Assessment of blade element momentum codes under varying turbulence levels by comparing with blade resolved computational fluid dynamics
Yusik Kim,
Helge Aa Madsen,
Maria Aparicio-Sanchez,
Georg Pirrung and
Thorsten Lutz
Renewable Energy, 2020, vol. 160, issue C, 788-802
Abstract:
Unsteady load calculations for a large wind turbine using two blade element momentum (BEM) codes (FAST, HAWC2) are compared with results from blade resolved computational fluid dynamics simulations (FLOWer). This is to assess the performance of BEM under varying turbulence levels. Instantaneous and mean of power, spectral data and sectional blade forces are analysed for the three codes. The AVATAR research turbine with a radius of 102.88 m is used in this study. Based on comparisons, FLOWer and HAWC2 show a very close trend in mean power variations with different turbulence intensities. In contrast, FAST does not show a similar trend. This is conjectured to be due to different implementation in the BEM codes of the unsteady turbulence inflows, such as azimuthal variation of induction and dynamic inflow. The maximum error of CP is 6% for FAST and 1% for HAWC2 compared to FLOWer.
Keywords: Wind turbine aerodynamics; Induction calculation method; Power scaling (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148120308995
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:160:y:2020:i:c:p:788-802
DOI: 10.1016/j.renene.2020.06.006
Access Statistics for this article
Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides
More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().