TY - GEN
T1 - An analytical framework for particle and volume data of large-scale combustion simulations
AU - Sauer, Franz
AU - Yu, Hongfeng
AU - Ma, Kwan Liu
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Data transformation and representation
KW - Feature extraction and tracking
KW - Scalability issues
UR - http://www.scopus.com/inward/record.url?scp=84893000002&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893000002&partnerID=8YFLogxK
U2 - 10.1145/2535571.2535590
DO - 10.1145/2535571.2535590
M3 - Conference contribution
AN - SCOPUS:84893000002
SN - 9781450325004
T3 - Proc. of UltraVis 2013: 8th Int. Workshop on Ultrascale Visualization - Held in Conjunction with SC 2013: The Int. Conference for High Performance Computing, Networking, Storage and Analysis
BT - Proc. of UltraVis 2013
T2 - 8th International Workshop on Ultrascale Visualization, UltraVis 2013 - Held in Conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2013
Y2 - 17 November 2013 through 17 November 2013
ER -