A Review of Simulations and Machine Learning Approaches for Flow Separation Analysis

Flow separation is a fundamental phenomenon in fluid mechanics governed by the Navier–Stokes equations, which are second-order partial differential equations (PDEs). This phenomenon significantly impacts aerodynamic performance in various applications across the aerospace sector, including micro air vehicles (MAVs), advanced air mobility, and the wind energy industry. Its complexity arises from its nonlinear, multidimensional nature, and is further influenced by operational and geometrical parameters beyond Reynolds number (Re), making accurate prediction a persistent challenge. Traditional models often struggle to capture the intricacies of separated flows, requiring advanced simulation and prediction techniques. This review provides a comprehensive overview of strategies for enhancing aerodynamic design by improving the understanding and prediction of flow separation. It highlights recent advancements in simulation and machine learning (ML) methods, which utilize flow field databases and data assimilation techniques. Future directions, including physics-informed neural networks (PINNs) and hybrid frameworks, are also discussed to improve flow separation prediction and control further.