A Coupled Machine Learning and Lattice Boltzmann Method Approach for Immiscible Two-Phase Flows

Peisheng Li, Hongsheng Zhou, Zhaoqing Ke, Shuting Zhao, Ying Zhang (), Jiansheng Liu () and Yuan Tian ()
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Peisheng Li: School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China
Hongsheng Zhou: School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China
Zhaoqing Ke: School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China
Shuting Zhao: School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China
Ying Zhang: School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China
Jiansheng Liu: School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China
Yuan Tian: School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China

Mathematics, 2023, vol. 12, issue 1, 1-20

Abstract: An innovative coupling numerical algorithm is proposed in the current paper, the front-tracking method–lattice Boltzmann method–machine learning (FTM-LBM-ML) method, to precisely capture fluid flow phase interfaces at the mesoscale and accurately simulate dynamic processes. This method combines the distinctive abilities of the FTM to accurately capture phase interfaces and the advantages of the LBM for easy handling of mesoscopic multi-component flow fields. Taking a single vacuole rising as an example, the input and output sets of the machine learning model are constructed using the FTM’s flow field, such as the velocity and position data from phase interface markers. Such datasets are used to train the Bayesian-Regularized Back Propagation Neural Network (BRBPNN) machine learning model to establish the corresponding relationship between the phase interface velocity and the position. Finally, the trained BRBPNN neural network is utilized within the multi-relaxation LBM pseudo potential model flow field to predict the phase interface position, which is compared with the FTM simulation. It was observed that the BRBPNN-predicted interface within the LBM exhibits a high degree of consistency with the FTM-predicted interface position, showing that the BRBPNN model is feasible and satisfies the accuracy requirements of the FT-LB coupling model.

Keywords: front-tracking method; lattice Boltzmann method; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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