A cost and time effective prediction technique for OWC-WEC devices

Prediction of the hydrodynamic efficiency of a Wave Energy Converter (WEC) device is crucial to evaluate the design and the concept of the device. Experimental and numerical techniques are the main tools currently available for WEC designers; however, these techniques are still costly and too time expensive to be used for optimisation and commercial purposes. It is, therefore, important to develop an efficient and cost/time-effective technique in order to investigate the hydrodynamic characteristics of WEC devices. In this work, an Adaptive Neuro-Fuzzy Inference System (ANFIS) technique was developed to predict the hydrodynamic efficiency of WEC devices. ANFIS models were designed, trained and tested using published experimental datasets for the hydrodynamic efficiency of fixed Oscillating Water Column (OWC) devices, and different types of membership functions were examined to develop the best accurate model. ANFIS technique was found to provide good estimates in comparison with experimental results and can be used to predict the hydrodynamic efficiency of WEC devices during the early stages of design.