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Experimental and numerical gust identification using deep learning models

Kayal Lakshmanan, Davide Balatti, Hamed Haddad Khodaparast Orcid Logo, Michael Friswell, Andrea Castrichini

Applied Mathematical Modelling, Volume: 132, Pages: 41 - 56

Swansea University Authors: Kayal Lakshmanan, Davide Balatti, Hamed Haddad Khodaparast Orcid Logo, Michael Friswell

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DOI (Published version): 10.1016/j.apm.2024.04.034

Abstract

Identifying gusts and turbulence events is of primary importance for designing future gust load alleviation systems, calculating airframe load, and analysing incidents. Due to the impossibility of their direct measurement, indirect methods are used and ad hoc experiments are necessary to validate th...

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Published in: Applied Mathematical Modelling
ISSN: 0307-904X
Published: Elsevier BV 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa66091
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spelling 2024-05-21T13:31:18.9211889 v2 66091 2024-04-19 Experimental and numerical gust identification using deep learning models 31fdeba4e76994bc72c5b8954389f8ab Kayal Lakshmanan Kayal Lakshmanan true false 4c58ba20bbabfef44b00b143e96b37e1 Davide Balatti Davide Balatti true false f207b17edda9c4c3ea074cbb7555efc1 0000-0002-3721-4980 Hamed Haddad Khodaparast Hamed Haddad Khodaparast true false 5894777b8f9c6e64bde3568d68078d40 Michael Friswell Michael Friswell true false 2024-04-19 ACEM Identifying gusts and turbulence events is of primary importance for designing future gust load alleviation systems, calculating airframe load, and analysing incidents. Due to the impossibility of their direct measurement, indirect methods are used and ad hoc experiments are necessary to validate the methodology. This paper employs Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) as well as CNN models for in-flight gust identification. Two aeroelastic models, with different levels of fidelity, representative of a civil and commercial aircraft, are used to generate gust responses to train and test the Deep Learning (DL) models. The results highlight the capability of both LSTM-CNN and CNN models in reconstructing gusts across the entire flight envelope of a civil commercial aircraft. The CNN model demonstrated its ability to identify gusts and turbulence when they occur concurrently, similar to real-world scenarios, in a significantly shorter amount of time. Furthermore, its application to wind tunnel gust response measurements, where the inflow has previously been characterised, demonstrated the effectiveness of the proposed methodology for experimental measurements. Journal Article Applied Mathematical Modelling 132 41 56 Elsevier BV 0307-904X Gust Identification; Inverse Method; Aeroelasticity; Deep Learning 1 8 2024 2024-08-01 10.1016/j.apm.2024.04.034 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University SU Library paid the OA fee (TA Institutional Deal) The research leading to these results has received funding from the Engineering Physical Science Research Council (EPSRC) through a program grant EP/R006768/1. The authors also acknowledge the EPSRC Impact acceleration fund. 2024-05-21T13:31:18.9211889 2024-04-19T11:36:01.7888054 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Kayal Lakshmanan 1 Davide Balatti 2 Hamed Haddad Khodaparast 0000-0002-3721-4980 3 Michael Friswell 4 Andrea Castrichini 5 66091__30175__f4c274b9d39b463aa5878078ca3186a3.pdf 66091.VoR.pdf 2024-04-28T20:56:53.6340042 Output 1655360 application/pdf Version of Record true © 2024 The Author(s). This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/).
title Experimental and numerical gust identification using deep learning models
spellingShingle Experimental and numerical gust identification using deep learning models
Kayal Lakshmanan
Davide Balatti
Hamed Haddad Khodaparast
Michael Friswell
title_short Experimental and numerical gust identification using deep learning models
title_full Experimental and numerical gust identification using deep learning models
title_fullStr Experimental and numerical gust identification using deep learning models
title_full_unstemmed Experimental and numerical gust identification using deep learning models
title_sort Experimental and numerical gust identification using deep learning models
author_id_str_mv 31fdeba4e76994bc72c5b8954389f8ab
4c58ba20bbabfef44b00b143e96b37e1
f207b17edda9c4c3ea074cbb7555efc1
5894777b8f9c6e64bde3568d68078d40
author_id_fullname_str_mv 31fdeba4e76994bc72c5b8954389f8ab_***_Kayal Lakshmanan
4c58ba20bbabfef44b00b143e96b37e1_***_Davide Balatti
f207b17edda9c4c3ea074cbb7555efc1_***_Hamed Haddad Khodaparast
5894777b8f9c6e64bde3568d68078d40_***_Michael Friswell
author Kayal Lakshmanan
Davide Balatti
Hamed Haddad Khodaparast
Michael Friswell
author2 Kayal Lakshmanan
Davide Balatti
Hamed Haddad Khodaparast
Michael Friswell
Andrea Castrichini
format Journal article
container_title Applied Mathematical Modelling
container_volume 132
container_start_page 41
publishDate 2024
institution Swansea University
issn 0307-904X
doi_str_mv 10.1016/j.apm.2024.04.034
publisher Elsevier BV
college_str Faculty of Science and Engineering
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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 - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 1
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description Identifying gusts and turbulence events is of primary importance for designing future gust load alleviation systems, calculating airframe load, and analysing incidents. Due to the impossibility of their direct measurement, indirect methods are used and ad hoc experiments are necessary to validate the methodology. This paper employs Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) as well as CNN models for in-flight gust identification. Two aeroelastic models, with different levels of fidelity, representative of a civil and commercial aircraft, are used to generate gust responses to train and test the Deep Learning (DL) models. The results highlight the capability of both LSTM-CNN and CNN models in reconstructing gusts across the entire flight envelope of a civil commercial aircraft. The CNN model demonstrated its ability to identify gusts and turbulence when they occur concurrently, similar to real-world scenarios, in a significantly shorter amount of time. Furthermore, its application to wind tunnel gust response measurements, where the inflow has previously been characterised, demonstrated the effectiveness of the proposed methodology for experimental measurements.
published_date 2024-08-01T08:29:36Z
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score 11.364387

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