A Machine Learning Approach to the Analysis of Wave Energy Converters

The hydrodynamic analysis and estimation of the performance of wave energy converters (WECs) is generally performed using semi-analytical/numerical models. Commercial boundary element codes are widely used in analyzing the interactions in arrays comprising of wave energy conversion devices. However, the analysis of an array of such converters becomes computationally expensive, and the computational time increases as the number of devices in the system is increased. As such determination of optimal layouts of WECs in arrays becomes extremely difficult. In this study, an innovative active experimental approach is presented to predict the behaviour of theWECs in arrays. The input variables are the coordinates of the center of the wave energy converters. Simulations for training examples and validation are performed for an array of OscillatingWave Surge Converters, using the mathematical model of Sarkar et. al. (Proc. R. Soc. A, 2014). As a part of the initial findings, results will be presented on the performance of wave energy converters located well inside an array. The broader scope/aim of this research would be to predict the behaviour of the individual devices and overall performance of the array for arbitrary layouts of the system and then identify optimal layouts subject to various constraints.