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首页> 外文期刊>Experiments in Fluids: Experimental Methods and Their Applications to Fluid Flow >Lagrangian coherent structure identification using a Voronoi tessellation-based networking algorithm
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Lagrangian coherent structure identification using a Voronoi tessellation-based networking algorithm

机译:使用基于Voronoi细分的联网算法进行拉格朗日相干结构识别

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The quantification of Lagrangian coherent structures (LCS) has been investigated using an algorithm based on the tesselation of unstructured data points. The applicability of the algorithm in resolving an LCS was tested using a synthetically generated unsteady double-gyre flow and experimentally in a nominally two-dimensional free shear flow. The effects of two parameters on LCS identification were studied: the threshold track length used to quantify the LCS and resulting effective seeding density upon applying the threshold. At lower threshold track lengths, increases in the threshold track length resulted in finite-time Lyapunov exponent (FTLE) field convergence towards the expected LCS ridge of the double-gyre flow field at several effective seeding densities. However, at higher track lengths, further increases to the threshold track length failed to improve convergence at low effective seeding densities. The FTLE of the experimental data set was well-resolved using moderate threshold track lengths that achieved field convergence but maintained a sufficiently high seeding density. In contrast, the use of lower or higher track lengths produced an FTLE field characterized by an incoherent LCS ridge. From the analytical and experimental results, recommendations are made for future experiments for identifying LCS directly from unstructured data.
机译:拉格朗日相干结构(LCS)的量化已使用基于非结构化数据点的镶嵌的算法进行了研究。使用合成生成的非稳定双回转流并在名义上为二维自由剪切流中实验测试了该算法在解决LCS方面的适用性。研究了两个参数对LCS识别的影响:用于量化LCS的阈值轨道长度以及在应用阈值时产生的有效播种密度。在较低的阈值轨道长度处,阈值轨道长度的增加导致有限时间的Lyapunov指数(FTLE)场在几种有效播种密度下趋向双回转流场的预期LCS脊。然而,在较高的轨道长度处,进一步增加到阈值轨道长度并不能改善低有效播种密度下的会聚。实验数据集的FTLE使用中等阈值轨道长度得到了很好的解析,该轨道长度实现了田间收敛,但保持了足够高的播种密度。相反,使用较低或较高的磁道长度会产生一个FTLE场,其特征是LCS脊不连贯。从分析和实验结果中,建议为以后的实验提供建议,以便直接从非结构化数据中识别LCS。

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