学术文献库

Arrays of Vertical Axis Wind Turbines (VAWTs) can achieve larger power generation per land area than horizontal axis turbines farms, due to the positive synergy between VATs in close proximity. Theoretical wake models enable the reliable design of the array layout that maximises the energy output, which need to depict the driving wake dynamics. VAWTs generate a highly complex wake that evolves according to two governing length-scales, namely the turbine rotor's diameter and height which define a rectangular shape of the wake cross-section, and feature distinct wake expansion rates. This paper presents analytical VAWT wake models that account for an asymmetric distribution of such wake expansion adopting a top-hat and Gaussian velocity deficit distribution. Our proposed analytical Gaussian model leads to an enhanced initial wake expansion prediction with the wake width ($\varepsilon$) behind the rotor equal to $(β/4 π)^{1/2}$ with $β$ being the ratio of initial wake area to the VAWT's frontal area, which addresses the limitations of previous models that under-predicted the wake onset area. Velocity deficit predictions are calculated in a series of numerical benchmarks consisting of a single and an array of four in-line vertical axis wind turbines. In comparisons with field data and large-eddy simulations, our models provide a good accuracy to represent the mean wake distribution, maximum velocity deficit, and momentum thickness, with the Gaussian model attaining the best predictions.These models will aid to drive the design of VAT arrays and accelerate this technology.