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Combustion Science Data Management Needs. Jacqueline H. Chen Combustion Research Facility Sandia National Laboratories DOE Data Management Workshop SLAC Stanford, CA March 16-18, 2004

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combustion science data management needs

Combustion Science Data Management Needs

Jacqueline H. Chen

Combustion Research Facility

Sandia National Laboratories

DOE Data Management Workshop


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

challenges in combustion understanding and modeling
Challenges in combustion understanding and modeling
  • Stiffness: wide range of length and time scales
    • turbulence
    • flames and ignition fronts
    • high pressure
  • Chemical complexity
    • large number of species and reactions
  • Multi-physics complexity
    • multiphase (liquid spray, gas phase, soot)
    • thermal radiation
    • acoustics ...

Diesel Engine Autoignition, Laser Incandescence

Chuck Mueller, Sandia National Laboratories


Direct Numerical Simulation (DNS) Approach







  • High-fidelity computer-based observations of micro-physics of chemistry-turbulence interactions
  • Resolve all relevant scales
  • At low error tolerances, high-order methods are more efficient
  • Laboratory scale configurations: homogeneous turbulence, v-flame turbulent jets, counterflow
  • Complex chemistry - gas phase/heterogeneous (catalytic)

Turbulent methane-air diffusion flame


High-fidelity Simulations of Turbulent

Combustion (TSTC)


Software design developments

Numerical developments

. S3D0: F90 MPP 3D

. S3D1: GrACE-based

. S3D2: CCA-compliant




Model developments


. Thermal radiation

. Soot particles

. Liquid droplets




Arnaud Trouvé, U. Maryland

Jacqueline Chen, Sandia

Chris Rutland, U. Wisconsin

Hong Im, U. Michigan

R. Reddy and R. Gomez, PSC

Post-processors: flamelet, statistical


3D DNS Code (S3D) scales to over a thousand processors

  • Scalability benchmark test for S3D on MPP platforms - 3D laminar

hydrogen/air flame/vortex problem (8 reactive scalars)

  • Ported to IBM-SP3, SP4, Compaq SC, SGI Origin, Cray T3E,

Intel Xeon Linux clusters

a computational facility for reacting flow science cfrfs
A Computational Facility for Reacting Flow Science (CFRFS)
  • Develop a flexible, maintainable, toolkit for high-fidelity Adaptive Mesh Refinement (AMR) Massively-Parallel low Mach number reacting flow computations
  • Develop an associated CSP data analysis and reduction toolkit for multidimensional reacting flow
  • Use CSP and a PRISM tabulation approach to enable adaptive chemistry reacting flow computations
    • PRISM = Piecewise Reusable Implementation of Solution Mapping (M. Frenklach)

CCA GUI showing connections


Motivation: Control of HCCI combustion

  • Overall fuel-lean, low NOx and soot, high efficiencies
  • Volumetric autoignition, kinetically driven
  • Mixture/thermal inhomogeneities used to control ignition timing and burn rate
  • Spread heat release over time to minimize pressure oscillations

Chen et al., submitted 2004, Sankaran et al., submitted 2004

  • Gain fundamental insight into turbulent autoignition with compression heating
  • Develop systematic method for determining ignition front speed and establish criteria to distinguish between combustion modes
  • Quantify front propagation speed and parametric dependence on turbulence and initial scalar fields
  • Develop control strategy using temperature inhomogeneities to control timing and rate of heat release in HCCI combustion
    • deflagration
    • spontaneous ignition
    • detonation
initial conditions

Baseline symmetric case

Hot core gas

Cold core gas

Initial conditions
  • Same mean T (1070K)
  • Different T skewness

and variance (15,30K)

  • Pressure 41 – 55 atm
  • Lean hydrogen/air
temperature skewness effect on heat release rate


Hot core

Cold core

2.0 ms

2.4 ms

2.6 ms

2.8 ms

Temperature skewness effect on heat release rate

Heat release, HighT, positive skewness

temperature skewness effect on ignition delay and burn time
Temperature skewness effect on ignition delay and burn time
  • Temperature distribution influences ignition and duration of burning.
  • Hot core gas
    • Ignited earlier
    • Burns longer
  • Cold core gas
    • Ignited later
    • Slow end gas combustion
ignition front tracking method
Ignition front tracking method
  • Density-weighted displacement speed (Echekki and Chen, 1999):
  • YH2 = 8.5x10-4 isocontour – location of maximum heat release
  • Laminar reference speed, sL based on freely propagating premixed flame at local enthalpy and pressure conditions at front surface
species balance and normalized front speed criteria for propagation mode
Species balance and normalized front speed criteria for propagation mode



Heat release isocontours


Black lines – s*d/sL < 1.1 (deflagration)

White lines – s*d/sL > 1.1 (spontaneous ignition)

A – deflagration B, C – spontaneous ignition

fraction of front length and burnt gas area production due to deflagration
Fraction 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 data
Comparison of experimental and DNS data for ignition/edge flame data
  • 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

Normalized OH Expt

Normalized OH DNS

Heated air


H2 + O = OH + H

O2 + H = O + OH

slow OH recombination








apriori testing of reaction models using dns of turbulent jet flames
Apriori 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 flame
Joint 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 science
Data 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.
  • 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 detection
Feature Detection
  • Detection = Identify features in each time step
  • FDTOOLS tests each cell & groups connected ones
  • There are many possible algorithms including pattern recognition
feature tracking
Feature Tracking
  • Tracking = Identify relationships between features in different time steps
  • Again, there are many different algorithms, and knowing about how your features interact helps
  • Merge
  • (Birth)
  • (Death)
  • Split
  • Other domain specific events like hard-body collision, vorticity tube reconnect, etc. …
design goal flexible reusable
Design Goal: Flexible & Reusable
  • Callable from running programs
  • Independent of visualization package
  • Modular
    • Detector plug-ins
    • Tracker plug-ins
    • Other plug-ins …
  • CCA compatible
  • Output interface for further analysis
dataset types
DataSet Types

fdRegular 2 & 3D

of all

fdRefined structured


fdtools design wendy koegler snl
FDTOOLS Design (Wendy Koegler SNL)

Output Interface

Data Interface

FDTOOLS Component


Feature Manager







Detection and tracking of autoignition features

FDTools (Koegler, 2002): evolution of ignition features

Hydroperoxy mass fraction

data management framework for combustion science i
Data management framework for combustion science – I
  • Distributed data mining tools: feature ID and tracking
  • Distributed analysis tools operating on regions of interest
    • Reaction source term and Jacobian evaluation
    • Conditional statistics
    • Isolevel surface of multiply-connected 3D surfaces
      • Interpolate, integrate, differentiate in principle directions to surface
    • Computational singular perturbation analysis
    • Reaction flux analysis
    • Principal component analysis
    • Spectral analysis
data management framework for combustion science ii
Data management framework for combustion science – II
  • Data objects, which interface to metadata and data
    • Enabling writing and reading data with various flexible formats
    • Standard data formats
    • Automatic conversion utilities
  • Flexible, user-configurable, user-friendly GUI’s to enable

user to specify desired operations on data

  • General structured and unstructured adaptive mesh data
  • Real-time feature-borne detection, tracking and analysis for computational steering (e.g. adaptive IO, temporal statistics)
data management framework for combustion science iii
Data management framework for combustion science – III
  • Distributed visualization tools

scalar and non-scalar data

    • Non-scalar data, i.e. vector or tensor
    • Heterogeneous data – combined experimental and computational data
    • Iso-surface rendering and interpolating data onto user-specified slices
    • Streamlines, information overlays
    • Uncertainty
    • Viz reduced-order representations of flow and combustion features