Advanced visualization technology for terascale particle accelerator simulations
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
RGVis: Region Growing Based Techniques for Volume Visualization
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
An Integrated Exploration Approach to Visualizing Multivariate Particle Data
Computing in Science and Engineering
High performance multivariate visual data exploration for extremely large data
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Visualizing Temporal Patterns in Large Multivariate Data using Modified Globbing
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Feature-Based Statistical Analysis of Combustion Simulation Data
IEEE Transactions on Visualization and Computer Graphics
Visual Analysis of Particle Behaviors to Understand Combustion Simulations
IEEE Computer Graphics and Applications
Visual Analytics for Finding Critical Structures in Massive Time-Varying Turbulent-Flow Simulations
IEEE Computer Graphics and Applications
Geometric Quantification of Features in Large Flow Fields
IEEE Computer Graphics and Applications
A tri-space visualization interface for analyzing time-varying multivariate volume data
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
In-situ sampling of a large-scale particle simulation for interactive visualization and analysis
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
| Hi-index | 0.00 |
This paper presents a framework to enable parallel data analyses and visualizations that combine both Lagrangian particle data and Eulerian field data of large-scale combustion simulations. Our framework is characterized by a new range query based design that facilitates mutual queries between particles and volumetric segments. Scientists can extract complex features, such as vortical structures based on vector field classifications, and obtain detailed statistical information from the corresponding particle data. This framework also works in reverse as it can extract vector field information based on particle range queries. The effectiveness of our approach has been demonstrated by an experimental study on vector field data and particle data from a large-scale direct numerical simulation of a turbulent lifted ethylene jet flame. Our approach provides a foundation for scalable heterogeneous data analytics of large scientific applications.