The work represents a systematic numerical optimization methodology using artificial neural network and hybrid genetic algorithm for a bi-directional axial impulse turbine used in wave energy harvesting system. Reynolds-averaged Navier-Stokes equations with k-ε turbulence model were discretized and solved for unstructured tetrahedral grid elements for flow analyses. Efficiency enhancement of the turbine was chosen as an objective. The design variables chosen were numbers of stator and rotor blades. The responses obtained from CFD analysis were used to train the neural network. The optimal point search from the network by hybrid genetic algorithm produced 13% increase in turbine efficiency. Detailed description of the methodology and analysis of the results has been presented in this paper.

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