Sea State Estimation with Neural Networks Based on the Motion of a Moored FPSO Subjected to Campos Basin Metocean Conditions

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

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

Resumen

Important information for the design and operation of oceanic systems can be obtained by assessing local sea state parameters such as significant height, peak period and incidence direction. Techniques for motion-based inference and their possible drawbacks have been extensively discussed in the literature (their motivation coming from the simplicity of the required instrumentation when compared to traditional measuring systems), and machine learning approaches are now appearing in a few investigations. This paper addresses the estimation problem through supervised learning, using time series with the movement of a moored vessel to train neural networks models so as to estimate the sea state. Such time series are obtained through simulations, that consider a model of a spread-moored FPSO (Floating Production Storage and Offloading) platform with constant draft, out of a set of metocean conditions observed at Brazil’s Campos Basin. A sensitivity analysis for different classes of neural networks was run, based on the significant height estimation, to choose the network architecture with the best results with respect to the mean absolute error metric. That topology was trained and employed in the estimation of the remaining sea state parameter, separately. The outcomes of the proposed models were confronted with other neural networks-based methods and showed up a comparable or slightly better performance in the error metrics. A preliminary discussion of the ability of the approach to deal with some classical issues on motion-based estimation is presented.

Idioma originalInglés
Título de la publicación alojadaIntelligent Systems - 10th Brazilian Conference, BRACIS 2021, Proceedings, Part 2
EditoresAndré Britto, Karina Valdivia Delgado
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas294-308
Número de páginas15
ISBN (versión impresa)9783030916985
DOI
EstadoPublicada - 2021
Publicado de forma externa
Evento10th Brazilian Conference on Intelligent Systems, BRACIS 2021 - Virtual, Online
Duración: 29 nov. 20213 dic. 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen13074 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia10th Brazilian Conference on Intelligent Systems, BRACIS 2021
CiudadVirtual, Online
Período29/11/213/12/21

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