Boat-shaped Buoy Optimization of an Ocean Wave Energy Converter Using Neural Networks and Genetic Algorithms

The point absorber is one of the most popular types of ocean wave energy converter (WEC) that harvests energy from the ocean. Often such a WEC is deployed in an ocean location with tidal currents or ocean streams, or serves as a mobile platform to power the blue economy. The shape of the floating body, or buoy, of the point absorber type WEC is important for the wave energy capture ratio and for the current drag force. In this work, a new approach to optimize the shape of the point absorber buoy is developed to reduce the ocean current drag force on the buoy while capturing more energy from ocean waves. A specific parametric modeling is constructed to define the shape of the buoy with 12 parameters. The implementation of neural networks significantly reduces the computational time compared to solving hydrodynamics equations for each iteration. And the optimal shape of the buoy is solved using a genetic algorithm with multiple self-defined functions. The final optimal shape of the buoy in a case study reduces 68.7% of current drag force compared to a cylinder-shaped buoy, while maintaining the same level of energy capture ratio from ocean waves. The method presented in this work has the capability to define and optimize a complex buoy shape, and solve for a multi-objective optimization problem.