A machine learning method for the evaluation of hydrodynamic performance of floating breakwaters in waves

• Published in: Ships and offshore structures Vol. 17; no. 7; pp. 1447 - 1461
• Main Authors: Saghi, Hassan, Mikkola, Tommi, Hirdaris, Spyros
• Format: Journal Article
• Language: English
• Published: Cambridge Taylor & Francis 03.07.2022; Taylor & Francis Ltd
• Subjects: Aspect ratio; Breakwaters; Coefficients; cuckoo search algorithm; Floating; Floating breakwaters; Floating breakwaters (FBs); fluid-structure interactions (FSI); Free surfaces; hydrodynamic performance; Hydrodynamics; Irregular waves; Machine learning; Methods; Performance evaluation; regular and irregular waves; Simulation models; Support vector machines; Two dimensional models
• ISSN: 1744-5302; 1754-212X; 1754-212X
• DOI: 10.1080/17445302.2021.1927358

This paper presents a two-dimensional simulation model for the idealisation of moored rectangular and trapezoidal floating breakwaters (FB) motions in regular and irregular waves. Fast-Fictitious Domain and Volume of Fluid methods are coupled to track-free surface effects and predict FB motions. Hydrodynamic performance is assessed by a machine learning method based on Cuckoo Search-Least Square Support Vector Machine model (CS-LSSVM). Results confirm that a suitable combination of the aspect ratio of an FB and her sidewall mooring angle could help attenuate incoming waves to a minimum height. It is concluded that moored trapezoidal FBs are more efficient than traditional rectangular designs and subject to further validation CS-LSSVM can be useful in terms of optimising the values of predicted wave transmission coefficients.