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[Defense] Multi-scale Coherent Structure Extraction and Visualization for Flow Analysis

Friday, October 23, 2020

3:00 pm - 4:30 pm

In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
Duong Nguyen
will defend his dissertation
Multi-scale Coherent Structure Extraction and Visualization for Flow Analysis


Coherent structures are important features in fluid flows. A better understanding of the physics of coherent structures will help explain a diverse range of physical phenomena and help improve our capability of modeling complex turbulence flows, such as those often seen in combustion, chemical reaction and heat transfer. However, due to their multi-scale nature and non-unified characterizations, extraction and separation of coherent structures remains a challenging task. This is further complicated by the overly complicated visual representation of these structures, significantly reducing the efficiency of the domain experts workflows for discovering the flow physics when they need to spend considerable amount of time and effort to read the complex charts/graphs/geometries. In addition, the physical behaviors of flow that experts care about are not reliably conveyed in the visualizations due to the predominant focus on the geometric characteristics of the flow data.

In order to support domain experts in the analyzing various flow behaviors, especially coherent structures in the flow, this work proposes (1) a method to encode relevant physics into the geometric representation, (2) a pipeline to extract and visualize multi-scale coherent structures for the turbulent fluid motion created between two concentric and independently rotating cylinders called Taylor-Couette (TC), and (3) a threshold-free method, for large-scale structure extraction in shear flows. The proposed methods successfully reveal coherent structures in different scales and their relations, which are difficult to convey with conventional methods

 Friday, October 23, 2020
3:00PM - 4:30PM
Online via MS Teams

Dr. Guoning Chen, dissertation advisor

Faculty, students and the general public are invited.

Online via MS Teams