Steady-State Transonic Flowfield Prediction via Deep-Learning Framework

Abstract

This paper focuses on the development of a deep-learning framework for predicting distributed quantities around aircraft flying in the transonic regime. These quantities play a crucial role in determining aerodynamic loads and conducting aeroelastic analysis. Angle of attack and Mach number are chosen as the two independent parameters for the reduced-order models. A comparative assessment is conducted between the proposed deep-learning framework and the proper orthogonal decomposition approach to identify the strengths and weaknesses of each method. The accuracy of the data-driven machine-learning method in modeling steady-state transonic aerodynamics is assessed against three benchmark cases of three-dimensional test cases: Benchmark Super Critical Wing and ONERA M6 wings, and the wing–body Common Research Model configuration. Despite the challenges of the analyzed scenarios, promising results are obtained for each test case, showing the effectiveness of the model implemented. Furthermore, the paper demonstrates the application of the method for aeroelastic analysis and uncertainty quantification. This quantifies the robustness and versatility of the implemented model.