Development of a parameterized reduced-order vertical-axis wind turbine wake model. Issue 5 (October 2020)

• Record Type: Journal Article
• Authors: Tingey, Eric B; Ning, Andrew
• Is Part Of: Wind engineering. Volume 44:Issue 5(2020)
• Page Start: 494
• Page End: 508
• Publication Date: 2020-10
• Subjects: Vertical-axis wind turbine -- wind farm optimization -- wake model -- computational fluid dynamics -- data parameterization; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X19849864
• Languages: English
• ISSNs: 0309-524X

Abstract

Analyzing or optimizing wind farm layouts often requires reduced-order wake models to estimate turbine wake interactions and wind velocity. We propose a wake model for vertical-axis wind turbines in streamwise and crosswind directions. Using vorticity data from computational fluid dynamic simulations and cross-validated Gaussian distribution fitting, we produced a wake model that can estimate normalized wake velocity deficits of an isolated vertical-axis wind turbine using normalized downstream and lateral positions, tip-speed ratio, and solidity. Compared with computational fluid dynamics, taking over a day to run one simulation, our wake model predicts a velocity deficit in under a second with an appropriate accuracy and computational cost necessary for wind farm optimization. The model agreed with two experimental studies producing percent differences of the maximum wake deficit of 6.3% and 14.6%. The wake model includes multiple wake interactions and blade aerodynamics to calculate power, allowing its use in wind farm layout analysis and optimization.