A Visualization Framework for Multi-scale Coherent Structures in Taylor-Couette Turbulence

Duong Nguyen, Rodolfo Ostilla Monico, Guoning Chen

View presentation: 2020-10-28T17:00:00Z GMT-0600 Change your timezone on the schedule page
2020-10-28T17:00:00Z
Exemplar figure
Taylor-Couette turbulent flow (TCF) is the fluid motion between two coaxial, independently rotating cylinders. TCF has become an important model system in fluid dynamics as it helps to understand the development of hydrodynamic stabilities and pattern formation. To support domain experts in the analysis of TCF, we propose a first 3D visualization framework that enables the clear separation of large- and small- scale coherent structures for TCF. The proposed method successfully reveals 3D large-scale coherent structures of TCF with different control parameter settings, which are difficult to achieve with the conventional methods.
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Direct link to video on YouTube: https://youtu.be/Al7uuUlkCa4

Keywords

Flow visualization, Taylor-Couette turbulence, coherent structures

Abstract

Taylor-Couette flow (TCF) is the turbulent fluid motion created between two concentric and independently rotating cylinders. It has been heavily researched in fluid mechanics thanks to the various nonlinear dynamical phenomena that are exhibited in the flow. As many dense coherent structures overlap each other in TCF, it is challenging to isolate and visualize them, especially when the cylinder rotation ratio is changing. Previous approaches rely on 2D cross sections to study TCF due to its simplicity, which cannot provide the complete information of TCF. In the meantime, standard visualization techniques, such as volume rendering/iso-surfacing of certain attributes and the placement of integral curves/surfaces, usually produce cluttered visualization. To address this challenge and to support domain experts in the analysis of TCF, we developed a visualization framework to separate large-scale structures from the dense, small-scale structures and provide an effective visual representation of these structures. Instead of using a single physical attribute as the standard approach which cannot efficiently separate structures in different scales for TCF, we adapt the feature level-set method to combine multiple attributes and use them as a filter to separate large- and small-scale structures. To visualize these structures, we apply the iso-surface extraction on the kernel density estimate of the distance field generated from the feature level-set. The proposed methods successfully reveal 3D large-scale coherent structures of TCF with different control parameter settings, which are difficult to achieve with the conventional methods.