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Direct numerical simulations of three-dimensional two-phase flow using physics-informed neural networks with a distributed parallel training algorithm

Published online by Cambridge University Press:  15 August 2025

Rundi Qiu ,
Junzhe Li ,
Jingzhu Wang Open the ORCID record for Jingzhu Wang [Opens in a new window] ,
Chun Fan Open the ORCID record for Chun Fan [Opens in a new window]  and
Yiwei Wang Open the ORCID record for Yiwei Wang [Opens in a new window]
Rundi Qiu
Affiliation:
Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, PR China School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, PR China
Junzhe Li
Affiliation:
School of Computer Science, Peking University, Beijing 100871, PR China Computer Center, Peking University, Beijing 100871, PR China
Jingzhu Wang
Affiliation:
Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, PR China School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, PR China
Chun Fan
Affiliation:
Computer Center, Peking University, Beijing 100871, PR China Changsha Institute for Computing and Digital Economy, Peking University, Changsha 410000, PR China
Yiwei Wang*
Affiliation:
Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, PR China School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, PR China School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, PR China
*
Corresponding author: Yiwei Wang, wangyw@imech.ac.cn
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Abstract

In recent years, integrating physical constraints within deep neural networks has emerged as an effective approach for expediting direct numerical simulations in two-phase flow. This paper introduces physics-informed neural networks (PINNs) that utilise the phase-field method to model three-dimensional two-phase flows. We present a fully connected neural network architecture with residual blocks and spatial parallel training using the overlapping domain decomposition method across multiple graphics processing units to enhance the accuracy and computational efficiency of PINNs for the phase-field method (PF-PINNs). The proposed PINNs framework is applied to a bubble rising scenario in a three-dimensional infinite water tank to quantitatively assess the performance of PF-PINNs. Furthermore, the computational cost and parallel efficiency of the proposed method was evaluated, demonstrating its potential for widespread application in complex training environments.

JFM classification

Multiphase and Particle-laden Flows: Gas/liquid flow Mathematical Foundations: Machine learning Mathematical Foundations: Computational methods

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Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 1017 , 25 August 2025 , A15
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Copyright
© The Author(s), 2025. Published by Cambridge University Press

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