Combustion Science Data Management Needs

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Combustion Science Data Management Needs

• Jacqueline H. Chen
• Combustion Research Facility
• Sandia National Laboratories
• jhchen_at_sandia.gov
• 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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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
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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
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High-fidelity Simulations of Turbulent
Combustion (TSTC) http//scidac.psc.edu
CFRFS
Software design developments
Numerical developments
. S3D0 F90 MPP 3D . S3D1 GrACE-based . S3D2
CCA-compliant
. IMEX ARK . IBM . AMR
Model developments
CCA
. Thermal radiation . Soot particles . Liquid
droplets
MPP S3D
CMCS DM
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
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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

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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
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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

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Objectives
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

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Initial conditions

• Same mean T (1070K)
• Different T skewness
• and variance (15,30K)
• Pressure 41 55 atm
• Lean hydrogen/air

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Temperature skewness effect on heat release rate
Heat release, HighT, positive skewness
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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

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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

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Species balance and normalized front speed
criteria for propagation mode
A
C
Heat release isocontours
B
Black lines sd/sL lt 1.1 (deflagration) White
lines sd/sL gt 1.1 (spontaneous ignition) A
deflagration B, C spontaneous ignition
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Fraction of front length and burnt gas area
production due to deflagration

• Solid line front length
• Dashed line burnt area production

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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/N2
H2 O OH H O2 H O OH slow OH
recombination
H2
LP
OH
xst
H
DF
RP
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Apriori testing of reaction models using DNS of
turbulent jet flames
Sutherland et al., submitted 2004
CO/H2/air jet flame, scalar dissipation rate
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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
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Lean premixed combustion at Sandiaswirl burner,
LES, and DNS
DNS E. Hawkes and J.H. Chen
Experiment OH PLIF, PIV R.W Schefer
LES J. Oefelein
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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.

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Features

• 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

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Why Feature Tracking?

• Reduce size of data
• How do you find small ROIs 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

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Feature Detection

• Detection Identify features in each time step
• FDTOOLS tests each cell groups connected ones
• There are many possible algorithms including
  pattern recognition

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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

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Events

• Merge
• (Birth)
• (Death)
• Split
• Other domain specific events like hard-body
  collision, vorticity tube reconnect, etc.

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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

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DataSet Types
fdRegular 2 3D of
all fdRefined structured (AMR)
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FDTOOLS Design (Wendy Koegler SNL)
Output Interface
Data Interface
FDTOOLS Component
Director
Feature Manager
Representer
Detector
Analyzer
Tracker
Visualizer
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Detection and tracking of autoignition features
FDTools (Koegler, 2002) evolution of ignition
features
Hydroperoxy mass fraction
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Feature graph tracks evolution of ignition
features
time
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Feature-borne analysis
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Ignition feature classification
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Terascale virtual combustion analysis facility

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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

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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 GUIs
  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)

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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