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An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings

Kensley Balla, Rubén Sevilla Orcid Logo, Oubay Hassan Orcid Logo, Kenneth Morgan Orcid Logo

Applied Mathematical Modelling, Volume: 96, Pages: 456 - 479

Swansea University Authors: Kensley Balla, Rubén Sevilla Orcid Logo, Oubay Hassan Orcid Logo, Kenneth Morgan Orcid Logo

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DOI (Published version): 10.1016/j.apm.2021.03.019

Abstract

This work proposes a novel multi-output neural network for the prediction of aerodynamic coefficients of aerofoils in two dimensions and wings in three dimensions. Contrary to existing neural networks that are often designed to predict aerodynamic quantities of interest, the proposed network conside...

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Published in: Applied Mathematical Modelling
ISSN: 0307-904X
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa56398
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spelling 2022-10-31T18:45:16.7714250 v2 56398 2021-03-11 An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings f2113c910a9cea449dc18d3302638e4b Kensley Balla Kensley Balla true false b542c87f1b891262844e95a682f045b6 0000-0002-0061-6214 Rubén Sevilla Rubén Sevilla true false 07479d73eba3773d8904cbfbacc57c5b 0000-0001-7472-3218 Oubay Hassan Oubay Hassan true false 17f3de8936c7f981aea3a832579c5e91 0000-0003-0760-1688 Kenneth Morgan Kenneth Morgan true false 2021-03-11 This work proposes a novel multi-output neural network for the prediction of aerodynamic coefficients of aerofoils in two dimensions and wings in three dimensions. Contrary to existing neural networks that are often designed to predict aerodynamic quantities of interest, the proposed network considers as output the pressure at a number of selected points on the aerodynamic shape. The proposed multi-output neural network is compared with other approaches found in the literature. Furthermore, a detailed comparison of the proposed neural network with the popular proper orthogonal decomposition method is presented. The numerical results, involving high dimensional problems with flow and geometric parameters, show the benefits of the proposed approach. Journal Article Applied Mathematical Modelling 96 456 479 Elsevier BV 0307-904X neural network, proper orthogonal decomposition, CFD, NURBS, aerofoil, wing 1 8 2021 2021-08-01 10.1016/j.apm.2021.03.019 COLLEGE NANME COLLEGE CODE Swansea University 2022-10-31T18:45:16.7714250 2021-03-11T09:13:14.8409901 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Kensley Balla 1 Rubén Sevilla 0000-0002-0061-6214 2 Oubay Hassan 0000-0001-7472-3218 3 Kenneth Morgan 0000-0003-0760-1688 4 56398__19473__9fa40e8368d64256a88e7cff60891086.pdf 56398.pdf 2021-03-11T09:14:38.9680395 Output 3307665 application/pdf Accepted Manuscript true 2022-03-23T00:00:00.0000000 ©2021 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings
spellingShingle An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings
Kensley Balla
Rubén Sevilla
Oubay Hassan
Kenneth Morgan
title_short An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings
title_full An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings
title_fullStr An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings
title_full_unstemmed An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings
title_sort An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings
author_id_str_mv f2113c910a9cea449dc18d3302638e4b
b542c87f1b891262844e95a682f045b6
07479d73eba3773d8904cbfbacc57c5b
17f3de8936c7f981aea3a832579c5e91
author_id_fullname_str_mv f2113c910a9cea449dc18d3302638e4b_***_Kensley Balla
b542c87f1b891262844e95a682f045b6_***_Rubén Sevilla
07479d73eba3773d8904cbfbacc57c5b_***_Oubay Hassan
17f3de8936c7f981aea3a832579c5e91_***_Kenneth Morgan
author Kensley Balla
Rubén Sevilla
Oubay Hassan
Kenneth Morgan
author2 Kensley Balla
Rubén Sevilla
Oubay Hassan
Kenneth Morgan
format Journal article
container_title Applied Mathematical Modelling
container_volume 96
container_start_page 456
publishDate 2021
institution Swansea University
issn 0307-904X
doi_str_mv 10.1016/j.apm.2021.03.019
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description This work proposes a novel multi-output neural network for the prediction of aerodynamic coefficients of aerofoils in two dimensions and wings in three dimensions. Contrary to existing neural networks that are often designed to predict aerodynamic quantities of interest, the proposed network considers as output the pressure at a number of selected points on the aerodynamic shape. The proposed multi-output neural network is compared with other approaches found in the literature. Furthermore, a detailed comparison of the proposed neural network with the popular proper orthogonal decomposition method is presented. The numerical results, involving high dimensional problems with flow and geometric parameters, show the benefits of the proposed approach.
published_date 2021-08-01T04:11:19Z
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score 11.014067

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