Low Capillary Elastic Flow Model Optimization Using the Lattice Boltzmann Method and Non-Dominated Sorting Genetic Algorithm.

Yaqi Hou, Wei Zhang, Jiahua Hu, Feiyu Gao, Xuexue Zong

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Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Yaqi HouShanxi Key Laboratory of Chemical Product Engineering, College of Chemistry and Chemical Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Wei ZhangShanxi Key Laboratory of Chemical Product Engineering, College of Chemistry and Chemical Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Jiahua HuShanxi Key Laboratory of Chemical Product Engineering, College of Chemistry and Chemical Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Feiyu GaoShanxi Key Laboratory of Chemical Product Engineering, College of Chemistry and Chemical Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Xuexue ZongCollege of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China.

Funding

Hyperparameters of the SVR model after particle swarm optimization Nos. 22178241

6 · The paper itself

Abstract

In simulations of elastic flow using the lattice Boltzmann method (LBM), the steady-state behavior of the flow at low capillary numbers is typically poor and prone to the formation of bubbles with inhomogeneous lengths. This phenomenon undermines the precise control of heat transfer, mass transfer, and reactions within microchannels and microreactors. This paper establishes an LBM multiphase flow model enhanced by machine learning. The hyperparameters of the machine learning model are optimized using the particle swarm algorithm. In contrast, the non-dominated sorting genetic algorithm (NSGA-II) is incorporated to optimize bubble lengths and stability. This results in a coupled multiphase flow numerical simulation model that integrates LBM, machine learning, and the particle swarm algorithm. Using this model, we investigate the influence of elastic flow parameters on bubble length and stability in a T-shaped microchannel. The simulation results demonstrate that the proposed LBM multiphase flow model can effectively predict bubble elongation rates under complex conditions. Furthermore, multi-objective optimization determines the optimal gas-liquid two-phase inlet flow rate relationship, significantly mitigating elastic flow instability at low capillary numbers. This approach enhances the controllability of the elastic flow process and improves the efficiency of mass and heat transfer.

Indexed as

lattice Boltzmann methodmachine learningmicrochannel elastic flowmodel optimizationNSGA-II

Identifiers

PMID40141909

DOI10.3390/mi16030298

PMCPMC11945988

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