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A generic deep learning framework for propagation and scattering of acoustic waves in quiescent flows

Alguacil, Antonio and Bauerheim, Michaël and Jacob, Marc C. and Moreau, Stéphane A generic deep learning framework for propagation and scattering of acoustic waves in quiescent flows. (2021) In: AIAA AVIATION 2021 FORUM, 2 August 2021 - 6 August 2021 (Virtual event, United States).

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Official URL: https://doi.org/10.2514/6.2021-2239

Abstract

A deep learning surrogate for the direct numerical prediction of two-dimensional acoustic waves propagation and scattering with obstacles is developed through an auto-regressive spatio- temporal convolutional neural network. A single database of high-fidelity lattice Boltzmann temporal simulations is employed in the training of the network, achieving accurate predictions for long simulation times for a variety of test cases, representative of bounded and unbounded configurations. The capacity of the network to extrapolate outside the manifold of examples seen during the training phase is demonstrated by the obtaining of accurate acoustic predic- tions for relevant applications, such as the scattering of acoustic waves on an airfoil trailing edge, an engine nacelle or in-duct propagation. The method is tested for two types of input normalizations, coupled with an a-posteriori correction which improves the acoustic energy conservation of the predictions. The use of an adaptive local normalization along with the physics-based energy conservation results in an error reduction for all the studied cases.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-03369917
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National des Sciences Appliquées de Toulouse - INSA (FRANCE)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Other partners > Université de Lyon - UDL (FRANCE)
Other partners > Université de Sherbrooke (CANADA)
Laboratory name:
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Deposited On:07 Oct 2021 14:21

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