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Study on dynamic tension estimation for the underwater soft yoke mooring system with LSTM-AM neural network

It is significant to estimate the mooring tension for the safety, operation and maintenance of single point mooring system of floating production, storage and offloading unit (FPSO). In this study, a neural network model named long-short term memory combined with attention mechanism (LSTM-AM) is ado...

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Main Authors: Ma, Gang, Jin, Conglin, Wang, Hong Wei, Li, Peng, Kang, Hooi Siang
Format: Article
Published: Elsevier Ltd 2023
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/106055/
http://dx.doi.org/10.1016/j.oceaneng.2022.113287
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spelling my.utm.1060552024-05-31T03:04:28Z http://eprints.utm.my/106055/ Study on dynamic tension estimation for the underwater soft yoke mooring system with LSTM-AM neural network Ma, Gang Jin, Conglin Wang, Hong Wei Li, Peng Kang, Hooi Siang TJ Mechanical engineering and machinery It is significant to estimate the mooring tension for the safety, operation and maintenance of single point mooring system of floating production, storage and offloading unit (FPSO). In this study, a neural network model named long-short term memory combined with attention mechanism (LSTM-AM) is adopted to estimate the mooring legs dynamic tension of an underwater soft yoke mooring system in time domain. The mooring legs tension and FPSO motions are set as the training features in the LSTM-AM neural network, together with the corresponding first-order and second-order central moments. The training data collection is implemented with the numerical hydrodynamic coupled analysis of FPSO and an underwater soft yoke mooring system. Different variables are studied to determine the optimal structure of the LSTM-AM neural network model, including the time window, the layer numbers, the neural units and the optimizer. Through cases studied, it is proved that the LSTM-AM neural network model is suitable to estimate the mooring legs tension of FPSO and the underwater soft yoke mooring system in different sea states. Elsevier Ltd 2023-01-01 Article PeerReviewed Ma, Gang and Jin, Conglin and Wang, Hong Wei and Li, Peng and Kang, Hooi Siang (2023) Study on dynamic tension estimation for the underwater soft yoke mooring system with LSTM-AM neural network. Ocean Engineering, 267 (NA). NA. ISSN 0029-8018 http://dx.doi.org/10.1016/j.oceaneng.2022.113287 DOI:10.1016/j.oceaneng.2022.113287
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Ma, Gang
Jin, Conglin
Wang, Hong Wei
Li, Peng
Kang, Hooi Siang
Study on dynamic tension estimation for the underwater soft yoke mooring system with LSTM-AM neural network
description It is significant to estimate the mooring tension for the safety, operation and maintenance of single point mooring system of floating production, storage and offloading unit (FPSO). In this study, a neural network model named long-short term memory combined with attention mechanism (LSTM-AM) is adopted to estimate the mooring legs dynamic tension of an underwater soft yoke mooring system in time domain. The mooring legs tension and FPSO motions are set as the training features in the LSTM-AM neural network, together with the corresponding first-order and second-order central moments. The training data collection is implemented with the numerical hydrodynamic coupled analysis of FPSO and an underwater soft yoke mooring system. Different variables are studied to determine the optimal structure of the LSTM-AM neural network model, including the time window, the layer numbers, the neural units and the optimizer. Through cases studied, it is proved that the LSTM-AM neural network model is suitable to estimate the mooring legs tension of FPSO and the underwater soft yoke mooring system in different sea states.
format Article
author Ma, Gang
Jin, Conglin
Wang, Hong Wei
Li, Peng
Kang, Hooi Siang
author_facet Ma, Gang
Jin, Conglin
Wang, Hong Wei
Li, Peng
Kang, Hooi Siang
author_sort Ma, Gang
title Study on dynamic tension estimation for the underwater soft yoke mooring system with LSTM-AM neural network
title_short Study on dynamic tension estimation for the underwater soft yoke mooring system with LSTM-AM neural network
title_full Study on dynamic tension estimation for the underwater soft yoke mooring system with LSTM-AM neural network
title_fullStr Study on dynamic tension estimation for the underwater soft yoke mooring system with LSTM-AM neural network
title_full_unstemmed Study on dynamic tension estimation for the underwater soft yoke mooring system with LSTM-AM neural network
title_sort study on dynamic tension estimation for the underwater soft yoke mooring system with lstm-am neural network
publisher Elsevier Ltd
publishDate 2023
url http://eprints.utm.my/106055/
http://dx.doi.org/10.1016/j.oceaneng.2022.113287
_version_ 1800714798633058304
score 13.211869

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