基于稀疏增强动力学模态分解的风力机尾流模型研究

张虎; 许昌; 魏赏赏; 霍志红; 韩星星; 薛飞飞

doi:10.19912/j.0254-0096.tynxb.2023-0406

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (7) : 681-690. DOI: 10.19912/j.0254-0096.tynxb.2023-0406

基于稀疏增强动力学模态分解的风力机尾流模型研究

张虎, 许昌, 魏赏赏, 霍志红, 韩星星, 薛飞飞

作者信息 +

STUDY ON WAKE MODEL OF WIND TURBINE BASED ON SPARSITY PROMOTING DYNAMIC MODE DECOMPOSITION

Zhang Hu, Xu Chang, Wei Shangshang, Huo Zhihong, Han Xingxing, Xue Feifei

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文章历史 +

摘要

基于稀疏增强动力学模态分解（SPDMD）方法对风力机尾流大涡模拟（LES）结果开展降阶模型研究,并将分解结果与标准DMD方法进行比较。结果表明,动力学模态分解方法能提取尾流动态特征,揭示风力机尾流演化规律。标准DMD方法倾向于选择具有小尺度和高频率的模态,而SPDMD方法选择具有低频率的大尺度流动特征。相比于标准DMD方法,SPDMD方法在低维子空间上建立风力机非定常尾流场的降阶模型,以较少的模态数目重构和预测风力机尾流场,可提高计算效率。

Abstract

This study investigates the reduced-order model of wind turbine wakes based on the sparsity promoting dynamic mode decomposition （SPDMD） method using large eddy simulation （LES） results. A comparison is made between the decomposition outcomes obtained from SPDMD and the standard DMD method. The findings demonstrate that the dynamic mode decomposition approach can extract the dynamic characteristics of wake flow, thereby revealing the underlying evolution patterns of wind turbine wakes. The standard DMD method tends to prioritize modes with small scales and high frequencies, whereas the SPDMD method selects large-scale flow features with low frequencies. In comparison to the standard DMD method, the SPDMD approach constructs a reduced-order model of wind turbine unsteady wake fields in a lower-dimensional subspace. By utilizing a smaller number of modes, the SPDMD method improves computational efficiency in the reconstruction and prediction of wind turbine wake fields.

导出引用

张虎, 许昌, 魏赏赏, 霍志红, 韩星星, 薛飞飞. 基于稀疏增强动力学模态分解的风力机尾流模型研究[J]. 太阳能学报. 2024, 45(7): 681-690 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0406

Zhang Hu, Xu Chang, Wei Shangshang, Huo Zhihong, Han Xingxing, Xue Feifei. STUDY ON WAKE MODEL OF WIND TURBINE BASED ON SPARSITY PROMOTING DYNAMIC MODE DECOMPOSITION[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 681-690 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0406

中图分类号： TK89

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