Optimization of a Horizontal Axis Tidal Current Turbine by Multi-Objective Optimization

Abstract

A global search optimization system is applied to the design of a horizontal axis tidal current turbine with shroud. 11 design parameters of the turbine blade and 4 design parameters of the shroud casing are considered for the optimization using a genetic algorithm. In order to reduce the simulation cost, a neural network is applied as the meta-model of the RANS (Reynolds-averaged Navier–Stokes) equation solver. Multi-objectives of the power coefficient at different tip speed ratios are applied to cover a wide operating range of the turbine. The CFD (Computational fluid dynamics) for optimization is validated experimentally for the case of a baseline design, and an optimum design is proposed. In this paper, a static structural analysis has been performed, and its robustness is confirmed under several operating conditions. Furthermore, internal flow of the optimized turbine is discussed in detail. It is found that the optimized blade generates a swirling flow and suppresses flow separation at the diffuser wall. The wide angle of the diffuser successfully achieves a high pressure recovery ratio and results in a high level of suction at the inlet of the turbine. It is found that the high-performance tidal turbine is possible to design if both the blade and the shroud diffuser are optimized at the same time.