Steady-State Transonic Flowfield Prediction via Deep-Learning Framework
Steady-State Transonic Flowfield Prediction via Deep-Learning Framework
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.
Aeroelastic Analysis, Convolutional Neural Network, Hyperparameter Optimization, Proper Orthogonal Decomposition, Reduced Order Model, Transonic Aerodynamics, Uncertainty Quantification
1915-1931
Immordino, Gabriele
ed9626cc-aa2b-40be-b376-0868967e5e65
Da Ronch, Andrea
a2f36b97-b881-44e9-8a78-dd76fdf82f1a
Righi, Marcello
83c3435f-2abf-45f3-9aef-1f1971043a82
May 2024
Immordino, Gabriele
ed9626cc-aa2b-40be-b376-0868967e5e65
Da Ronch, Andrea
a2f36b97-b881-44e9-8a78-dd76fdf82f1a
Righi, Marcello
83c3435f-2abf-45f3-9aef-1f1971043a82
Immordino, Gabriele, Da Ronch, Andrea and Righi, Marcello
(2024)
Steady-State Transonic Flowfield Prediction via Deep-Learning Framework.
AIAA Journal, 62 (5), .
(doi:10.2514/1.J063545).
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.
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AIAA_Journal_Steady_Flowfield_Prediction_in_Transonic_Regime_Via_Deep_Learning_Framework
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immordino-et-al-2024-steady-state-transonic-flowfield-prediction-via-deep-learning-framework
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Submitted date: 18 October 2023
Accepted/In Press date: 28 December 2023
Published date: May 2024
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Publisher Copyright:
© 2024, American Institute of Aeronautics and Astronautics, Inc.. All rights reserved.
Keywords:
Aeroelastic Analysis, Convolutional Neural Network, Hyperparameter Optimization, Proper Orthogonal Decomposition, Reduced Order Model, Transonic Aerodynamics, Uncertainty Quantification
Identifiers
Local EPrints ID: 484752
URI: http://eprints.soton.ac.uk/id/eprint/484752
ISSN: 0001-1452
PURE UUID: e448635f-55af-456a-a15c-340f132d90f8
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Date deposited: 21 Nov 2023 17:33
Last modified: 31 Jul 2024 04:02
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Author:
Gabriele Immordino
Author:
Marcello Righi
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