Open AccessInvestigating of Flow Field and Power Performance on a Straight-blade Vertical Axis Wind Turbine with CFD Simulation
Author(s) -
Yanfeng Zhang,
Zhiwei Guo,
Xiaowen Song,
Xinyu Zhu,
Chang Cai,
Qingan Li
Publication year - 2021
Publication title -
journal of energy research and reviews
Language(s) - English
Resource type - Journals
ISSN - 2581-8368
DOI - 10.9734/jenrr/2021/v9i130223
Subject(s) - vertical axis wind turbine , freestream , computational fluid dynamics , turbine , airfoil , blade element momentum theory , mechanics , stall (fluid mechanics) , tip speed ratio , marine engineering , wind speed , turbulence , blade element theory , turbine blade , simulation , engineering , aerospace engineering , meteorology , physics , reynolds number
Forecasting the power performance and flow field of straight-blade vertical axis wind turbine (VAWT) and paying attention to the dynamic stall can enhance more adaptability to high turbulence and complicated wind conditions in cities environment. According to the blade element-momentum theory, the force of blade is analyzed in one period of revolution based on the structural characteristics of straight blade airfoil. The power performance of VAWT obtained by computational fluid dynamics (CFD) simulation is compared with experiment to estimate the accuracy about the numerical simulation results. As a result, the trend of average value of simulation Cpower is entirely consistent with the value of experiment data, and the extreme value of average Cpower of VAWT is 0.225 for tip speed ration (TSR) λ=2.19 when the freestream velocity is 8 m/s. The flow separation around the blade surface also gradually changes with the azimuth angle increasing, and the maximum pressure difference on the blade surface appears in the upstream. In the case of high leaf tip velocity, the synthetic velocity is much larger than the incoming wind velocity, and the angle of synthetic velocity increases slightly with the increase of blade tangential velocity. Thus, the angles of attack are very close in two TSRs λ=2.19 and 2.58. The research provides a computational model and theoretical basis for predicting wind turbine flow field to improve wind turbine power performance.