ASSESSMENT OF SEA STATE ESTIMATION WITH CONVOLUTIONAL NEURAL NETWORKS BASED ON THE MOTION OF A MOORED FPSO SUBJECTED TO HIGH-FREQUENCY WAVE EXCITATION

Gustavo A. Bisinotto, Lucas P. Cotrim, Fabio G. Cozman, Eduardo A. Tannuri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Motion-based wave inference has been extensively discussed over the past years to estimate sea state parameters from the measured motions of a vessel. Most of those methods rely on the linearity assumption between waves and ship response and present a limitation related to high-frequency waves, whose first-order excitation is mostly filtered by the vessel. In a previous study in this project, the motion of a spread-moored FPSO platform, associated with a dataset of environmental conditions, was used to train convolutional neural networks models so as to estimate sea state parameters, displaying good results, even for high-frequency waves. This paper further explores this supervised learning inference method, focusing on the estimation of unimodal high-frequency waves along with an evaluation of particular features related to the approach. The analysis is performed by training estimation models under different circumstances. First, models are obtained from the simulated platform response out of a dataset with synthetic sea state parameters, that are uniformly distributed. Then, a second dataset of metocean conditions, with unimodal waves observed at a Brazilian Offshore Basin, is considered to verify the behavior of the models with data that have different distributions of wave parameters. Next, the input time series are filtered to separate first-order response and slow drift motion, allowing the derivation of distinct models and the determination of the contribution of each motion component to the estimation. Finally, a comparison among the outcomes of the approach based on neural networks evaluated under those conditions and the results obtained by the traditional Bayesian modeling is carried out, to assess the performance presented by the proposed models and their applicability to face one of the classical issues on motion-based wave inference.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791885901
ISBN (Print)9780791885901
DOIs
StatePublished - 5 Jun 2022
Externally publishedYes
EventASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2022 - Hamburg, Germany
Duration: 5 Jun 202210 Jun 2022

Publication series

NameVolume 5B: Ocean Engineering; Honoring Symposium for Professor Günther F. Clauss on Hydrodynamics and Ocean Engineering

Conference

ConferenceASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2022
Country/TerritoryGermany
CityHamburg
Period5/06/2210/06/22

Keywords

  • convolutional neural networks
  • high-frequency waves
  • moored FPSO
  • Sea state estimation

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