论文标题
风电空气动力学的三维动态模式分解分析
A three-dimensional dynamic mode decomposition analysis of wind farm flow aerodynamics
论文作者
论文摘要
高保真大型模拟适合于洞察扩展风电场中复杂的流动动力学。为了更好地理解这些流动动力学,我们使用动态模式分解(DMD)来分析和通过大型模拟的大规模数字模拟风场(LES)分析和重建流场。考虑了不同的风电场布局,我们发现与传统的水平惊人相比,水平和垂直惊人的结合可以改善风电场的性能。我们使用振幅选择(AP)和启动稀疏(SP方法)DMD方法分析风电场流。我们发现,AP方法倾向于选择具有较小长度尺度和高频的模式,而SP方法选择低频的大型连贯结构。后者有些让人联想到使用正确的正交分解(POD)获得的模式。我们发现,相对有限的SP-DMD模式足以准确地重建整个风电场中的流场,而AP-DMD方法需要更多模式才能实现准确的重建。因此,就流场的重建而言,与AP-DMD方法相比,SP-DMD方法的性能损失较小。
High-fidelity large-eddy simulations are suitable to obtain insight into the complex flow dynamics in extended wind farms. In order to better understand these flow dynamics, we use dynamic mode decomposition (DMD) to analyze and reconstruct the flow field in large-scale numerically simulated wind farms by large-eddy simulations (LES). Different wind farm layouts are considered, and we find that a combination of horizontal and vertical staggering leads to improved wind farm performance compared to traditional horizontal staggering. We analyze the wind farm flows using the amplitude selection (AP) and sparsity-promoting (SP method) DMD approach. We find that the AP method tends to select modes with a small length scale and a high frequency, while the SP method selects large coherent structures with low frequency. The latter are somewhat reminiscent of modes obtained using proper orthogonal decomposition (POD). We find that a relatively limited number of SP-DMD modes is sufficient to accurately reconstruct the flow field in the entire wind farm, whereas the AP-DMD method requires more modes to achieve an accurate reconstruction. Thus, the SP-DMD method has a smaller performance loss compared to the AP-DMD method in terms of the reconstruction of the flow field.