Graduation Semester and Year

Spring 2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Mathematics

Department

Mathematics

First Advisor

Dr. Chaoqun Liu

Abstract

Vortex dynamics govern critical processes in turbulent flows, yet conventional identification methods struggle to resolve individual structures' morphology and evolutionary pathways. Detecting and extracting vortex structures by objective algorithms may lead to a better understanding of turbulence regeneration dynamics through the automated and quantitative assessment of the structures. Although there are many vortex-identification criteria available, they pinpoint only the spatial regions belonging to vortices, with no information on the specific identity, topology, and shape of individual vortices. In this dissertation, we develop a new scheme based on Vortex axis tracking by iterative propagation (VATIP), called modified VATIP algorithm, for three-dimensional vortex axis-line tracking and analysis in wall-bounded turbulent flows. This new algorithm employs the Liutex magnitude $R$, a rigorous rotation strength metric, to guide axis-line propagation through three-dimensional velocity fields. This alternative method makes a significant improvement over some of the weaknesses of the original method, thus resulting in a better physical and accurate representation of vortex structures. Moreover, we introduce an advanced version of the predictor-corrector vortex identification method, called the modified PCM. The idea revolves around replacing pressure and vorticity variables by Liutex magnitude $R$ and the real eigenvector $\vec r$, respectively. Our approach enables enhanced isolation of the rigid rotational components of the flow field in complex geometries. This methodological advancement allows for more accurate characterization of vortex core dynamics, along with numerically inherent artifacts, which will be proven further in theory and computation.

License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.