Combustion Science Data Management Needs Jacqueline H. Chen Combustion Research Facility Sandia National Laboratories [email protected]DOE Data Management Workshop SLAC Stanford, CA March 16-18, 2004 Sponsored by the Division of Chemical Sciences Geosciences, and Biosciences, the Office of Basic Energy Sciences, the U. S. Department of Energy
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Combustion Science Data Management Needs Jacqueline H. Chen Combustion Research Facility Sandia National Laboratories [email protected] DOE Data Management.
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Combustion Science Data Management NeedsCombustion Science Data Management Needs
Fraction of front length and burnt gas area production due to deflagrationFraction of front length and burnt gas area production due to deflagration
•Solid line front length
•Dashed line – burnt area production
Comparison of experimental and DNS data for ignition/edge flame dataComparison of experimental and DNS data for ignition/edge flame data
H2 + O = OH + HO2 + H = O + OHslow OH recombination
RP
LP
DF
OH
H st
Normalized OH Expt
H2
Heated air
H2/N2
Normalized OH DNS Flow divergence effect – (Ruetsch et al. 1994) upstream divergence of flow due to increase in normal component of flow resulting from heat release
Curvature – preferential diffusion focusing effect at leading edge
Apriori testing of reaction models using DNS of turbulent jet flamesApriori testing of reaction models using DNS of turbulent jet flames
Sutherland et al., submitted 2004
CO/H2/air jet flame, scalar dissipation rate
Joint experiment/computation of turbulent premixed methane/air V-flameJoint experiment/computation of turbulent premixed methane/air V-flame
Stationary statistics required for turbulent premixed flame model development LES/RANS
Flame topology – curvature stretch statistics
Complex chemistry versus simple or tabulated chemistry (heat release, radicals, minor species)
Is preheat zone thickening due to small scales or higher curvatures in thin reaction zone regime? V-flame, expt. Renou 2003
and DNS, Vervisch 2003
Data management challenges for combustion scienceData management challenges for combustion science
• 2D complex chemistry simulations today: 200 restart files (x,y,Z1,…Z50) skeletal n-heptane 41 species, 2000x2000 grid, 1.6 Gbytes/time x200 files = 0.32 Tbyte, 5 runs in parametric study 1.6 Tbytes raw data
• Processed data: 2 Tbyte data
• 3D complex chemistry simulations in 5 years: 200 restart files (x,y,Z1,…Z50) skeletal n-heptane 41 species, 2000x2000x2000 grid, 3.2 Tbytes/time x 200 files = 640 Tbytes per run, 5 runs = 3.2 Petabytes raw data
• Processed data: 3 Petabytes
• Combustion regions of interest are spatially sparse
• Feature-borne analysis and redundant subsetting of data for storage
• Provenance of subsetted data
• Temporal analysis must be done on-the-fly
• Remote access to transport subsets of data for local analysis and viz.
FeaturesFeatures
• Feature is an overloaded word
• A feature in this context is a subset of the data grid that is interesting for some reason.
• Might call it a “Region of Interest” (ROI)
• Also might call it a “structure”
Why Feature Tracking?Why Feature Tracking?
• Reduce size of data– How do you find small ROI’s in a large 3D domain?
– Retrieve and analyze only what you need
• Provide quantification– Can exactly define ROI chosen & do specific statistics
• Enhance visualization– Can visualize features individually
– Can color code features
• Facilitate event searching– Events are feature interactions
Feature DetectionFeature Detection
• Detection = Identify features in each time step
• FDTOOLS tests each cell & groups connected ones
• There are many possible algorithms including pattern recognition
Feature TrackingFeature Tracking
• Tracking = Identify relationships between features in different time steps
• Again, there are many different algorithms, and knowing about how your features interact helps
EventsEvents
• Merge
• (Birth)
• (Death)
• Split
• Other domain specific events like hard-body collision, vorticity tube reconnect, etc. …