Steady-state flowfield prediction in transonic regime via deep-learning framework

Immordino, Gabriele, Da Ronch, Andrea and Righi, Marcello (2023) Steady-state flowfield prediction in transonic regime via deep-learning framework. AIAA Journal. (In Press)

Record type: Article

Abstract

This article 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 strengths and weaknesses of each method. The accuracy of the data–driven machine–learning method in modelling transonic, steady–state aerodynamics is assessed against three benchmark cases of three-dimensional wings. Despite the challenges of the analyzed scenarios, promising results are obtained for each test case, showing the effectiveness of the model implemented. Furthermore, the article demonstrates the application of the method for aeroelastic analysis and uncertainty quantification. This quantifies robustness and versatility of the implemented model.

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AIAA_Journal_Steady_Flowfield_Prediction_in_Transonic_Regime_Via_Deep_Learning_Framework - Accepted Manuscript

Available under License University of Southampton Accepted Manuscript Licence.

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