Inverse Aerodynamic Design Using Neural Networks

Balla, Kensley; Sevilla, Rubén; Hassan, Oubay; Morgan, Kenneth

doi:10.1007/978-3-031-12019-0_10

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Inverse Aerodynamic Design Using Neural Networks

Kensley Balla, Rubén Sevilla

, Oubay Hassan

, Kenneth Morgan

Advances in Computational Methods and Technologies in Aeronautics and Industry, Volume: 57, Pages: 131 - 143

Swansea University Authors: Rubén Sevilla , Oubay Hassan , Kenneth Morgan

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DOI (Published version): 10.1007/978-3-031-12019-0_10

Abstract

An efficient computational framework is presented and applied to the inverse aerodynamic shape design problem. The main building block is a novel neural network capable to accurately predict the pressure distribution on aerofoils and wings. The trained neural network is used to accelerate the evalua...

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Published in:	Advances in Computational Methods and Technologies in Aeronautics and Industry
ISBN:	9783031120183 9783031120190
ISSN:	1871-3033 2543-0203
Published:	Cham Springer International Publishing 2022
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa62238
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first_indexed	2023-01-03T11:15:57Z
last_indexed	2023-02-04T04:13:25Z
id	cronfa62238
recordtype	SURis
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spelling	2023-02-03T12:25:50.4235527 v2 62238 2023-01-03 Inverse Aerodynamic Design Using Neural Networks 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 2023-01-03 CIVL An efficient computational framework is presented and applied to the inverse aerodynamic shape design problem. The main building block is a novel neural network capable to accurately predict the pressure distribution on aerofoils and wings. The trained neural network is used to accelerate the evaluation of the objective function in an optimisation algorithm based on the gradient-free modified cuckoo search method. Two applications are presented in two and three dimensions for problems involving up to 50 geometric parameters. Book chapter Advances in Computational Methods and Technologies in Aeronautics and Industry 57 131 143 Springer International Publishing Cham 9783031120183 9783031120190 1871-3033 2543-0203 Aerodynamic design; Neural network; Optimisation 13 12 2022 2022-12-13 10.1007/978-3-031-12019-0_10 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University 2023-02-03T12:25:50.4235527 2023-01-03T11:12:51.6880858 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
title	Inverse Aerodynamic Design Using Neural Networks
spellingShingle	Inverse Aerodynamic Design Using Neural Networks Rubén Sevilla Oubay Hassan Kenneth Morgan
title_short	Inverse Aerodynamic Design Using Neural Networks
title_full	Inverse Aerodynamic Design Using Neural Networks
title_fullStr	Inverse Aerodynamic Design Using Neural Networks
title_full_unstemmed	Inverse Aerodynamic Design Using Neural Networks
title_sort	Inverse Aerodynamic Design Using Neural Networks
author_id_str_mv	b542c87f1b891262844e95a682f045b6 07479d73eba3773d8904cbfbacc57c5b 17f3de8936c7f981aea3a832579c5e91
author_id_fullname_str_mv	b542c87f1b891262844e95a682f045b6_*_Rubén Sevilla 07479d73eba3773d8904cbfbacc57c5b__Oubay Hassan 17f3de8936c7f981aea3a832579c5e91_**_Kenneth Morgan
author	Rubén Sevilla Oubay Hassan Kenneth Morgan
author2	Kensley Balla Rubén Sevilla Oubay Hassan Kenneth Morgan
format	Book chapter
container_title	Advances in Computational Methods and Technologies in Aeronautics and Industry
container_volume	57
container_start_page	131
publishDate	2022
institution	Swansea University
isbn	9783031120183 9783031120190
issn	1871-3033 2543-0203
doi_str_mv	10.1007/978-3-031-12019-0_10
publisher	Springer International Publishing
college_str	Faculty of Science and Engineering
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hierarchy_top_id	facultyofscienceandengineering
hierarchy_top_title	Faculty of Science and Engineering
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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
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description	An efficient computational framework is presented and applied to the inverse aerodynamic shape design problem. The main building block is a novel neural network capable to accurately predict the pressure distribution on aerofoils and wings. The trained neural network is used to accelerate the evaluation of the objective function in an optimisation algorithm based on the gradient-free modified cuckoo search method. Two applications are presented in two and three dimensions for problems involving up to 50 geometric parameters.
published_date	2022-12-13T04:21:42Z
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score	11.037603

Inverse Aerodynamic Design Using Neural Networks

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