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Implications of the selection of a particular modal decomposition technique for the analysis of shallow flows

机译:选择一种特殊的模态分解技术对浅水流分析的意义

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摘要

This work deals with the capabilities of two synoptic modal decomposition techniques for the identification of the spatial patterns and temporal dynamics of coherent structures in shallow flows. Using two different experimental datasets it is shown that due to the linear behaviour of large-scale, quasi-two-dimensional flow structures, there are almost no differences in the identification of dominant modes between the results obtained from a traditional proper orthogonal decomposition and the more recently developed dynamic mode decomposition. However, it is also shown that nonlinear dynamics can arise in the transition of these structures to a quasi-two-dimensional behaviour, which can result in the proper orthogonal decomposition identifying structures composed of multi-frequencies, a sign of a convoluted dynamics. Thus dynamic mode decomposition is recommended instead for the analysis of such phenomena. In addition, this paper introduces a simple ranking methodology for the use of the dynamic mode decomposition technique in shallow flows, which is based on the results of the proper orthogonal decomposition.
机译:这项工作处理两种天气模式分解技术的能力,用于识别浅流中相干结构的空间模式和时间动态。使用两个不同的实验数据集表明,由于大规模准二维流动结构的线性行为,在从传统适当的正交分解获得的结果与线性正交分析得到的显性模态之间几乎没有区别。最近开发的动态模式分解。但是,还表明,在这些结构向准二维行为的过渡中可能会出现非线性动力学,这可能会导致识别由多频率组成的适当正交分解结构,这是复杂的动力学现象。因此,建议使用动态模式分解来分析此类现象。此外,本文介绍了一种基于浅层流动态模式分解技术的简单排序方法,该方法基于适当的正交分解结果。

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