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

Paul Veers, Matthew Churchfield, Jae Moon, Lance Manuel, Sang Lee

Research output: Contribution to journalArticlepeer-review

5 Scopus Citations


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.

Original languageAmerican English
Article number53
Number of pages34
Issue number1
StatePublished - 2018

Bibliographical note

Publisher Copyright:
© 2018 by the authors.

NREL Publication Number

  • NREL/JA-5000-70698


  • Large eddy simulation
  • Multivariate Multiple Linear Regression (MMLR)
  • Turbine loads
  • Wake modeling
  • Wind turbine wake


Dive into the research topics of 'Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads'. Together they form a unique fingerprint.

Cite this