YUAN, YUYANG ORCID: https://orcid.org/0000-0002-0754-9149 (2024) The Analysis of the Solar Vortex on the Solar Atmosphere. PhD thesis, University of Sheffield.
Abstract
Ubiquitous vortical structures are considered to act as a natural source of various solar plasma phenomena, e.g., a wide range of magnetohydrodynamic (MHD) waves and jet excitation. This work aims to develop an advanced vortex detection algorithm based on the $\Gamma$ method and using a separable convolution kernel technique. This method is applied to detect and analyze the photospheric vortices in 3D realistic magnetoconvection numerical and observational data. We present the advanced $\Gamma$ Method (AGM), and our results indicate that the AGM performs with better accuracy in comparison with the original $\Gamma$ method. The AGM allows us to identify small and large-scale vortices with no vortex interposition without requiring changing the threshold. Thereby, the nondetection issue is mostly prevented. It was found that the $\Gamma$ method failed to identify the large and longer-lived vortices, which were detected by the AGM. The size of the detected vortical structures tends to vary over time, with most vortices shrinking towards their end. The vorticity at the center is also not constant, presenting a sharp decay as the vortex ceases to exist. Due to its capability of identifying vortices with minimum nondetection, the vortices' properties--such as lifetime, geometry, and dynamics--are better captured by the AGM than the $\Gamma$ method. In this era of new high-resolution observation, the AGM can be used as a precise technique for identifying and performing statistical analysis of solar atmospheric vortices.
Next, this Thesis introduces a novel vortex analysis method, that is, the discrete Fréchet distance vortex visualization method (DFDVVM). The DFDVVM is developed to analyze the time-dependent behavior of vortices while quantifying each detected vortex's vorticity evolution in a high-dimensional space (the DFD space). We also developed a vortex clustering algorithm for vortices and clustered them into different clusters. As a result, each cluster's average vorticity pattern is calculated, and each cluster's statistical analysis is presented.
Metadata
Supervisors: | Verth, Gary and Fedun, Viktor |
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Related URLs: | |
Awarding institution: | University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Mr YUYANG YUAN |
Date Deposited: | 20 Feb 2024 09:25 |
Last Modified: | 20 Feb 2024 09:25 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:34328 |
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