Physics-informed neural networks for phase-field method in two-phase flow

doi:10.1063/5.0091063

CORC > 力学研究所 > 中国科学院力学研究所 > 流固耦合系统力学重点实验室(2012-)

	Physics-informed neural networks for phase-field method in two-phase flow
	Qiu, Rundi 4,5; Huang, Renfang 5; Xiao, Yao 3; Wang, Jingzhu 5; Zhang, Zhen 2; Yue, Jieshun 5; Zeng, Zhong 3; Wang, Yiwei 1,4,5
刊名	PHYSICS OF FLUIDS
	2022-05-01
卷号	34 期号:5 页码:15
ISSN号	1070-6631
DOI	10.1063/5.0091063
通讯作者	Wang, Yiwei(wangyw@imech.ac.cn)
英文摘要	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.
资助项目	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]
WOS关键词	BENCHMARK COMPUTATIONS ; NONUNIFORM SYSTEM ; FREE-ENERGY ; MODEL ; SIMULATIONS
WOS研究方向	Mechanics ; Physics
语种	英语
WOS记录号	WOS:000802776300002
资助机构	National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/89468]
专题	力学研究所_流固耦合系统力学重点实验室(2012-)
通讯作者	Wang, Yiwei
作者单位	1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Shijiazhuang Tiedao Univ, Sch Civil Engn, Shijiazhuang 050043, Peoples R China 3.Chongqing Univ, Coll Aerosp Engn, Dept Engn Mech, Chongqing 400044, Peoples R China 4.Univ Chinese Acad Sci, Sch Future Technol, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Qiu, Rundi,Huang, Renfang,Xiao, Yao,et al. Physics-informed neural networks for phase-field method in two-phase flow[J]. PHYSICS OF FLUIDS,2022,34(5):15.
APA	Qiu, Rundi.,Huang, Renfang.,Xiao, Yao.,Wang, Jingzhu.,Zhang, Zhen.,...&Wang, Yiwei.(2022).Physics-informed neural networks for phase-field method in two-phase flow.PHYSICS OF FLUIDS,34(5),15.
MLA	Qiu, Rundi,et al."Physics-informed neural networks for phase-field method in two-phase flow".PHYSICS OF FLUIDS 34.5(2022):15.