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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年1月30日

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  • Nanxi Ding,
  • Hengzhen Feng,
  • H. Z. Lou,
  • Shenghua Fu,
  • Chenglong Li,
  • Zihao Zhang,
  • Wenlong Ma,
  • Zhengqian Zhang

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.