The periodic flows, such as vortex shedding and rotating flow in turbomachinery, are very common in both scientific and engineering fields. However, high-fidelity numerical simulations of unsteady flows are usually time-consuming, particularly when varying flow parameters need to be considered. In this paper, a novel nonintrusive parametrized reduced order model (PROM) approach for prediction of periodic flows is presented. The establishment of this ROM is based on two techniques, proper orthogonal decomposition (POD) and discrete Fourier transform (DFT), where the first one can extract the spatial features and the second has the ability to quantify the temporal effects of parameters. A prediction model based on artificial neural networks (ANNs) is used to map the flow parameters with DFT coefficients. Flows past a cylinder and two dimensions turbine flows are used to demonstrate the effectiveness of the proposed PROM. It is shown that the proposed POD-DFT-ANN (PDA) ROM are both efficient and accurate for the predictions of periodic flows with varying flow parameters.

中文翻译：

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基于扩展真正交分解的周期流非侵入式参数化降阶模型

周期性流动，如涡轮机中的涡旋脱落和旋转流动，在科学和工程领域都很常见。然而，非定常流动的高保真数值模拟通常非常耗时，特别是在需要考虑变化的流动参数时。在本文中，提出了一种用于预测周期性流动的新型非侵入式参数化降阶模型 (PROM) 方法。该ROM的建立基于两种技术，适当的正交分解（POD）和离散傅里叶变换（DFT），其中第一个可以提取空间特征，第二个可以量化参数的时间效应。基于人工神经网络 (ANN) 的预测模型用于将流量参数与 DFT 系数进行映射。流过气缸和二维涡轮流用于证明所提出的 PROM 的有效性。结果表明，所提出的 POD-DFT-ANN (PDA) ROM 对于预测具有不同流动参数的周期性流动既有效又准确。