Modeling of Wind Turbine Interactions and Wind Farm Losses Using the Velocity-Dependent Actuator Disc Model

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Computation Pub Date : 2023-11-01 DOI:10.3390/computation11110213
Ziemowit Malecha, Gideon Dsouza
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Abstract

This paper analyzes the interaction of wind turbines and losses in wind farms using computational fluid dynamics (CFD). The mathematical model used consisted of three-dimensional Reynolds-averaged Navier–Stokes (RANS) equations, while the presence of wind turbines in the flow was simulated as additional source terms. The novelty of the research is the definition of the source term as a velocity-dependent actuator disc model (ADM). This allowed for modeling the operation of a wind farm consisting of real wind turbines, characterized by power coefficients Cp and thrust force coefficients CT, which are a function of atmospheric wind speed. The calculations presented used a real 5 MW Gamesa turbine. Two different turbine spacings, 5D and 10D, where D is the diameter of the turbine, and two different locations corresponding to the offshore and onshore conditions were examined. The proposed model can be used to analyze wind farm losses not only in terms of the geometric distribution of individual turbines but also in terms of a specific type of wind turbine and in the entire wind speed spectrum.
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基于速度相关驱动盘模型的风力涡轮机相互作用和风电场损失建模
本文利用计算流体力学(CFD)分析了风力发电机与风电场损失的相互作用。所使用的数学模型由三维reynolds -average Navier-Stokes (RANS)方程组成,而气流中风力涡轮机的存在作为附加源项进行模拟。该研究的新颖之处在于将源项定义为与速度相关的驱动器盘模型(ADM)。这允许模拟由真实风力涡轮机组成的风电场的运行,其特征是功率系数Cp和推力系数CT,它们是大气风速的函数。给出的计算使用了一个真正的5兆瓦Gamesa涡轮机。测试了两种不同的涡轮机间距5D和10D,其中D是涡轮机的直径,以及对应于海上和陆上条件的两个不同位置。所提出的模型不仅可以根据单个涡轮机的几何分布,而且可以根据特定类型的风力涡轮机和整个风速谱来分析风电场的损失。
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来源期刊
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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