Sparsity Promoting Dynamic Mode Decomposition for Data-Driven Modeling of Wind Turbine Wake

作者：Zhang Hu, Xu Chang, Shangshang Wei, Zhiwen Deng, Zhixiong Jiao · 发表于：Journal of Physics Conference Series · 年份：2023 · DOI：10.1088/1742-6596/2474/1/012028 · 被引用次数：6 · 研究领域：Fluid Dynamics and Vibration Analysis、Model Reduction and Neural Networks、Wind and Air Flow Studies

Abstract High-fidelity numerical simulation is suitable for analyzing the complex unsteady flow field dynamics of wind turbines. For a better understanding of these flow characteristics, the dynamic mode decomposition method can be used to carry out a reduced-order model study on the wakefield of wind turbines based on large-eddy simulations (LES) numerical simulation. In this paper, we abstract material dynamic information from the wakefield of the wind turbine by applying the sparsity-promoting dynamic mode decomposition (SPDMD) method, and the decomposition results are contrasted with the standard dynamic mode decomposition (DMD) method. Indicated that both mode decomposition methods can abstract the dynamic characteristics of wake and reveal the development and variation law of wind turbine wake. However, the frequency and spatial structure of the selected modes are different. For the purpose of demonstrating the extraction impact of the DMD/SPDMD method on the wakefield of wind turbines, DMD/SPDMD reduced-order models are established respectively. The result indicated that the relatively limited number of SPDMD modes is adequate to validly rehabilitate the wakefield of the unabridged wind turbine while standard DMD methods prerequisite more decomposition modes. Therefore, compared with the standard DMD method, the SPDMD method has strong robustness in mode selection, eliminates the feature information that contributes weakly to the flow, and has a smaller performance los...