Date Thesis Awarded
5-2024
Access Type
Honors Thesis -- Open Access
Degree Name
Bachelors of Science (BS)
Department
Physics
Advisor
Saskia Mordijck
Committee Members
Sarah Day
David Armstrong
Abstract
Cross-field transport and heat loss in a magnetically confined plasma is determined by turbulence driven by perpendicular (to the magnetic field) pressure gradients. The heat losses from turbulence can make it difficult to maintain the energy density required to reach and maintain the conditions necessary for fusion. Self-organization of turbulence into intermediate scale so-called zonal flows can reduce the radial heat losses, however identifying when the transition occurs and any precursors to the transition is still a challenge. Topological Data Analysis (TDA) is a mathematical method which is used to extract topological features from point cloud and digital data to develop a methodology to identify the transition from turbulent dominant to zonal flow dominant behavior. When expanding this approach to experimental observations, certain topological methods are susceptible to noise, which can appear as small scale topological features and crowd out legitimate topology. We explore techniques to mitigate the effects of noise in the use of TDA on plasma data, and to demonstrate methodology that is able to identify transitions despite a high noise-to-signal ratio. In this thesis, we will focus on developing mathematical models to test the efficacy of different smoothing algorithms on reestablishing topological structure lost in modeled noisy data, as well as show that it is possible capture the transition to self-organized flows in the presence of a high noise-to-signal ratio without first using processing to approximate the pre-noise image. Finally, we apply the methodology to experimental image data to capture turbulence transitions.
Recommended Citation
Kiewel, Julius, "Identifying Transitions in Plasma with Topological Data Analysis of Noisy Turbulence" (2024). Undergraduate Honors Theses. William & Mary. Paper 2233.
https://scholarworks.wm.edu/honorstheses/2233
