Phase-field physics-informed neural networks for multiphase flow simulations in axisymmetric coordinates with applications of bubble rising in multiple regimes
文献类型：期刊论文
| 作者 | Huang CC(黄驰超)3,4; Wang GH(王广航)3,4; Wang JZ(王静竹)1,2,4; Qiu RD(丘润荻)4; Wang YW(王一伟)2,3,4 |
| 刊名 | PHYSICS OF FLUIDS
 |
| 出版日期 | 2025-08-01 |
| 卷号 | 37期号:8页码:14 |
| ISSN号 | 1070-6631 |
| DOI | 10.1063/5.0281713 |
| 通讯作者 | Qiu, Rundi(qiurundi@imech.ac.cn) ; Wang, Yiwei(wangyw@imech.ac.cn) |
| 英文摘要 | While physics-informed neural networks are becoming a promising approach for multiphase flow simulations, the transition from two-dimensional to three-dimensional applications is primarily hindered by computational inefficiency. This study presents a phase-field Physics-Informed Neural Networks (PF-PINNs) specifically designed for axisymmetric multiphase flow dynamics. Adaptive time marching strategy and axisymmetric weighted sampling strategy are introduced to ensure training convergence. The framework is validated through bubble rising simulations in infinite fluid domain. Results show that the shape of the interface coincides well with numerical validation. Dynamic behaviors, including centroid trajectories, rising velocity and velocity fields are precisely captured. We also evaluate the applicable range of this method with cases over different Reynolds numbers and Bond numbers. The prediction succeeds in Re=[10, 200] and Bo=[10, 200], which demonstrates the generality of PF-PINNs in axisymmetric coordinate and the ability to capture different bubble dynamics in different regimes. |
| 分类号 | 一类/力学重要期刊 |
| WOS关键词 | COAGULATION FLOTATION PROCESS ; 2-PHASE INCOMPRESSIBLE FLOWS ; DEEP LEARNING FRAMEWORK ; MICRO-BUBBLES ; COMPUTATIONS |
| 资助项目 | National Natural Science Foundation of China10.13039/501100001809[12293003] ; National Natural Science Foundation of China10.13039/501100001809[12293000] ; National Natural Science Foundation of China10.13039/501100001809[12293004] ; National Natural Science Foundation of China10.13039/501100001809[52441101] ; National Natural Science Foundation of China10.13039/501100001809[12272382] ; National Natural Science Foundation of China10.13039/501100001809[U22B6010] ; National Natural Science Foundation of China[2022019] ; Youth Innovation Promotion Association of Chinese Academy of Sciences |
| WOS研究方向 | Mechanics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001544290500031 |
| 资助机构 | National Natural Science Foundation of China10.13039/501100001809 ; National Natural Science Foundation of China ; Youth Innovation Promotion Association of Chinese Academy of Sciences |
| 其他责任者 | 丘润荻 ; 王一伟 |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/103737]  |
| 专题 | 力学研究所_流固耦合系统力学重点实验室(2012-) |
| 作者单位 | 1.Guangdong Aerosp Res Acad, Guangzhou 511458, Peoples R China 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China; 3.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China; 4.Chinese Acad Sci, Key Lab Mech Fluid Solid Coupling Syst, Inst Mech, Beijing 100190, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Huang CC,Wang GH,Wang JZ,et al. Phase-field physics-informed neural networks for multiphase flow simulations in axisymmetric coordinates with applications of bubble rising in multiple regimes[J]. PHYSICS OF FLUIDS,2025,37(8):14. |
| APA | 黄驰超,王广航,王静竹,丘润荻,&王一伟.(2025).Phase-field physics-informed neural networks for multiphase flow simulations in axisymmetric coordinates with applications of bubble rising in multiple regimes.PHYSICS OF FLUIDS,37(8),14. |
| MLA | 黄驰超,et al."Phase-field physics-informed neural networks for multiphase flow simulations in axisymmetric coordinates with applications of bubble rising in multiple regimes".PHYSICS OF FLUIDS 37.8(2025):14. |
入库方式： OAI收割
来源：力学研究所
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