| Physics-informed neural networks for phase-field method in two-phase flow | |
Qiu RD(丘润荻)1,2; Huang RF(黄仁芳)1; Xiao, Yao3; Wang JZ(王静竹)1; Zhang, Zhen4; Yue, Jieshun1; Zeng, Zhong3; Wang YW(王一伟)1,2,5
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| Corresponding Author | Wang, Yiwei([email protected]) |
| Source Publication | PHYSICS OF FLUIDS
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| 2022-05-01 | |
| Volume | 34Issue:5Pages:15 |
| ISSN | 1070-6631 |
| Abstract | The complex flow modeling based on machine learning is becoming a promising way to describe multiphase fluid systems. This work demonstrates how a physics-informed neural network promotes the combination of traditional governing equations and advanced interface evolution equations without intricate algorithms. We develop physics-informed neural networks for the phase-field method (PF-PINNs) in two-dimensional immiscible incompressible two-phase flow. The Cahn-Hillard equation and Navier-Stokes equations are encoded directly into the residuals of a fully connected neural network. Compared with the traditional interface-capturing method, the phase-field model has a firm physical basis because it is based on the Ginzburg-Landau theory and conserves mass and energy. It also performs well in two-phase flow at the large density ratio. However, the high-order differential nonlinear term of the Cahn-Hilliard equation poses a great challenge for obtaining numerical solutions. Thus, in this work, we adopt neural networks to tackle the challenge by solving high-order derivate terms and capture the interface adaptively. To enhance the accuracy and efficiency of PF-PINNs, we use the time-marching strategy and the forced constraint of the density and viscosity. The PF-PINNs are tested by two cases for presenting the interface-capturing ability of PINNs and evaluating the accuracy of PF-PINNs at the large density ratio (up to 1000). The shape of the interface in both cases coincides well with the reference results, and the dynamic behavior of the second case is precisely captured. We also quantify the variations in the center of mass and increasing velocity over time for validation purposes. The results show that PF-PINNs exploit the automatic differentiation without sacrificing the high accuracy of the phase-field method.& nbsp;Published under an exclusive license by AIP Publishing. |
| DOI | 10.1063/5.0091063 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000802776300002 |
| WOS Keyword | BENCHMARK COMPUTATIONS ; NONUNIFORM SYSTEM ; FREE-ENERGY ; MODEL ; SIMULATIONS |
| WOS Research Area | Mechanics ; Physics |
| WOS Subject | Mechanics ; Physics, Fluids & Plasmas |
| Funding Project | National Natural Science Foundation of China[12122214] ; National Natural Science Foundation of China[11872065] ; National Natural Science Foundation of China[52006232] ; National Natural Science Foundation of China[12002346] ; Youth Innovation Promotion Association CAS[Y201906] ; Youth Innovation Promotion Association CAS[Y2022019] |
| Funding Organization | National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS |
| Classification | 一类/力学重要期刊 |
| Ranking | 1 |
| Contributor | Wang, Yiwei |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/89469 |
| Collection | 流固耦合系统力学重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China; 2.Univ Chinese Acad Sci, Sch Future Technol, Beijing 100049, Peoples R China; 3.Chongqing Univ, Coll Aerosp Engn, Dept Engn Mech, Chongqing 400044, Peoples R China; 4.Shijiazhuang Tiedao Univ, Sch Civil Engn, Shijiazhuang 050043, Peoples R China; 5.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| Recommended Citation GB/T 7714 | Qiu RD,Huang RF,Xiao, Yao,et al. Physics-informed neural networks for phase-field method in two-phase flow[J]. PHYSICS OF FLUIDS,2022,34,5,:15.Rp_Au:Wang, Yiwei |
| APA | 丘润荻.,黄仁芳.,Xiao, Yao.,王静竹.,Zhang, Zhen.,...&王一伟.(2022).Physics-informed neural networks for phase-field method in two-phase flow.PHYSICS OF FLUIDS,34(5),15. |
| MLA | 丘润荻,et al."Physics-informed neural networks for phase-field method in two-phase flow".PHYSICS OF FLUIDS 34.5(2022):15. |
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| Jp2022FA402_2022_Phy（4062KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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