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arXiv 1902.08002physics.flu-dyn
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湍流混合的拉格朗日网络分析

Lagrangian network analysis of turbulent mixing

Giovanni Iacobello, Stefania Scarsoglio, J. G. M. Kuerten, Luca Ridolfi

摘要:

提出了一种基于时间复杂网络的方法,从拉格朗日的角度研究湍流混合。通过利用空间邻近准则,一组流体粒子的动力学被几何化为一个随时间变化的加权网络。具体而言,数值求解的湍流槽道流作为一个示例性的情况下。我们表明,随时间变化的网络是能够清楚地描述粒子群动力学,在参数鲁棒性和计算成本低廉的方式。网络形式主义能够直接识别瞬态和长期流态,湍流混合和平均流平流之间的相互作用,以及颗粒之间的邻近事件的发生。由于其多功能性和突出显着的流动特征的能力,复杂的网络代表了一个合适的工具,湍流混合的拉格朗日调查。复杂网络的应用为湍流的拉格朗日分析提供了强有力的资源,从而为湍流研究和网络科学之间的桥梁提供了进一步的步骤。

英文摘要:

A temporal complex network-based approach is proposed as a novel formulation to investigate turbulent mixing from a Lagrangian viewpoint. By exploiting a spatial proximity criterion, the dynamics of a set of fluid particles is geometrized into a time-varying weighted network. Specifically, a numerically solved turbulent channel flow is employed as an exemplifying case. We show that the time-varying network is able to clearly describe the particle swarm dynamics, in a parametrically robust and computationally inexpensive way. The network formalism enables to straightforwardly identify transient and long-term flow regimes, the interplay between turbulent mixing and mean flow advection, and the occurrence of proximity events among particles. Thanks to their versatility and ability to highlight significant flow features, complex networks represent a suitable tool for Lagrangian investigations of turbulence mixing. The present application of complex networks offers a powerful resource for Lagrangian analysis of turbulent flows, thus providing a further step in building bridges between turbulence research and network science.

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