Abstract:
In the field of aerodynamics, there is a growing need for rapid load prediction in engineering applications. Surrogate modeling offers a promising solution, providing faster results compared to high-fidelity computational models. This study focuses on a Machine Learning (ML) framework tailored for surrogate modeling, specifically for integrated aerodynamic load predictions in aircraft design. Central to this framework is a Deep Neural Network (DNN) component capable of handling both steady-state and fluctuating aerodynamics. A key challenge for surrogate models lies in maintaining prediction accuracy, especially in scenarios involving nonlinear flow phenomena like flow separation and transonic shifts. To address these challenges, we introduce a two-step physics-state predictor that integrates an intermediate Convolutional Neural Network (CNN) component. This approach enhances the surrogate model's capability to accurately represent dynamic separated flows and other nonlinear patterns without relying on unrealistic user inputs. Results are presented for NACA0015 dynamic stall predictions for two different physics-state inputs.