Vectorizing Quantum Turbulence Vortex-Core Lines for Real-Time Visualization

TL;DR

This paper introduces a novel, efficient method for vectorizing vortex-core lines in quantum turbulence datasets, enabling real-time high-resolution visualization and analysis of complex vortex structures.

Contribution

The paper presents a new graph-based vectorization technique specifically designed for quantum fluids, preserving topology and reducing memory for real-time visualization.

Findings

Enables real-time visualization of quantum turbulence structures.

Preserves complex vortex topology during vectorization.

Significantly reduces memory consumption for high-resolution data.

Abstract

Vectorizing vortex-core lines is crucial for high-quality visualization and analysis of turbulence. While several techniques exist in the literature, they can only be applied to classical fluids. Recently, quantum fluids with turbulence get more and more attention in physics. It is thus desirable that vortex-core lines can also be well extracted and visualized for quantum fluids. In this paper, we aim for this goal and developed an efficient vortex-core line vectorization method for quantum fluids, which enables real-time visualization of high-resolution quantum turbulence structure. Given the datasets by simulation, our technique is developed from the vortices identified by the circulation-based method. To vectorize the vortex-core lines enclosed by those vortices, we propose a novel graph-based data structure, with iterative graph reduction and density-guided local optimization, to locate more precisely sub-grid-scale vortex-core line samples, which are then vectorized by continuous curves. This not only represents vortex-core line structures continuously, but also naturally preserves complex topology, such as branching during reconnection. By vectorization, the memory consumption can be largely reduced by orders of magnitude, enabling real-time rendering performance. Different types of interactive visualizations are demonstrated to show the effectiveness of our technique, which could assist further research on quantum turbulence.