Dynamics of Large Scale Turbulence in Finite-Sized Wind Farm Canopy Using Proper Orthogonal Decomposition and a Novel Fourier-POD Framework
Dynamics of Large Scale Turbulence in Finite-Sized Wind Farm Canopy Using Proper Orthogonal Decomposition and a Novel Fourier-POD Framework
复制标题
DOI：
10.20944/preprints202002.0390.v1
复制
发表时间：
2020-02
期刊：
影响因子：
3.2
通讯作者：
Tanmoy Chatterjee;Y. Peet
中科院分区：
文献类型：
--
作者：
Tanmoy Chatterjee;Y. Peet
大气边界层（ABL）中的大尺度相干结构是风力发电场的重要组成部分。为了了解大尺度结构的动力学特性，本文对有限尺寸的风力涡轮机阵列遮篷进行了正交分解（POD）分析。POD分析揭示了大尺度相干模式的动力学，以及在异质风电场的本征谱的缩放。我们还提出了适应一种新的傅里叶POD（FPOD）模态分解进行POD分析的展向傅里叶变换速度。在研究三维能量相干模态时，FPOD方法有助于我们在展向和流向上解耦长度尺度。此外，FPOD特征谱还提供了更深层次的见解，了解三维POD特征谱的标度趋势及其收敛性，这是固有的湍流动力学。了解风电场流中大尺度结构的行为，不仅有助于更好地评估降阶模型（ROM）预测的流量和发电量，但也将发挥重要作用，提高决策能力，在未来的风电场优化算法。此外，这项研究也提供了指导，更好地了解POD分析在湍流和风电场社区。
Large scale coherent structures in the atmospheric boundary layer (ABL) are known to contribute to the power generation in wind farms. In order to understand the dynamics of large scale structures, we perform proper orthogonal decomposition (POD) analysis of a finite sized wind turbine array canopy in the current paper. The POD analysis sheds light on the dynamics of large scale coherent modes as well as on the scaling of the eigenspectra in the heterogeneous wind farm. We also propose adapting a novel Fourier-POD (FPOD) modal decomposition which performs POD analysis of spanwise Fourier-transformed velocity. The FPOD methodology helps us in decoupling the length scales in the spanwise and streamwise direction when studying the 3D energetic coherent modes. Additionally, the FPOD eigenspectra also provide deeper insights for understanding the scaling trends of the three-dimensional POD eigenspectra and its convergence, which is inherently tied to turbulent dynamics. Understanding the behaviour of large scale structures in wind farm flows would not only help better assess reduced order models (ROM) for forecasting the flow and power generation but would also play a vital role in improving the decision making abilities in wind farm optimization algorithms in future. Additionally, this study also provides guidance for better understanding of the POD analysis in the turbulence and wind farm community.