Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137143
Title: Towards high-quality visualization of superfluid vortices
Authors: Guo, Yulong
Liu, Xiaopei
Xiong, Chi
Xu, Xuemiao
Fu, Chi-Wing
Keywords: Science::Physics
Issue Date: 2017
Source: Guo, Y., Liu, X., Xiong, C., Xu, X., & Fu, C.-W. (2018). Towards high-quality visualization of superfluid vortices. IEEE transactions on visualization and computer graphics, 24(8), 2440-2455. doi:10.1109/TVCG.2017.2719684
Journal: IEEE transactions on visualization and computer graphics
Abstract: Superfluidity is a special state of matter exhibiting macroscopic quantum phenomena and acting like a fluid with zero viscosity. In such a state, superfluid vortices exist as phase singularities of the model equation with unique distributions. This paper presents novel techniques to aid the visual understanding of superfluid vortices based on the state-of-the-art non-linear Klein-Gordon equation, which evolves a complex scalar field, giving rise to special vortex lattice/ring structures with dynamic vortex formation, reconnection, and Kelvin waves, etc. By formulating a numerical model with theoretical physicists in superfluid research, we obtain high-quality superfluid flow data sets without noise-like waves, suitable for vortex visualization. By further exploring superfluid vortex properties, we develop a new vortex identification and visualization method: a novel mechanism with velocity circulation to overcome phase singularity and an orthogonal-plane strategy to avoid ambiguity. Hence, our visualizations can help reveal various superfluid vortex structures and enable domain experts for related visual analysis, such as the steady vortex lattice/ring structures, dynamic vortex string interactions with reconnections and energy radiations, where the famous Kelvin waves and decaying vortex tangle were clearly observed. These visualizations have assisted physicists to verify the superfluid model, and further explore its dynamic behavior more intuitively.
URI: https://hdl.handle.net/10356/137143
ISSN: 1077-2626
DOI: 10.1109/TVCG.2017.2719684
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TVCG.2017.2719684.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IAS Journal Articles

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