Leading effect for wind turbine wake models

TL;DR

This paper demonstrates that incorporating a virtual origin length scale significantly improves wind turbine wake models, especially in the near wake zone, based on tests with laboratory and field data.

Contribution

It introduces the importance of the virtual origin length scale as a leading parameter for more accurate wind turbine wake modeling.

Findings

Models perform better with a virtual origin added

The virtual origin helps define the near wake zone

Improved wake modeling accuracy demonstrated

Abstract

As wind energy expands worldwide, the demand of reliable, fast, cost-efficient wind turbine wake models is growing. This is a significant challenge as wind turbines face various inflow conditions, that include turbulence, inhomogeneities/instationarities and upstream wakes. In consequence, an enormous number of engineering models, each one based on different physical concepts, has been proposed. The majority focuses on the far wake where the mean velocity recovers and turbulence decays after it built up. We argue that the most important, or the leading, parameter for wake modeling is the length scale of a virtual origin. Testing different models from the literature for data sets from laboratory wind turbines and multi-megawatt turbines obtained by LiDAR, we find that all models perform significantly better when such a virtual origin is added. Our results can therefore be used for a yet missing definition of a near wake zone.