Semantic Flow Graph: A Framework for Discovering Object Relationships in Flow Fields

FlowNL: Asking the Flow Data in Natural Languages

Flow visualization is essentially a tool to answer domain experts' questions about flow fields using rendered images. Static flow visualization approaches require domain experts to raise their questions to visualization experts, who develop specific techniques to extract and visualize the flow structures of interest. Interactive visualization approaches allow domain experts to ask the system directly through the visual analytic interface, which provides flexibility to support various tasks. However, in practice, the visual analytic interface may require extra learning effort, which often discourages domain experts and limits its usage in real-world scenarios. In this paper, we propose FlowNL, a novel interactive system with a natural language interface. FlowNL allows users to manipulate the flow visualization system using plain English, which greatly reduces the learning effort. We develop a natural language parser to interpret user intention and translate textual input into a declarative language. We design the declarative language as an intermediate layer between the natural language and the programming language specifically for flow visualization. The declarative language provides selection and composition rules to derive relatively complicated flow structures from primitive objects that encode various kinds of information about scalar fields, flow patterns, regions of interest, connectivities, etc. We demonstrate the effectiveness of FlowNL using multiple usage scenarios and an empirical evaluation.

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

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. As quantum fluids with turbulence are gaining attention in physics, extracting and visualizing vortex-core lines for quantum fluids is increasingly desirable. In this article, we develop an efficient vortex-core line vectorization method for quantum fluids enabling real-time visualization of high-resolution quantum turbulence structure. From a dataset obtained through simulation, our technique first identifies vortex nodes based on the circulation field. To vectorize the vortex-core lines interpolating these vortex nodes, we propose a novel graph-based data structure, with iterative graph reduction and density-guided local optimization, to locate sub-grid-scale vortex-core line samples more precisely, which are then vectorized by continuous curves. This vortex-core representation naturally captures complex topology, such as branching during reconnection. Our vectorization approach reduces memory consumption by orders of magnitude, enabling real-time visualization performance. Different types of interactive visualizations are demonstrated to show the effectiveness of our technique, which could help further research on quantum turbulence.

SurfRiver: Flattening Stream Surfaces for Comparative Visualization

We present SurfRiver, a new visual transformation approach that flattens stream surfaces in 3D to rivers in 2D for comparative visualization. Leveraging the TextFlow-like visual metaphor, SurfRiver untangles the convoluted individual stream surfaces along the flow direction and maps them along the horizontal direction of the abstract river view. It stacks multiple surfaces along the vertical direction of the river view. This visual mapping makes it easy for users to track along the flow direction and align stream surfaces for comparative study. Through brushing and linking, the river view is connected to the spatial surface view for collective reasoning. SurfRiver can be used to examine a single stream surface, investigate seeding sensitivity or variability of a family of surfaces from a group of related seeding curves, or explore a collection of representative surfaces. We describe our optimization solution to achieve the desirable mapping, present SurfRiver interface and interactions, and report results from different flow fields to demonstrate its efficacy. Feedback from a domain expert also indicates the promise of SurfRiver.