Experimental studies of fpso responses with validation by numerical and artificial neural network prediction

There will be a force on the floating systems after applied environmental load which has an important effect on the performance and safety of the structure. Therefore, the research on orientation of the structure and wave impact has a practical significance. Experimental and numerical simulation bec...

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Main Authors: Azizan, N.L., Irawan, R., Liew, M.S., Al-Yacouby, A.M., Danyaro, K.U.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.24698 /
Published: Springer Science and Business Media Deutschland GmbH 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091294092&doi=10.1007%2f978-981-15-5753-8_43&partnerID=40&md5=c03e3d387fa8f7ab618caca2c425139d
http://eprints.utp.edu.my/24698/
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spelling utp-eprints.246982021-08-27T05:50:38Z Experimental studies of fpso responses with validation by numerical and artificial neural network prediction Azizan, N.L. Irawan, R. Liew, M.S. Al-Yacouby, A.M. Danyaro, K.U. There will be a force on the floating systems after applied environmental load which has an important effect on the performance and safety of the structure. Therefore, the research on orientation of the structure and wave impact has a practical significance. Experimental and numerical simulation becomes valuable in predicting the performance during the system operation. Hence, in this article, a study on the effect of FPSO response by changing the orientation of FPSO has been presented by conducting experiments in the UTP wave basin subjected to regular wave condition. The results are used to be validated with numerical models using a commercial software AQWA by the 3D frequency domain theory and presented in terms of Response Amplitude Operators (RAO) of six degrees of freedom. To accurately consider the effect of response, artificial neural network (ANN) is adopted to predict the FPSO behaviour under different orientations and validate the results. ANN can provide meaningful solutions and can process information in extremely rapid mode ensuring high accuracy of prediction, especially for long response in time histories. Results show that three methods were achieved to generalize the responses. © Springer Nature Singapore Pte Ltd 2020. Springer Science and Business Media Deutschland GmbH 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091294092&doi=10.1007%2f978-981-15-5753-8_43&partnerID=40&md5=c03e3d387fa8f7ab618caca2c425139d Azizan, N.L. and Irawan, R. and Liew, M.S. and Al-Yacouby, A.M. and Danyaro, K.U. (2020) Experimental studies of fpso responses with validation by numerical and artificial neural network prediction. Lecture Notes in Mechanical Engineering . pp. 469-482. http://eprints.utp.edu.my/24698/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description There will be a force on the floating systems after applied environmental load which has an important effect on the performance and safety of the structure. Therefore, the research on orientation of the structure and wave impact has a practical significance. Experimental and numerical simulation becomes valuable in predicting the performance during the system operation. Hence, in this article, a study on the effect of FPSO response by changing the orientation of FPSO has been presented by conducting experiments in the UTP wave basin subjected to regular wave condition. The results are used to be validated with numerical models using a commercial software AQWA by the 3D frequency domain theory and presented in terms of Response Amplitude Operators (RAO) of six degrees of freedom. To accurately consider the effect of response, artificial neural network (ANN) is adopted to predict the FPSO behaviour under different orientations and validate the results. ANN can provide meaningful solutions and can process information in extremely rapid mode ensuring high accuracy of prediction, especially for long response in time histories. Results show that three methods were achieved to generalize the responses. © Springer Nature Singapore Pte Ltd 2020.
format Article
author Azizan, N.L.
Irawan, R.
Liew, M.S.
Al-Yacouby, A.M.
Danyaro, K.U.
spellingShingle Azizan, N.L.
Irawan, R.
Liew, M.S.
Al-Yacouby, A.M.
Danyaro, K.U.
Experimental studies of fpso responses with validation by numerical and artificial neural network prediction
author_sort Azizan, N.L.
title Experimental studies of fpso responses with validation by numerical and artificial neural network prediction
title_short Experimental studies of fpso responses with validation by numerical and artificial neural network prediction
title_full Experimental studies of fpso responses with validation by numerical and artificial neural network prediction
title_fullStr Experimental studies of fpso responses with validation by numerical and artificial neural network prediction
title_full_unstemmed Experimental studies of fpso responses with validation by numerical and artificial neural network prediction
title_sort experimental studies of fpso responses with validation by numerical and artificial neural network prediction
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091294092&doi=10.1007%2f978-981-15-5753-8_43&partnerID=40&md5=c03e3d387fa8f7ab618caca2c425139d
http://eprints.utp.edu.my/24698/
_version_ 1741196854709714944
score 11.62408

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