2024/12/01 by Liwei Chen, Nils Thuerey · 1 voice
Engineering · Physics and Astronomy · #Fluid Dynamics and Turbulent Flows #Model Reduction and Neural Networks #Aerodynamics and Acoustics in Jet Flows
paper · doi:10.1063/5.0238413
openalex publication_date 2024/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
Effectively predicting transonic unsteady flow over an airfoil presents significant challenges due to its complex dynamics. In this study, we utilize a deep neural network architecture designed to capture intricate flow behavior. Through comprehensive training, our models successfully represent the complexities of transonic and unsteady flow, even under previously unseen conditions. By leveraging the differentiable nature of neural network representations, we develop a framework for evaluating fundamental physical properties using linear stability analysis. This approach bridges neural network modeling with traditional modal analysis, providing critical insights into transonic flow dynamics while improving the interpretability of neural network-based flow diagnostics.