Deep understanding on the impacts of leading edge erosion on the performance and flow characteristics of wind turbines is significant for the blade design and wind farms management. Pitting erosion and three levels of delamination are considered in the present study. The results show that the degrees of leading edge erosion have great influence on the flow separation, tangential force coefficient, normal force coefficient as well as the power output of the wind turbine. Leading edge erosion has the greatest impact on aerodynamics of the wind turbine at 15 m/s, where the maximum loss in the power output can reach up to 73.26%.
In order to study turbulence conditions in an underwater square cavity, the large eddy simulation method was adopted to analyze flow field distributions in the cavity as well as its development and pressure pulsation characteristics at some key positions. MATLAB was also adopted to realize Fast Fourier Transform of signals in time domain and obtain pressure pulsation levels in frequency domain. Based on the analyzed results, pressure pulsation characteristics of key points in the cavity were further discussed. The results showed that pressure pulsation frequencies and characteristics were different with different positions in the square cavity and were closely related with relevant vortex motion states. It was found through comparisons with the experimental results, that pressure pulsation simulation had a good consistency with the experiment when the analyzed frequency was more than 31.5 Hz. As a result, feasibility and accuracy of numerical simulation and Fourier analysis methods were verified. Finally, a numerical model of square cavity in near sound field was built, and sound source intensity distributions at two frequency points were extracted. It could be found that the sound source intensity was large at the rear-edge step, which was consistent with the intensity distribution of vortices. Therefore, reliability of the numerical model in this paper was indirectly verified in the results.
Optimizing the wind farm layout requires accurately quantifying the wind-turbine wake distribution to minimize interference between wakes. Thus, the accuracy of wind turbine wake superposition models is critical. The sum of squares (SS) model is currently touted as the most accurate, but its application in engineering is hampered by its overestimation of the velocity deficit of the mixed wake. Therefore, previous work relied on approximate power calculations for performing optimization. The physical meaning of the SS model is unclear, which makes optimization difficult. In this study, a univariate linear correction idea is proposed based on the linear increase phenomenon of the SS method error. The unknown coefficients are obtained by fitting experimental data. The results demonstrate that the proposed method can accurately quantify the full-wake two-dimensional distribution of the mixed wake.
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