Validation and comparison of turbulence models for predicting wakes of vertical axis wind turbines

Andrew Barnes, Daniel Marshall-Cross, Ben Richard Hughes

Research output: Contribution to journalArticlepeer-review

Abstract

Vertical axis wind turbine (VAWT) array design requires adequate modelling of the turbine wakes to model the flow throughout the array and, therefore, the power output of turbines in the array. This paper investigates how accurately different turbulence models using 2D computational fluid dynamics (CFD) simulations can estimate near and far wakes of VAWTs to determine an approach towards accurate modelling for array design. Three experiments from the literature are chosen as baselines for validation, with these experiments representing the near to far wake of the turbine. Five URANS turbulence models were chosen due to their common and potential usage for VAWT CFD: models k–ω SST, k–ω SST LRN, k–ω SSTI, transition SST, and k–k l–ω. In addition, the lifting line-free vortex wake (LLFWV) model was tested as an alternative to CFD for the far turbine wake where it was appropriate for use. The results for turbulent kinetic energy and vorticity were compared for the first experiment, whilst streamwise and cross-stream velocity were used for the other two experiments. It was found that none of the turbulence models tested or LLFVW produced adequate estimations within the methodology tested, however, transition SST produced the closest estimations. Further adjustments to the methodology are required to improve accuracy due to their large impact on results including use of 3D CFD, adjustment of surface roughness, and inlet flow characteristics.

Original languageEnglish
Pages (from-to)339-362
Number of pages24
JournalJournal of Ocean Engineering and Marine Energy
Volume7
Issue number4
Early online date23 Jul 2021
DOIs
Publication statusE-pub ahead of print - 23 Jul 2021

Keywords

  • VAWT
  • CFD
  • wind energy

Fingerprint

Dive into the research topics of 'Validation and comparison of turbulence models for predicting wakes of vertical axis wind turbines'. Together they form a unique fingerprint.

Cite this