Three-dimensional asymmetric Gaussian wake model of H-VAWTs based on field experiments

Vertical-axis wind turbines (VAWTs) offer strong environmental adaptability, making VAWTs have broad application prospects in complex environments such as urban areas and deep-sea regions. However, optimizing turbine array layouts to reduce wake interference and improve wind farm efficiency remains a significant challenge. Conventional symmetric wake models cannot accurately capture the spatial asymmetry and non-uniformity of real-world VAWT wakes, limiting their effectiveness in array optimization. To address this issue, field experiments employing LiDAR were conducted to capture high-resolution incoming flow and wake velocity profiles of a large-scale straight-blade vertical axis wind turbine (H-VAWT). Based on the field experiment results, a three-dimensional asymmetric Gaussian wake model (3DAGWM) was proposed. This model incorporates the wind shear effect of the incoming flow and introduces an anisotropic Gaussian distribution to describe wake diffusion. In addition, a wake center offset mechanism driven by the Magnus effect was proposed to quantitatively describe wake asymmetry. Comparative analysis with field measurements and literature data demonstrated that 3DAGWM achieves high accuracy, with an overall error of 4.53 %. It can predict wake results at a distance of over 4 D (diameter) more accurately (attaining 3.13 %). It can be applied to the layout design and wake interference analysis of H-VAWTs. It provides a reference for the large-scale application of H-VAWTs in complex environments.