Abstract

A smart buoy is used for one method of visualizing the ocean. The design concept of smart buoy is measuring ocean conditions by mounting several sensors, and it also has an independent power supply system. In this study, a vertical-axis tidal turbine is installed under the buoy as a power turbine by tidal current. The design of turbine considered that the turbine will slant due to the dynamic force of the water flow. Therefore, an optimized design of the turbine under slanting condition with a higher power coefficient over a wide range of flow conditions was carried out using a global search for optimization system based on genetic algorithm. In order to reduce the simulation cost, ANN (Artificial Neural Networks) was employed as a meta-model for a RANS equation solver. The output shaft power and torque amplitudes under transient conditions at different tip speed ratios were applied for multiple purposes in order to cover a wide operating range with high output shaft torque with reducing negative torque. The optimized turbine changes the angle of the blades to match the inclination of the turbine, resulting in a high skew blade. This is attributed to the fact that the blade angle at the negative torque position is optimized to follow the flow of the optimized turbine, which is believed to contribute to the simultaneous improvement of both the torque coefficient and the power coefficient of the turbine. The optimized turbine was manufactured using a 3D printer, and its performance was experimentally verified using a circulating water tank. The experimental results were in good agreement with the CFD results and showed that the efficiency was improved in the case of the inclination condition of 30 degrees to the horizontal direction compared to the case without slanting condition. It was found that the optimization system using ANN-assisted genetic algorithm possible to design the slanting tidal power turbine for smart buoys with both high efficiency and wide operating range.

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