In this paper, an efficient Machine Learning-based Legendre wavelet method (ML-LWM) is utilised to estimate the parameters in ship damping models. Damping is critical for the roll motion response of a ship in waves. To the best of our knowledge, there is no Legendre Wavelet Method (LWM) has been reported to estimate the damping and restoring moments in ship dynamics models. LWM is applied to estimate roll angle, damping coefficients and restoring moments. Some numerical examples are given to demonstrate the validity and applicability of the proposed ML-LWM. The ML-LWM results are compared with the results obtained by the Homotopy Perturbation Method (HPM). Also, the proposed results are validated with the experimental data. Satisfactory agreement with experimental and HPM is noticed. The efficiency of the proposed wavelet method is confirmed by CPU runtime.
Machine learning-based wavelet method for estimating the parameters in ship roll damping models using Legendre polynomials
Ships and Offshore Structures ; 18 , 12 ; 1668-1678
2023-12-02
11 pages
Article (Journal)
Electronic Resource
Unknown
Nonlinear Models of Ship Roll Damping
IOS Press | 1988
Nonlinear models of ship roll damping
Tema Archive | 1988
|Roll Damping Predictions using Physics-based Machine Learning
TIBKAT | 2019
|Ship roll damping using the rudder and stabilising fins
British Library Conference Proceedings | 1992
|Roll damping characteristics of a trimaran displacement ship
Online Contents | 1999