025-02-01
发表期刊	COMPUTATIONAL MATERIALS SCIENCE
ISSN	0927-0256
卷号	248 页码:9
摘要	Low computing efficiency is a significant barrier in the phase-field modeling of multi-component alloys coupled with the CALPHAD (CALculation of PHAse Diagram) method. The fundamental issue is that the quasi-equilibrium thermodynamic data (QETD) required for calculating the chemical driving force must be acquired by repeatedly solving a large number of nonlinear equations. In this work, a novel method is developed to predict the QETD by a shallow neural network in a straightforward manner, so circumventing the repetitive numerical calculation of nonlinear quasi-equilibrium thermodynamic conditions. The numerical evaluation of a Ni-Cr-Al ternary alloy demonstrates that the proposed scheme can decrease the computing consuming to about 1/80 of that required by the conventional phase-field method when the computational domain size reaches the millimeter scale, while accurately reproducing the equiaxed dendritic growth and solute segregation kinetics during polycrystalline solidification. The method presented in this work will provide an effective tool for modeling the microstructure evolution of complex materials involved in practical engineering applications.
关键词	Phase-field method Machine learning Solidification Multi-component alloy Calculation of phase diagram
资助者	National Science and Technology Major Project ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Youth Talent Program of Shenyang National Laboratory for Materials Science ; Special Projects of the Central Government in Guidance of Local Science and Technology Development
DOI	10.1016/j.commatsci.2024.113594
收录类别	SCI
语种	英语
资助项目	National Science and Technology Major Project[J2019-VI-0019-0134] ; National Natural Science Foundation of China[52203301] ; China Postdoctoral Science Foundation[2021TQ0335] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDC0160301] ; Youth Talent Program of Shenyang National Laboratory for Materials Science ; Special Projects of the Central Government in Guidance of Local Science and Technology Development[2024010859-JH6/1006]
WOS研究方向	Materials Science
WOS类目	Materials Science, Multidisciplinary
WOS记录号	WOS:001375496900001
出版者	ELSEVIER
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/181468
专题	中国科学院金属研究所
通讯作者	Gong, Tongzhao; Chen, Yun
作者单位	1.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang, Peoples R China 2.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Peoples R China
推荐引用方式 GB/T 7714	Gong, Tongzhao,Hao, Weiye,Fan, Weiqi,et al. Accelerating phase-field simulation of multi-component alloy solidification by shallow artificial neural network[J]. COMPUTATIONAL MATERIALS SCIENCE,2025,248:9.
APA	Gong, Tongzhao,Hao, Weiye,Fan, Weiqi,Chen, Yun,Chen, Xing-Qiu,&Li, Dianzhong.(2025).Accelerating phase-field simulation of multi-component alloy solidification by shallow artificial neural network.COMPUTATIONAL MATERIALS SCIENCE,248,9.
MLA	Gong, Tongzhao,et al."Accelerating phase-field simulation of multi-component alloy solidification by shallow artificial neural network".COMPUTATIONAL MATERIALS SCIENCE 248(2025):9.