Issue |
MATEC Web Conf.
Volume 70, 2016
2016 The 3rd International Conference on Manufacturing and Industrial Technologies
|
|
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Article Number | 12004 | |
Number of page(s) | 6 | |
Section | Development and Applications of Energy | |
DOI | https://doi.org/10.1051/matecconf/20167012004 | |
Published online | 11 August 2016 |
Modelling of a CFD Microscale Model and Its Application in Wind Energy Resource Assessment
1 School of Aeronautical Science and Engineering, Beihang University, 100083, Beijing, P. R. China
2 National computational fluid dynamics laboratory of China, 100083, Beijing, P. R. China
3 Sino-French Engineering School, Beihang University, 100083, Beijing, P. R. China
a Corresponding author: buaayjs@buaa.edu.cn
The prediction of a wind farm near the wind turbines has a significant effect on the safety as well as economy of wind power generation. To assess the wind resource distribution within a complex terrain, a computational fluid dynamics (CFD) based wind farm forecast microscale model is developed. The model uses the Reynolds Averaged Navier-Stokes (RANS) model to characterize the turbulence. By using the results of Weather Research and Forecasting (WRF) mesoscale weather forecast model as the input of the CFD model, a coupled model of CFD-WRF is established. A special method is used for the treatment of the information interchange on the lateral boundary between two models. This established coupled model is applied in predicting the wind farm near a wind turbine in Hong Gang-zi, Jilin, China. The results from this simulation are compared to real measured data. On this basis, the accuracy and efficiency of turbulence characterization schemes are discussed. It indicates that this coupling system is easy to implement and can make these two separate models work in parallel. The CFD model coupled with WRF has the advantage of high accuracy and fast speed, which makes it valid for the wind power generation.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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