DNS and Visual Analysis of Superstructures in Turbulent Channels with Mixing by Parallel Injection

为了分析高雷诺数下平行喷射通道中超结构的发生及其对湍流混合的影响，提出了一种直接数值模拟（DNS）、涡定义与识别以及基于特征的可视化相结合的方法。对于DNS，核心问题是在HPC系统上以优异的效率访问高雷诺数。此外，必须包括合适的模型来数值描述与混合相关的所有流体性质。首先，高强度湍流排除了基于流动导数局部分析的标准涡流定义。相反，全球，拉格朗日，或层次涡的定义是必要的，是基于过滤操作的流量图，而不是速度场。其次，涡的定义和参数调整，使其不侧重于上游涡接近注射，但处理不太明显，噪音和更不稳定的涡结构下游。第三，在可视化分析方面，主要的挑战与数据集的庞大规模有关：DNS通常提供无法在模拟期间完全存储的数据集。因此，在飞行中的解决方案的可视化分析是必要的。为了分析的现象，DNS，涡提取和可视化havetoe结合成一个反馈循环，在一个计算的指导意义上。而多尺度POD沿着与自动涡提取进行on-the-fly，所产生的涡后来在一个交互式的方式进行可视化分析，允许调整的可视化参数和进一步的simulationparameters。这种DNS、POD和视觉分析的有效结合将允许识别上层建筑，并有助于解释其对运输过程的影响。

In order to analyze the occurrence and the impact of superstructures onturbulent mixing in channels at high Reynolds numbers with a parallel injection,a combination of Direct Numerical Simulation (DNS), vortexdefinition and identification, and feature-based visualization, isproposed. Standard, off-the-shelf solutions are not available for thispurpose.Concerning DNS, the central issue is to access high Reynolds numberswith excellent efficiency on HPC systems. Additionally, suitablemodels must be included to describe numerically all fluidproperties relevant for mixing.The main challenge in vortex extraction is three-fold. Firstly,high-intensity turbulence excludes standard vortex definitions that arebased on a local analysis of the flow derivatives. Instead, global,Lagrangian, or hierarchical vortex definitions are necessary that arebased on filtering operations on the flow map instead of the velocityfield. Secondly, vortex definitions and parameter tuning has to beadapted such that it does not focus on upstream vorticesclose to injection but tackles the less obvious, noisierand more unsteady vortex structures downstream. Thirdly, in terms ofvisual analysis, the main challenge is associated with the sheer size ofthe data sets: DNS typically delivers data sets that cannot becompletely stored during the simulation. Hence, on-the-fly solutions forthe visual analysis are necessary.To analyze the phenomena, DNS, vortex extraction and visualization haveto be combined into a feedback cycle in a computational steering sense.While a multi-scale POD along with an automatic vortex extraction is carried outon-the-fly, the resulting vortices are later visually analyzed in an interactive manner,allowing adaptation of both the visualization parameters and further simulationparameters. This efficient combination of DNS, POD, and visual analysis shall allow the identification of superstructures and help explain their impact on transport processes.