Feature Curves and Surfaces of 3D Asymmetric Tensor Fields

TL;DR

This paper introduces new topological features and visualization techniques for 3D asymmetric tensor fields, addressing their complex eigenvalues and stability, with applications in fluid dynamics and solid mechanics.

Contribution

It presents six topological surfaces, a topological curve, and a novel eigenvalue space for 3D asymmetric tensor fields, along with methods for extracting quadratic surfaces and visualizing eigenvector fields.

Findings

Triple degenerate tensors are structurally stable and form curves.

Two methods to measure rotation and deformation strengths are proposed.

The approach effectively visualizes complex tensor field features in real-world datasets.

Abstract

3D asymmetric tensor fields have found many applications in science and engineering domains, such as fluid dynamics and solid mechanics. 3D asymmetric tensors can have complex eigenvalues, which makes their analysis and visualization more challenging than 3D symmetric tensors. Existing research in tensor field visualization focuses on 2D asymmetric tensor fields and 3D symmetric tensor fields. In this paper, we address the analysis and visualization of 3D asymmetric tensor fields. We introduce six topological surfaces and one topological curve, which lead to an eigenvalue space based on the tensor mode that we define. In addition, we identify several non-topological feature surfaces that are nonetheless physically important. Included in our analysis are the realizations that triple degenerate tensors are structurally stable and form curves, unlike the case for 3D symmetric tensors fields. Furthermore, there are two different ways of measuring the relative strengths of rotation and angular deformation in the tensor fields, unlike the case for 2D asymmetric tensor fields. We extract these feature surfaces using the A-patches algorithm. However, since three of our feature surfaces are quadratic, we develop a method to extract quadratic surfaces at any given accuracy. To facilitate the analysis of eigenvector fields, we visualize a hyperstreamline as a tree stem with the other two eigenvectors represented as thorns in the real domain or the dual-eigenvectors as leaves in the complex domain. To demonstrate the effectiveness of our analysis and visualization, we apply our approach to datasets from solid mechanics and fluid dynamics.