Abstract Due to the damage caused by stall flutter, the investigation and modeling of the flow over a wind turbine airfoil at high angles of attack are essential. Dynamic mode decomposition (DMD) and dynamic mode decomposition with control (DMDc) are used to analyze unsteady flow and identify the intrinsic dynamics. The DMDc algorithm is found to have an identification problem when the spatial dimension of the training data is larger than the number of snapshots. IDMDc, a variant algorithm based on reduced dimension data, is introduced to identify the precise intrinsic dynamics. DMD, DMDc and IDMDc are all used to decompose the data for unsteady flow over the S809 airfoil that are obtained by numerical simulations. The DMD results show that the dominant feature of a static airfoil is the adjacent shedding vortices in the wake. For an oscillating airfoil, the DMDc results may fail to consider the effect of the input and have an identification problem. IDMDc can alleviate this problem. The dominant IDMDc modes show that the intrinsic flow for the oscillating case is similar to the unsteady flow over the static airfoil. Moreover, the input–output model identified by IDMDc can give better predictions for different oscillating cases than the identified DMDc model.

中文翻译：

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基于动态模态分解与控制的大攻角机翼非定常流动输入输出降阶建模

摘要 由于失速颤振会造成损坏，因此对大迎角下风轮机翼面流动的研究和建模至关重要。动态模式分解 (DMD) 和带控制的动态模式分解 (DMDc) 用于分析非定常流动并识别内在动力学。当训练数据的空间维度大于快照数量时，发现DMDc算法存在识别问题。IDMDc 是一种基于降维数据的变体算法，用于识别精确的内在动态。DMD、DMDc 和IDMDc 均用于分解数值模拟得到的S809 翼型非定常流动数据。DMD 结果表明静态翼型的主要特征是尾流中相邻的脱落涡。对于振荡翼型，DMDc 结果可能无法考虑输入的影响并存在识别问题。IDMDc 可以缓解这个问题。主要的 IDMDc 模式表明，振荡情况下的固有流动类似于静态翼型上的不稳定流动。此外，与已识别的 DMDc 模型相比，IDMDc 识别的输入-输出模型可以对不同的振荡情况提供更好的预测。