Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation

Author:

Bui HaiORCID,Bakhoday-Paskyabi Mostafa,Mohammadpour-Penchah MohammadrezaORCID

Abstract

Abstract. In this study, we present the development of a Simple Actuator Disk model for Large-Eddy Simulation (SADLES), implemented within the Weather Research and Forecasting (WRF) model, which is widely used in atmospheric research. The WRF-SADLES model supports both idealized studies and realistic applications through downscaling from realistic data, with a focus on resolutions of tens of meters. Through comparative analysis with the Parallelized Large-eddy Simulation Model (PALM) at resolutions of 10 and 30 m, we validate the effectiveness of WRF-SADLES in simulating the wake characteristics of a 5 MW wind turbine. Results indicate good agreement between WRF-SADLES at 30 m resolution and 10 m resolution and the PALM model. Additionally, we demonstrate a practical case study of WRF-SADLES by downscaling ERA5 reanalysis data using a nesting method to simulate turbine wakes at the Alpha Ventus wind farm in the south of the North Sea. The meso-to-micro downscaling simulation reveals that the wake effect simulated by WRF-SADLES at the FINO1 offshore meteorological mast station aligns well with the cup anemometer and lidar measurements. Furthermore, we investigate an event of farm-to-farm interaction, observing a 16 % reduction in ambient wind speed and a 38 % decrease in average turbine power at Alpha Ventus due to the presence of a wind farm to the southwest. WRF-SADLES offers a promising balance between computational efficiency and accuracy for wind turbine wake simulations, making it valuable for wind energy assessments and wind farm planning.

Funder

Horizon 2020

Publisher

Copernicus GmbH

Reference60 articles.

1. Anderson, C.: Wind turbines: Theory and practice, Cambridge University Press, 2020. a

2. Ardillon, E., Paskyabi, M. B., Cousin, A., Dimitrov, N., Dupoiron, M., Eldevik, S., Fekhari, E., Ferreira, C., Guiton, M., Jezequel, B., Joulin, P.-A., Lovera, A., Mayol, L., and Penchah, M. R.: Turbine loading and wake model uncertainty, Deliverable D3.2 for HIPERWIND project, https://www.hiperwind.eu/ (last access: 23 May 2024), 2023. a

3. Arthur, R. S., Mirocha, J. D., Marjanovic, N., Hirth, B. D., Schroeder, J. L., Wharton, S., and Chow, F. K.: Multi-scale simulation of wind farm performance during a frontal passage, Atmosphere, 11, 245, https://doi.org/10.3390/atmos11030245, 2020. a

4. Avissar, R. and Schmidt, T.: An evaluation of the scale at which ground-surface heat flux patchiness affects the convective boundary layer using large-eddy simulations, J. Atmos. Sci., 55, 2666–2689, 1998. a

5. Bakhoday-Paskyabi, M., Bui, H., and Mohammadpour Penchah, M.: Atmospheric-Wave Multi-Scale Flow Modelling, Deliverable D2.1 for HIPERWIND project, https://www.hiperwind.eu/ (last access: 23 May 2024), 2022a. a, b, c, d, e

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3