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Identifying vortical network connectors for turbulent flow modification

机译:识别湍流流动修改的涡流网络连接器

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We introduce a network (graph) theoretic community-based framework to extract vortical structures that serve the role of connectors for the vortical interactions in two- and three-dimensional isotropic turbulence. The present framework represents the vortical interactions on a network, where the vortical elements are viewed as the nodes and the vortical interactions are regarded as edges weighted by induced velocity. We identify closely interacting vortical elements as vortical network communities through community detection algorithms. The inter- and intra-community interactions are used to identify the communities which have the strongest and weakest interactions amongst them. These vortical communities are referred to as the connector and peripheral communities, respectively. We demonstrate the influence of the network-based structures to modify the dynamics of a collection of discrete point vortices. Taking advantage of the strong inter-community interactions, connector community can significantly modify the collective dynamics of vortices through the application of multiple impulse perturbations. We then apply the community-based framework to extract influential structures in isotropic turbulence. The connector and peripheral communities extracted from turbulent flows resemble shear-layer and vortex-core-like structures, respectively. The influence of the connector structures on the flow field and their neighbouring vortical structures is analysed by adding impulse perturbations to the connectors in direct numerical simulations. The findings are compared with the cases of perturbing the strongest vortex tube and shear-layer regions. We reveal that perturbing the connector structures enhances local turbulent mixing beyond what is achieved by the other cases.
机译:我们引入了一个基于网络(图)理论社区的框架来提取旋涡结构,这些结构在二维和三维各向同性湍流中充当旋涡相互作用的连接件。本框架表示网络上的涡相互作用,其中涡元素被视为节点,涡相互作用被视为由诱导速度加权的边。我们通过社区检测算法将紧密交互的漩涡元素识别为漩涡网络社区。社区间和社区内的互动用于确定社区之间互动最强和最弱的社区。这些旋涡群落分别被称为连接体和外围群落。我们展示了基于网络的结构对修改离散点涡集合动力学的影响。利用强大的群体间相互作用,连接器群体可以通过应用多个脉冲扰动,显著改变漩涡的集体动力学。然后,我们应用基于社区的框架来提取各向同性湍流中有影响的结构。从湍流中提取的连接体和外围群落分别类似于剪切层和涡核结构。在直接数值模拟中,通过在连接件上添加脉冲扰动,分析了连接件结构对流场及其附近旋涡结构的影响。研究结果与扰动最强涡管和剪切层区域的情况进行了比较。我们发现,与其他情况相比,扰动连接结构会增强局部湍流混合。

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