Data-driven, non-linear ship response prediction based on time series of irregular, long-crested sea states amidships
Citation Link: https://doi.org/10.15480/882.14290
Publikationstyp
Journal Article
Date Issued
2025-02-01
Sprache
English
TORE-DOI
Journal
Volume
317
Article Number
119963
Citation
Ocean Engineering 317: 119963 (2025)
Publisher DOI
Scopus ID
Publisher
Elsevier
The accurate prediction of vessel responses in waves is crucial for decision-making and contribute to the operational safety and risk minimization. Short-term predictions can be carried out by estimating the vessel's motions and loads based on incident waves. Existing model-based approaches either require computationally intensive simulations that compromise real-time capability or use simplified models affecting the accuracy of the prediction. Therefore, this study explores the feasibility of using neural networks for mapping time signals of surface elevation data and a set of corresponding ship responses, i.e. the heave and pitch motions as well as the vertical bending moment. The approach followed here is built on the assumption that the wave profile amidships is known. A synthetic dataset was generated using a time-domain strip theory solver with considerations of non-linear effects on motions and loads due to large amplitude waves in a variety of irregular, long-crested sea state conditions. We propose two different neural network models, a multi-layer perceptron (MLP) and a fully convolutional neural network (FCNN), and compare their performances on measurement data obtained from model tests in a seakeeping basin. The evaluations also include the freak wave reproduction of the ‘new year wave’. The proposed networks are able to estimate the motions and bending moment accurately for a wide range of sea state conditions, surpassing current state-of-the-art models on the given data sets.
Subjects
Data-driven prediction
Neural networks
New year wave
Non-linear ship response prediction
Response prediction zone
MLE@TUHH
DDC Class
620.1: Engineering Mechanics and Materials Science
623: Military Engineering and Marine Engineering
004: Computer Sciences
551: Geology, Hydrology Meteorology
Publication version
publishedVersion