Utilizing topology based domain segmentation for in-situ identification and classification of vortices in simulations of turbulent flows

Abstract

Fully turbulent flow fields are populated with features such as vortices or eddies. Analysing flow features and their interaction can lead to insight into the physics of turbulence. An enhanced understanding of turbulence benefits, for example, the development of turbulence models, which can help dramatically reducing the computational costs of turbulent flow simulations. The vast abundance of features in fully turbulent flow fields demands an automated identification and analysis process. To address this issue Tracer was developed, an in-situ software framework to extract flow features from data produced by high-fidelity unsteady computational fluid dynamics (CFD) simulations conducted on GPU systems. Unsteady CFD simulations can produce a significant amount of data. The majority of this data cannot be accessed following the classical paradigm of storing the flow field to disk due to the bottleneck of writing the data disk and limitations of available storage capacity. To avoid the restrictions related to moving and storing data, Tracer is able to run concurrently with the simulation, analysing the data while it is still on the GPU's system memory. The intensities of flow features generally span multiple orders of magnitude making their identification a challenging task. Contrary to the classic approach of extracting features by defining a global threshold of a scalar $f$, Tracer identifies an individual threshold for each feature based on the topology of $f$, facilitating the identification of features over a wide range of scales and intensities. The individual thresholds are identified using a join tree, which tracks the topology of superlevel sets of $f$. A performance analysis showed that Tracer requires less system memory than the standalone CFD solver. Adding Tracer in-situ to a CFD simulation hence comes with a reasonable increase in system memory usage. The increase in runtime depends on the number of time steps that the CFD solver takes between two applications of Tracer to the flow field, but is typically within the single-digit percentage range. The advantages of topology based feature identification using Tracer versus the classic approach of using a global threshold are demonstrated qualitatively by visualising vortices in flow around an SD7003 aerofoil. The application of Tracer is demonstrated on the simulation of the turbulent transition of a Taylor-Green vortex with the aim of counting the number of vortices over time. If vortices are extracted based on the topolgy of the $Q$-criterion field a steep increase in the number of vortices can be observed during turbulent breakdown and a decrease during the decay of turbulence. Additionally a feature based analysis of turbulent channel flows up to $Re_\tau = 550$ was conducted examining the topology and the geometry of vortices. While the topological organisation of individual vortices in the near-wall and in the central region are found to be indistinguishable, there is a difference when vortex clusters are extracted. It was also found that vortices less than 70 wall units away from the wall tend to align in the streamwise direction; vortices further away from the wall were found to be geometrically isotropic. These results furthermore support the assumption that the diameter of elongated vortices scales with the Kolmogorov length.Open Acces