Accelerating phase-field simulation of multi-component alloy solidification by shallow artificial neural network
文献类型：期刊论文
| 作者 | Gong, Tongzhao1; Hao, Weiye1,2; Fan, Weiqi1,2; Chen, Yun1; Chen, Xing-Qiu1; Li, Dianzhong1 |
| 刊名 | COMPUTATIONAL MATERIALS SCIENCE
 |
| 出版日期 | 2025-02-01 |
| 卷号 | 248页码:9 |
| 关键词 | Phase-field method Machine learning Solidification Multi-component alloy Calculation of phase diagram |
| ISSN号 | 0927-0256 |
| DOI | 10.1016/j.commatsci.2024.113594 |
| 通讯作者 | Gong, Tongzhao(tzgong15s@imr.ac.cn) ; Chen, Yun(chenyun@imr.ac.cn) |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:001375496900001 |
| 出版者 | ELSEVIER |
| 资助机构 | 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 |
| 源URL |  |
| 专题 | 金属研究所_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. |
入库方式： OAI收割
来源：金属研究所
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