Open Access

ARTICLE

Seakeeping analysis of dead ship condition in fishing ships based on Artificial Neural Networks

P. Romero-Tello^1,2, B. Serván-Camas³, José Enrique Gutiérrez Romero^1,2, Javier Ignacio Piazzese⁴

1 Universidad Politécnica de Cartagena
2 Escuela Técnica Superior de Ingeniería Naval y Oceánica, Murcia, Spain
3 Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), Barcelona, Spain
4 Compass Ingeniería y Sistemas (CompassIS), Barcelona, Spain

* Corresponding Author: José Enrique Gutiérrez Romero ()

Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería 2023, 39(4), 1-6. https://doi.org/10.23967/j.rimni.2023.10.004

Accepted 10 October 2023; Issue published 25 October 2023

Abstract

In the operation of ships, assessing seakeeping performance is crucial. Historically, this has been done through experimentation in towing tank basins or numerical computations. However, with the rise of Artificial Intelligence (AI) and increased computational resources, there are many opportunities to use AI in predicting seakeeping performance. This research will utilize a pre-trained Artificial Neural Network (ANN) to evaluate the behaviour of fishing vessels in various operational scenarios. One of the key advantages of using these algorithms is the ability to predict a large number of scenarios quickly, compared to traditional methods. By analysing millions of variations in the principal dimensions of a fishing ship and different sea states, the study aims to identify the optimal seakeeping performance in challenging conditions, ultimately improving ship safety by examining principal form coefficients and dimensions. The research will also determine significant conclusions.

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Keywords

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