Prediction and optimization of oscillating wave surge converter using machine learning techniques

With the increase in global environmental problems and the energy crisis, the oscillating wave surge converter has been extensively studied in the past decades owing to its simple geometry and direct energy capture mechanism. However, systematic optimization of this converter is yet to be achieved. Consequently, in this study, a scaled oscillating wave surge converter under regular waves is numerically investigated using the smoothed particle hydrodynamics method, which is validated against experimental data. With the random changes in nine typical design parameters (i.e., the wave period, wave height, water depth, width of bottom border of the flap, width of top border, flap height, hinge height, flap density, and damping of the power take-off system), a total of 379 cases are generated and simulated. Subsequently, the capture factors corresponding to each case are calculated to quantitatively describe the energy conversion efficiency. With the design parameter combinations as input and the capture factors as output, a radial basis function neural network is trained as the prediction model of capture factors of the converters, which performs satisfactorily. Finally, this prediction model is used with the genetic algorithm to optimize the converters corresponding to different wave periods, wave heights, and water depths. By interpolating the open-source optimization results, a converter with high performance can be easily designed. The optimization method used in this study includes a radial basis function neural network based prediction model and genetic algorithm based optimization model, which can not only optimize oscillating wave surge converters but also has the potential to solve other scientific and technical optimization problems.