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

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Abstract

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.
Original languageEnglish
Number of pages15
JournalShips and Offshore Structures
Early online date27 May 2021
DOIs
Publication statusPublished - 29 May 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Floating breakwaters (FBs)
  • fluid–structure interactions (FSI)
  • machine learning
  • cuckoo search algorithm
  • regular and irregular waves
  • hydrodynamic performance

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