Deep learning for turbulent boundary layers: spatio-temporal reconstruction and prediction
X. Dong(1,2,3), W. Zhou(1,2,3), T. Cai(1,2,3), X. Cai(1,2,3)
1: School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2: Shanghai Key Laboratory of Multiphase Flow and Heat Transfer for Power Engineering, Shanghai 200093, China
3: Key Laboratory of Multiphase Flow Measurement Technology of China Machinery Industry Federation, Shanghai, 200093, China
DOI:
Spatio-temporal prediction of turbulent flow fields remains a challenging task due to complex multi-scale dynamics and high-dimensional data. Recent advances in deep learning offer promising data-driven approaches, yet their comparative performance under physical constraints warrants further investigation. In this study, a physics-informed neural network (PINN) framework and a long short term memory (LSTM) network are implemented and systematically compared for the spatio-temporal prediction of turbulent boundary layer flows over a wide range of Reynolds numbers. Both models are trained and tested using simulated data from a shock-induced turbulent boundary layer and experimental data from particle image velocimetry (PIV) measurements of turbulent channel flow. Prediction accuracy is evaluated across multiple time horizons, with particular attention to short-term and extended temporal extrapolation. Results indicate that the PINN framework achieves higher physical fidelity and robustness in reconstructing velocity and pressure fields from sparse or incomplete data, especially in scenarios requiring alignment with fundamental flow physics. The comparative analysis highlights the complementary strengths and limitations of physics-embedded and purely data-driven deep learning approaches for turbulent flow prediction, providing guidance for method selection based on prediction horizon, data availability, and physical accuracy requirements.
