3-blades onshore wind turbines, with good performance in wind power utilization, are widely applied in mountainous wind farms around the world, bring considerable profits in energy consumption and social development. As the rated power of single wind turbine and the length of single blade constantly increase, the structure of single blade has become increasingly lightweight and flexible, resulting in more pronounced wind-induced vibration (WIV) and fluid-structure interaction (FSI) phenomena. Existing studies have mostly concentrated on rigid wind turbine models under flat terrain conditions that fails to sufficiently consider mountainous factors and the feedback effects of WIV on the flow field, probably incapable of accurately predicting thrust, output power and wake characteristic.
Focusing on the above problems, this report aims to discuss the aeroelastic performance of NREL-5MW wind turbine in different hill slope conditions. With the combination of STAR-CCM+ and ABAQUS, the computational framework of bidirectional FSI is structured to simulate aeroelastic responses and flow field evolution. Output power fluctuation affected by the WIV of blades and turbulence is clearly revealed. The results show that the WIV of blades is not only controlled by dominant low-frequency excitation, but affected by high-order frequency components, corresponding to non-linear aeroelastoc effects. The vibratory blades could weaken both average thrust and output power by approximately 0.5% but their standard deviations, affected by blade flapwise vibration and unsteady turbulence, are respectively amplified by 49.9% and 43.4%. With the same altitude condition, larger thrust and output power could be obtained in gentle slope case while more unsteady thrust and output power occur in abrupt slope case. Moreover, the vibratory blades could delay wake evolution and enhance wake fluctuation, adverse for the output power of downstream wind turbines. These conclusions are favorable for performance improvement, efficient operation, and mountainous wind farm optimization.