Prediction of velocity and pressure of gas-liquid flow using spectrum-based physics-informed neural networks  

作　　者：Nanxi DING Hengzhen FENG H.Z.LOU Shenghua FU Chenglong LI Zihao ZHANG Wenlong MA Zhengqian ZHANG 

机构地区：[1]School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China [2]University of Southern California,California 90007,U.S.A [3]Xi’an Institute of Electromechanical Information Technology,Xi’an 710075,China

出　　处：《Applied Mathematics and Mechanics(English Edition)》2025年第2期341-356,共16页应用数学和力学(英文版)

基　　金：Supported by the National Natural Science Foundation of China(No.62304022)。

摘　　要：This research introduces a spectrum-based physics-informed neural network(SP-PINN)model to significantly improve the accuracy of calculation of two-phase flow parameters,surpassing existing methods that have limitations in global and continuous data sampling.SP-PINNs address the challenges of traditional methods in terms of continuous sampling by integrating the spectral analysis and pressure correction into the Navier-Stokes(N-S)equations,enhancing the predictive accuracy especially in critical regions like gas-phase boundaries and velocity peaks.The novel introduction of a pressure-correction module within SP-PINNs mitigates prediction errors,achieving a substantial reduction to 1‰compared with the conventional physics-informed neural network(PINN)approaches.Experimental applications validate the model’s ability to accurately and rapidly predict flow parameters with different sampling time intervals,with the computation time of predicting unsampled data less than 0.01 s.Such advancements signify a 100-fold improvement over traditional DNS calculations,underscoring the model’s potential in the real-time calculation and analysis of multiphase flow dynamics.

关 键 词：physics-informed neural network(PINN) spectral method two-phase flow parameter prediction 

分 类 号：O359[理学—流体力学]