Deep learning-based predictive modeling of transonic flow over an airfoil

Abstract

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.