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Hairpin vortices are one of the most important vortical structures in turbulent flows. Extracting and characterizing hairpin vortices provides useful insight into many behaviors in turbulent flows. However, hairpin vortices have complex configurations and might be entangled with other vortices, making their extraction difficult. In this work, we introduce a framework to extract and separate hairpin vortices in shear-driven turbulent flows for their study. Our method first extracts general vortical regions with a region-growing strategy based on certain vortex criteria (e.g., $$\lambda_2$$) and then separates those vortices with the help of progressive extraction of ($$\lambda_2$$) iso-surfaces in a top-down fashion. This leads to a hierarchical tree representing the spatial proximity and merging relation of vortices. After separating individual vortices, their shape and orientation information is extracted. Candidate hairpin vortices are identified based on their shape and orientation information as well as their physical characteristics. An interactive visualization system is developed to aid the exploration, classification, and analysis of hairpin vortices based on their geometric and physical attributes. We also present additional use cases of the proposed system for the analysis and study of general vortices in other types of flows. 

Microvessels are frequent targets for research into tissue development and disease progression. These complex and subtle differences between networks are currently difficult to visualize, making sample comparisons subjective and difficult to quantify. These challenges are due to the structure of microvascular networks, which are sparse but space-filling. This results in a complex and interconnected mesh that is difficult to represent and impractical to interpret using conventional visualization techniques. We develop a bi-modal visualization framework, leveraging graph-based and geometry-based techniques to achieve interactive visualization of microvascular networks. This framework allows researchers to objectively interpret the complex and subtle variations that arise when comparing microvascular networks. 

Unsupervised clustering techniques have been widely applied to flow simulation data to alleviate clutter and occlusion in the resulting visualization. However, there is an absence of systematic guidelines for users to evaluate (both quantitatively and visually) the appropriate clustering technique and similarity measures for streamline and pathline curves. In this work, we provide an overview of a number of prevailing curve clustering techniques. We then perform a comprehensive experimental study to qualitatively and quantitatively compare these clustering techniques coupled with popular similarity measures used in the flow visualization literature. Based on our experimental results, we derive empirical guidelines for selecting the appropriate clustering technique and similarity measure given the requirements of the visualization task. We believe our work will inform the task of generating meaningful reduced representations for large-scale flow data and inspire the continuous investigation of a more refined guidance on clustering technique selection. 

This work proposes to analyze the time-dependent characteristics of the physical attributes measured along pathlines derived from unsteady flows, which can be represented as a series of time activity curves (TAC). A new TAC-based unsteady flow visualization and analysis framework is proposed. The center of this framework is a new event-based distance metric (EDM) that compares the similarity of two TACs, from which a new spatio-temporal, hierarchical clustering of pathlines based on their physical attributes and an attribute-based pathline exploration are proposed. These techniques are integrated into a visual analytics system, which has been applied to a number of unsteady flow in 2D and 3D to demonstrate its utility.