Open AccessParametric study and optimization of a two‐body wave energy converter
Author(s) -
Bao Xingxian,
Xiao Weijie,
Li Shubo,
Iglesias Gregorio
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12254
Subject(s) - parametric statistics , taguchi methods , artificial neural network , backpropagation , power (physics) , wave energy converter , energy (signal processing) , parametric model , work (physics) , computer science , engineering , control theory (sociology) , mathematics , statistics , physics , mechanical engineering , artificial intelligence , machine learning , control (management) , quantum mechanics
The parametric study and optimization of a two‐body wave energy converter (WEC) for the wave and current conditions in the region of Zhaitang Island (China) is presented. Nine parameters are considered, and their influence on the power captured by the two‐body WEC is investigated following both single‐parameter and multi‐parameter approaches. A backpropagation neural network model is developed and applied to predict the captured power for any given values of the nine parameters and the wave frequency. Then the robust design method, also known as the Taguchi method, is implemented to study the comprehensive effects of the parameters on the power output of the device. Moreover, scale model experiments are conducted to verify and confirm the influence of the principal parameters on the power output. Combining numerical simulations, a neural network model and experimental work, this study provides an optimization programme for the main parameters of the device in the target sea region and, at a more general level, references for two‐body WEC designs based on specific sea states.