Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network
FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull gi...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/161122 |
| _version_ | 1826121008319299584 |
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| author | Wu, Lei Yang, Yaowen Maheshwari, Muneesh |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Wu, Lei Yang, Yaowen Maheshwari, Muneesh |
| author_sort | Wu, Lei |
| collection | NTU |
| description | FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points. |
| first_indexed | 2024-10-01T05:25:44Z |
| format | Journal Article |
| id | ntu-10356/161122 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:25:44Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1611222022-08-16T06:26:18Z Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network Wu, Lei Yang, Yaowen Maheshwari, Muneesh School of Civil and Environmental Engineering Maritime Institute Engineering::Maritime studies Offshore Oil and Gas Industry Strain Prediction FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points. Singapore Maritime Institute (SMI) This research is funded by the Singapore Maritime Institute under the Asset Integrity & Risk Management (AIM) R&D Programme–Project SMI-2015-OF-04, and supported by the Taishan Scholars Program of Shandong Province (tsqn201909067). 2022-08-16T06:26:18Z 2022-08-16T06:26:18Z 2020 Journal Article Wu, L., Yang, Y. & Maheshwari, M. (2020). Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network. Marine Structures, 72, 102762-. https://dx.doi.org/10.1016/j.marstruc.2020.102762 0951-8339 https://hdl.handle.net/10356/161122 10.1016/j.marstruc.2020.102762 2-s2.0-85083007914 72 102762 en SMI-2015-OF-04 Marine Structures © 2020 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering::Maritime studies Offshore Oil and Gas Industry Strain Prediction Wu, Lei Yang, Yaowen Maheshwari, Muneesh Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network |
| title | Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network |
| title_full | Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network |
| title_fullStr | Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network |
| title_full_unstemmed | Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network |
| title_short | Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network |
| title_sort | strain prediction for critical positions of fpso under different loading of stored oil using gaifoa bp neural network |
| topic | Engineering::Maritime studies Offshore Oil and Gas Industry Strain Prediction |
| url | https://hdl.handle.net/10356/161122 |
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