Recently, the Key Laboratory of Wind Energy Utilization/National Energy Wind Power Blade R&D (Experimental) Center, Institute of Engineering Thermophysics, Chinese Academy of Sciences has conducted in-depth research on the wake modeling model of wind turbines, and proposed to expand the expansion coefficient and direction of the wake model The turbulence intensity is connected, and the classic MOST theory is introduced to establish a new MOST-Gaussian wake model. By comparing with high-precision numerical simulation data and experimental data, it is verified that the new model has good prediction ability for wake velocity loss. Compared with the traditional wake model, the MOST-Gaussian model can be applied to different conditions of surface roughness and atmospheric stability, and is of great significance for accurate microsite location and wake control of wind farms.
In the wind power industry, the flow field downstream of the wind wheel is usually called wake (Figure 1). The wake downstream of the wind turbine can be divided into a near wake area and a far wake area. The length of the near wake region is generally 1 to 2 times the diameter of the impeller, and the speed and turbulence intensity distribution are closely related to the number of blades, aerodynamic parameters of the blade, and the development of the blade tip vortex. The length of the far wake area can reach 10-15 times the diameter of the wind wheel. The flow field distribution is less affected by the geometry of the wind wheel, and the velocity distribution is close to the Gaussian distribution, showing a certain self-similarity (Figure 2).
By assuming that the velocity loss satisfies the Gaussian distribution, a two-dimensional wake model is reported in foreign literature. The predicted velocity distribution and the experimental results are in good agreement with the large eddy simulation results. However, the wake expansion coefficient in the model needs to pass the experimental results. Or the simulation data can be obtained by fitting, and the versatility is poor.
The MOST-Gaussian wake model has two main innovations. First, it is assumed that the wake expansion coefficient is related to the intensity of the spanwise turbulence. The traditional Gaussian model believes that the wake expansion coefficient is related to the intensity of the turbulent flow. However, wind tunnel experiments and numerical simulations show that the wake usually expands in the span and normal directions, that is, the wake recovery is mainly closely related to the momentum transport in the span and normal directions. Related, so the assumption of the MOST-Gaussian model is more reasonable. Another innovation is that by introducing MOST theory, the new model uses surface roughness and atmospheric stability as input parameters, which can study the law of wake development under different environmental conditions, expand the application range of the model, and make the model have better Versatility. Comparing the prediction results of the MOST-Gaussian wake model with other models reported in the literature, the speed loss of the downstream position of the hub height is different (Figure 3). The comparison results show that the new model has better prediction accuracy under different surface roughness. Unlike the models reported in the literature, the MOST-Gaussian model can predict the velocity deficit at different atmospheric stability (Figure 4), and the new model can better reflect the evolution of wakes at different stability.
Further analysis of the near wake region of the wind turbine shows that the velocity deficit profile exhibits two peaks. The use of a bimodal Gaussian distribution function can more accurately characterize the characteristics of the near wake, and at the same time, the bimodal Gaussian distribution function can be degraded into a conventional Single peak distribution, so it degenerates into the MOST-Gaussian wake model in the far wake region, indicating that the dual-peak Gaussian distribution model has great potential in predicting wake velocity distribution.
The above research results were published in Applied Energy, an international energy journal. Cheng Yu, PhD student of the Institute of Engineering Thermophysics, is the first author of the paper, and researcher Zhang Mingming is the corresponding author.
Figure 1 Wake structure of wind turbine
Figure 2 Speed ​​loss in the form of Gaussian distribution
Figure 3 Comparison of speed losses at different downstream positions at the hub height
Figure 4 Speed ​​loss of hub height under different atmospheric stability
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