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Green Image
Issue No. 12 - Dec. (2011 vol. 17)
ISSN: 1077-2626
pp: 1822-1831
Janine C. Bennett , Sandia Nat. Labs., Albuquerque, NM, USA
Vaidyanathan Krishnamoorthy , Sci. Comput. & Imaging Inst., Univ. of Utah, Salt Lake City, UT, USA
Shusen Liu , Sci. Comput. & Imaging Inst., Univ. of Utah, Salt Lake City, UT, USA
Ray W. Grout , Nat. Renewable Energy Lab., Golden, CO, USA
Evatt R. Hawkes , Univ. of New South Wales, Sydney, NSW, Australia
Jacqueline H. Chen , Sandia Nat. Labs., Albuquerque, NM, USA
Jason Shepherd , Sandia Nat. Labs., Albuquerque, NM, USA
Valerio Pascucci , Sci. Comput. & Imaging Inst., Univ. of Utah, Salt Lake City, UT, USA
Peer-Timo Bremer , Lawrence Livermore Nat. Lab., Livermore, CA, USA
We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.
Feature extraction, Information analysis, Data mining, Data models, Statistical analysis, Multi-variate Data., Topology, Statistics, Data analysis, Data exploration, Visualization in Physical Sciences and Engineering

J. H. Chen et al., "Feature-Based Statistical Analysis of Combustion Simulation Data," in IEEE Transactions on Visualization & Computer Graphics, vol. 17, no. , pp. 1822-1831, 2011.
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