On the role of domain-specific knowledge in the visualization of technical flows

In this paper, we present an overview of a number of existing flow visualization methods, developed by the authors in the recent past, that are specifically aimed at integrating and leveraging domain-specific knowledge into the visualization process. These methods transcend the traditional divide between interactive exploration and featurebased schemes and allow a visualization user to benefit from the abstraction properties of feature extraction and topological methods while retaining intuitive and interactive control over the visual analysis process, as we demonstrate on a number of examples

Visualization of intricate flow structures for vortex breakdown analysis

Journal ArticleVortex breakdowns and flow recirculation are essential phenomena in aeronautics where they appear as a limiting factor in the design of modern aircrafts. Because of the inherent intricacy of these features, standard flow visualization techniques typically yield cluttered depictions. The paper addresses the challenges raised by the visual exploration and validation of two CFD simulations involving vortex breakdown. To permit accurate and insightful visualization we propose a new approach that unfolds the geometry of the breakdown region by letting a plane travel through the structure along a curve. We track the continuous evolution of the associated projected vector field using the theoretical framework of parametric topology. To improve the understanding of the spatial relationship between the resulting curves and lines we use direct volume rendering and multi-dimensional transfer functions for the display of flow-derived scalar quantities. This enriches the visualization and provides an intuitive context for the extracted topological information. Our results offer clear, synthetic depictions that permit new insight into the structural properties of vortex breakdowns

Experimental study of turbulence past thin tabs

The turbulence past thin tabs of rectangular, triangular, and ellipsoidal geometries is experimentally investigated using particle image velocimetry at Reynolds number Re=2000 and Re=12900 based on the tabs height and freestream velocity. Turbulence statistics including streamwise velocity, turbulent kinetic energy (TKE), and vortex swirling strength are obtained in the symmetry plane of each tab. The results show a strong dependency of the tab geometry on the strength of the induced vortical structures at the low Re. The turbulent flow regime at high Re promotes high shear stress and TKE production right past the tabs, where the rectangular tab induces greater mixing. The spanwise vortices from the rectangular tab have an average vortex cores radius 20% larger and nearly 30% greater circulation as compared to those from the ellipsoidal tab. Compared to the ones produced by the triangular tab, the core radius is 25% larger and the circulation is almost 50% higher. Our results also show, for the rst time, that hairpin structures are possible in triangular tabs. Finally, we conclude that geometrical singularity plays an important role in the strength of the primary vortical structures shed by the tabs

Detection problems of vortical structures

Paper presented at the 8th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Mauritius, 11-13 July, 2011.Main conceptual problems faced in detection of vortical structures are dealt with and discussed on the background of a brief review of existing vortex-identification schemes.mp201

Vortex Characterization for Engineering Applications

Realistic engineering simulation data often have features that are not optimally resolved due to practical limitations on mesh resolution. To be useful to application engineers, vortex characterization techniques must be sufficiently robust to handle realistic data with complex vortex topologies. In this paper, we present enhancements to the vortex topology identification component of an existing vortex characterization algorithm. The modified techniques are demonstrated by application to three realistic data sets that illustrate the strengths and weaknesses of our approach