The Geometrical Design of the L-shaped Oscillating Water Column Using Artificial Neural Network

Abstract

Among the various wave energy converters available, the oscillating water column (OWC) shows a number of advantages in terms of implementation and maintenance. In dealing with the survivability issues, incorporating OWCs into reinforced concrete constructions, like breakwaters, is more cost-effective and can endure the effects of seawater impact and erosion.

This paper focuses on optimizing the geometrical design of a novel OWC type, the L-shaped OWC, by establishing a general design procedure to achieve higher energy-capturing efficiency. The performance of the OWC is influenced significantly by the OWC’s geometry under a specified wave condition. It is found that the dimension of the air chamber and water duct is critical in determining OWC’s performance. We develop the chamber and duct design procedure based on the artificial neural network approach by establishing a collection of two-dimensional RANS simulations as the training database. In the end, the performance of the optimal design is compared with the design of a previous paper. The result shows that the capture factor of the optimized chamber geometry of the L-shaped OWC is 12% more than the former design.