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

并行注入混合湍流通道中上层建筑的 DNS 和可视化分析

• 批准号: 429361363
• 负责人: Professor Dr.-Ing. Holger Theisel
• 金额: --
• 依托单位: Institut für Simulation und Graphik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/429361363?language=en
• 关键词: DNS; Visual; Analysis; Superstructures; Turbulent

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.

为了分析上层结构的发生和影响，在高雷诺数字的通道中进行了渗透的混合，并平行注射，直接数值模拟（DNS）的结合，涡流定义和识别以及基于特征的可视化。标准的，现成的解决方案不适合thispurpose.concerning DNS，核心问题是访问HPC系统上出色效率的高雷诺数。此外，必须包括适用的临时模型以描述与混合相关的所有流体特性。涡流提取的主要挑战是三倍。首先，高强度湍流不包括基于流衍生物的局部分析的标准涡旋定义。取而代之的是，必须使用全局，拉格朗日或层次涡流定义定义，这些定义是在过滤流图上而不是速度范围内过滤操作上的。其次，涡旋定义和参数调整必须串珠，以使其不关注上游涡旋插入注入，而要解决下游不太明显的，较不稳定的涡旋结构。第三，就视觉分析而言，主要挑战与数据集的巨大大小相关联：DNS通常提供在模拟过程中无法完全存储的数据集。因此，必须进行视觉分析的直播解决方案。要分析现象，DN，涡流提取和可视化的现象，将其组合成计算转向意义上的反馈周期。多尺度的POD以及自动涡流提取的多尺度启动以及后来的既有视觉上的均可进行的互动，则允许进行互动，并进行了互动的互动。进一步的模拟参数。 DNS，POD和视觉分析的这种有效组合应允许识别上层建筑，并有助于解释其对运输过程的影响。