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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2023
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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 |
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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 |
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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. |
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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 |
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Elsevier Ltd |
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2023 |
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http://eprints.utm.my/106055/ http://dx.doi.org/10.1016/j.oceaneng.2022.113287 |
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13.18916 |