Sea state estimation based on the motion data of a moored FPSO using neural networks: An evaluation with multiple draft conditions. (15th May 2023)

• Record Type: Journal Article
• Authors: Bisinotto, Gustavo A.; Sparano, João V.; Simos, Alexandre N.; Cozman, Fabio G.; Ferreira, Marcos D.; Tannuri, Eduardo A.
• Is Part Of: Ocean engineering. Volume 276(2023)
• Publication Date: 2023-05-15
• Subjects: Sea state estimation -- Vessel motions -- Neural networks -- Draft variation -- FPSO; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2023.114235
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Useful information for offshore systems can be obtained by monitoring sea state parameters, but the accurate estimation of the on-site wave condition is challenging. This paper deals with motion-based wave estimation using neural networks. The main novelty of this contribution is the evaluation of the performance of the data-driven inference system with motion data from different draft conditions. The study is based on the simulated motions of a Floating Production Storage and Offloading (FPSO) unit with multiple loadings, subjected to typical sea conditions observed offshore the Brazilian coast. The inference models were trained to estimate two independent sets of wave related parameters, corresponding to modal wave statistics, which were post processed to identify single and double-peaked sea states. Results showed that estimation errors were quite similar along the evaluated drafts, with an overall good agreement between estimations and reference values. However, a limitation of the method regarding less frequent sea conditions was observed, even when significant wave response is induced. Also, an analysis of the robustness of the estimation models with respect to a draft variation of ± 0.5 m was carried out considering the global wave statistics, presenting small average deviations compared to the expected values. Highlights: Ship motion-based wave inference is performed with neural network models. Crossed sea states are considered by estimating two sets of sea spectrum parameters. Models derived from data of different vessel drafts present similar performance. The inference models are robust for a variation of ± 0.5 m in the draft condition. Less frequent sea conditions may present challenges to the data-driven estimation.