ARBITRARY HYBRID TURBULENCE MODELING APPROACH FOR HIGH-FIDELITY NREL PHASE VIWIND TURBINE CFD SIMULATION

Loading...
Thumbnail Image

Date

Authors

Kamalov, Bagdaulet
Batay, Sagidolla
Zhangaskhanov, Dinmukhamed
Zhao, Yong
Ng, Eddie Yin Kwee

Journal Title

Journal ISSN

Volume Title

Publisher

Fluids

Abstract

Today, growth in renewable energy is increasing, and wind energy is one of the key renewable energy sources which is helping to reduce carbon emissions and build a more sustainable world. Developed countries and worldwide organizations are investing in technology and industrial application development. However, extensive experiments using wind turbines are expensive, and numerical simulations are a cheaper alternative for advanced analysis of wind turbines. The aerodynamic properties of wind turbines can be analyzed and optimized using CFD tools. Currently, there is a general lack of available high-fidelity analysis for the wind turbine design community. This study aims to fill this urgent gap. In this paper, an arbitrary hybrid turbulence model (AHTM) was implemented in the open-source code OpenFOAM and compared with the traditional URANS model using the NREL Phase VI wind turbine as a benchmark case. It was found that the AHTM model gives more accurate results than the traditional URANS model. Furthermore, the results of the VLES and URANS models can be improved by improving the mesh quality for usage of higher-order schemes and taking into consideration aeroelastic properties of the wind turbine, which will pave the way for high-fidelity concurrent multidisciplinary design optimization of wind turbines.

Description

Citation

Kamalov, B., Batay, S., Zhangaskhanov, D., Zhao, Y., & Ng, E. Y. K. (2022). Arbitrary Hybrid Turbulence Modeling Approach for High-Fidelity NREL Phase VI Wind Turbine CFD Simulation. Fluids, 7(7), 236. https://doi.org/10.3390/fluids7070236

Collections

Endorsement

Review

Supplemented By

Referenced By

Creative Commons license

Except where otherwised noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 United States