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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
EditorialAmerican Society of Mechanical Engineers (ASME)
ISBN (versión digital)9780791885901
ISBN (versión impresa)9780791885901
DOI
EstadoPublicada - 5 jun. 2022
Publicado de forma externa
EventoASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2022 - Hamburg, Alemania
Duración: 5 jun. 202210 jun. 2022

Serie de la publicación

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

Conferencia

ConferenciaASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2022
País/TerritorioAlemania
CiudadHamburg
Período5/06/2210/06/22

Huella

Profundice en los temas de investigación de 'ASSESSMENT OF SEA STATE ESTIMATION WITH CONVOLUTIONAL NEURAL NETWORKS BASED ON THE MOTION OF A MOORED FPSO SUBJECTED TO HIGH-FREQUENCY WAVE EXCITATION'. En conjunto forman una huella única.

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