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

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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.