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Energies 2018, 11(1), 53; doi:10.3390/en11010053

Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads

Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX 78712, USA
National Renewable Energy Laboratory, Golden, CO 80303, USA
Department of Mechanical Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Author to whom correspondence should be addressed.
Received: 27 October 2017 / Revised: 8 December 2017 / Accepted: 14 December 2017 / Published: 28 December 2017
(This article belongs to the Special Issue Wind Turbine Loads and Wind Plant Performance)


Wind turbines within an array do not experience free-stream undisturbed flow fields. Rather, the flow fields on internal turbines are influenced by wakes generated by upwind unit and exhibit different dynamic characteristics relative to the free stream. The International Electrotechnical Commission (IEC) standard 61400-1 for the design of wind turbines only considers a deterministic wake model for the design of a wind plant. This study is focused on the development of a stochastic model for waked wind fields. First, high-fidelity physics-based waked wind velocity fields are generated using Large-Eddy Simulation (LES). Stochastic characteristics of these LES waked wind velocity field, including mean and turbulence components, are analyzed. Wake-related mean and turbulence field-related parameters are then estimated for use with a stochastic model, using Multivariate Multiple Linear Regression (MMLR) with the LES data. To validate the simulated wind fields based on the stochastic model, wind turbine tower and blade loads are generated using aeroelastic simulation for utility-scale wind turbine models and compared with those based directly on the LES inflow. The study’s overall objective is to offer efficient and validated stochastic approaches that are computationally tractable for assessing the performance and loads of turbines operating in wakes. View Full-Text
Keywords: wind turbine wake; large eddy simulation; wake modeling; Multivariate Multiple Linear Regression (MMLR); turbine loads wind turbine wake; large eddy simulation; wake modeling; Multivariate Multiple Linear Regression (MMLR); turbine loads

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Moon, J.S.; Manuel, L.; Churchfield, M.J.; Lee, S.; Veers, P.S. Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads. Energies 2018, 11, 53.

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