In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
will defend his proposal
Multi-Scale Coherent Structure Extraction for Flow Analysis
AbstractCoherent 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 complex visual representation of these structures, significantly reducing the efficiency of the domain experts workflows for discovering the flow physics, when they need to spend a 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 analyzing various flow behaviors, especially coherent structures in the flow, this work proposes (1) a method to encode relevant physics into the geometric representation, and (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). By analyzing the time-dependent characteristics of the physical attributes measured along pathlines which can be represented as a series of time activity curves (TAC), we demonstrate that the temporal trends of these TACs can convey the relation between pathlines and certain well-known flow features (e.g., vortices and shearing layers), which enables us to select pathlines that can effectively represent the physical characteristics of interest and their temporal behavior in the fluid flow. Since a single physical attribute cannot help to differentiate levels of scales for coherent structures, we utilize the feature level-set to combine multiple physical attributes, and use it as a filter to separate large and small scale structures. To visualize these structures, we apply the iso-surface generation on the kernel density estimation of the distance field from the feature level-set. The proposed methods successfully reveal the detailed structure of vortices, the relation between shear layer and vortex formation, and vortex breakdown, which are difficult to convey with conventional methods.
Date: Thursday, March 19, 2020
Time: 2:30 - 4:00 PM
Place: Online Presentation - https://join.skype.com/K0AvM9f14WU9
Advisors: Dr. Guoning Chen
Faculty, students, and the general public are invited.