Spatiotemporal wall pressure forecast of a rectangular cylinder with physics-aware DeepUFNet

• URL: http://arxiv.org/abs/2508.03183v1
• Date: Tue, 05 Aug 2025 07:48:09 GMT
• Title: Spatiotemporal wall pressure forecast of a rectangular cylinder with physics-aware DeepUFNet
• Authors: Junle Liu, Chang Liu, Yanyu Ke, Wenliang Chen, Kihing Shum, K. T. Tse, Gang Hu,
• Abstract summary: This study develops a physicsaware DeepUier neural Network (DeepUFNet) deep learning model.<n>DeepUFNet comprises the UNet structure, with physical high-frequency loss control coefficient embedded in the model training stage.<n>The model is found to forecast wall pressure information with high accuracy.
• Score: 7.806444353431173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The wall pressure is of great importance in understanding the forces and structural responses induced by fluid. Recent works have investigated the potential of deep learning techniques in predicting mean pressure coefficients and fluctuating pressure coefficients, but most of existing deep learning frameworks are limited to predicting a single snapshot using full spatial information. To forecast spatiotemporal wall pressure of flow past a rectangular cylinder, this study develops a physics-aware DeepU-Fourier neural Network (DeepUFNet) deep learning model. DeepUFNet comprises the UNet structure and the Fourier neural network, with physical high-frequency loss control embedded in the model training stage to optimize model performance, where the parameter $\beta$ varies with the development of the training epoch. Wind tunnel testing is performed to collect wall pressures of a two-dimensional rectangular cylinder with a side ratio of 1.5 at an angle of attack of zero using high-frequency pressure scanning, thereby constructing a database for DeepUFNet training and testing. The DeepUFNet model is found to forecast spatiotemporal wall pressure information with high accuracy. The comparison between forecast results and experimental data presents agreement in statistical information, temporal pressure variation, power spectrum density, spatial distribution, and spatiotemporal correlation. It is also found that embedding a physical high-frequency loss control coefficient $\beta$ in the DeepUFNet model can significantly improve model performance in forecasting spatiotemporal wall pressure information, in particular, in forecasting high-order frequency fluctuation and wall pressure variance. Furthermore, the DeepUFNet extrapolation capability is tested with sparse spatial information input, and the model presents a satisfactory extrapolation ability

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