Deep learning-based predictive modelling of transonic flow over an aerofoil

2024/03/25 by Liwei Chen, Nils Thuerey, Chen, Li-Wei +1
Engineering · #Aerodynamics and Acoustics in Jet Flows #Computational Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Plasma and Flow Control in Aerodynamics #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2403.17131

openalex publication_date 2024/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Effectively predicting transonic unsteady flow over an aerofoil poses inherent challenges. In this study, we harness the power of deep neural network (DNN) models using the attention U-Net architecture. Through efficient training of these models, we achieve the capability to capture the complexities of transonic and unsteady flow dynamics at high resolution, even when faced with previously unseen conditions. We demonstrate that by leveraging the differentiability inherent in neural network representations, our approach provides a framework for assessing fundamental physical properties via global instability analysis. This integration bridges deep neural network models and traditional modal analysis, offering valuable insights into transonic flow dynamics and enhancing the interpretability of neural network models in flowfield diagnostics.

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