In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
will defend his dissertation proposal
Flow Visualization and Analysis: From Geometry to Physics
As the size and complexity of the flow data sets continuously increase, many vector field visualization techniques are applied to generate an abstract representation of the geometric characteristics to addresses the complexity of flow data interpretation. However, most of the geometric based visualization techniques are lack of the ability to reveal the physically important features. Additional efforts are needed to interpret the physical characteristics from the geometric representation of the flow. In this proposal, we first introduce our Lagrangian accumulation framework, accumulating various local physical and geometric properties of the individual particles along the associated integral curves. This accumulation process provides us with a number of attribute fields that encode global information of the particle behaviors, which introduces an abstract representation of the flow data. We show how to utilize this framework to aid the classification of integral curves, produce texture-based visualization, study property transportation structure, and identify discontinuous behaviors among neighboring integral curves, respectively. However, the detailed flow behavior at individual integration points (and times) along the integral curves is compressed by this accumulation, leading to incomplete analysis and visualization of flow data. In order to achieve a more detailed exploration, we are investigating a new flow exploration framework directly based on the time-series data or Time Activity Curves (TAC) of local properties. In this framework, the physical behavior of the individual particles can be described via their respective TACs.
We introduce an event detector based on multi-TACs to capture the local and global similarity of any spatial point with its neighboring points with a new dissimilarity metric. A hierarchical clustering framework can then be developed based on this metric, upon which a level-of-detail representation of the flow can be obtained. We also plan to investigate the correlation among different attributes and specifically, the correlation among different descriptors for vortex detection. We will apply our new framework to a number of 2D and 3D unsteady flow data sets to demonstrate its effectiveness.
Date: Monday, October 17, 2016
Time: 11:00 AM
Place: PGH 550
Advisor: Dr. Guoning Chen
Faculty, students, and the general public are invited.