Transcript Document

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
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
–
–
–
Diesel Engine Autoignition, Laser Incandescence
Chuck Mueller, Sandia National Laboratories
multiphase (liquid spray, gas
phase, soot)
thermal radiation
acoustics ...
Direct Numerical Simulation (DNS) Approach
 High-fidelity computer-based
observations of micro-physics of
chemistry-turbulence interactions
Turbulent methane-air diffusion flame
Oxidizer
Fuel
 Resolve all relevant scales
 At low error tolerances, high-order
methods are more efficient
HO2
CH4
CH3O
O
 Laboratory scale configurations:
homogeneous turbulence, v-flame
turbulent jets, counterflow
 Complex chemistry - gas
phase/heterogeneous (catalytic)
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
MPP S3D
CMCS
DM
Post-processors:
flamelet, statistical
. 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
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)
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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
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
Initial conditions
0.4
0.3
0.25
0.2
0.15
0.1
Hot core gas
Cold core gas
0.05
0.4
0.4
0.3
0.25
0.2
0.15
0.1
0
0
0.1
0.2
x (cm)
0.3
0.4
T (K)
1101
1095
1090
1084
1078
1072
1066
1060
1054
1048
1042
1036
1030
1024
1018
0.35
0.3
0.25
y (cm)
T (K)
1114
1109
1103
1098
1093
1088
1082
1077
1072
1066
1061
1056
1051
1045
1040
0.35
0.2
0.15
0.1
0.05
0
T (K)
1107
1101
1096
1091
1086
1080
1075
1070
1065
1059
1054
1049
1043
1038
1033
0.35
y (cm)
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
Baseline symmetric case
Same mean T (1070K)
Different T skewness
and variance (15,30K)
Pressure 41 – 55 atm
Lean hydrogen/air
y (cm)


0.05
0
0.1
0.2
x (cm)
0.3
0.4
0
0
0.1
0.2
x (cm)
0.3
0.4
Temperature skewness effect on heat release
rate
Symm
Hot core Cold core
2.0 ms
2.4 ms
2.6 ms
2.8 ms
Heat release, HighT, positive skewness
Temperature skewness effect on ignition delay
and burn time
0.8
influences ignition and
duration of burning.
 Hot core gas
 Ignited earlier
 Burns longer
 Cold core gas
 Ignited later
 Slow end gas combustion
Integrated HRR [MW/g]
 Temperature distribution
Baseline
Hot core
Cold core
0.7
0.6
0.5
0.4
0.3
0.2
0.1
1
1.5
2
2.5
t [ms]
DNS Cases
Burn time [ms]
Baseline
Hot
Cold
0.894
1.085
0.953
3
3.5
Ignition front tracking method
Density-weighted displacement speed (Echekki and Chen, 1999):
sd  s
*
d
 DDt

 o 
 c
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
Heat release isocontours
A
C
B
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
•Solid line front length
•Dashed line – burnt
area production
Comparison of experimental and DNS data for
ignition/edge flame data
Normalized OH Expt Normalized OH DNS
Heated air
H2/N2
 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
H2 + O = OH + H
O2 + H = O + OH
slow OH recombination
H2
LP
OH
H
DF
RP
xst
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
 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
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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.
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”
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
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Detection = Identify features in each time step
FDTOOLS tests each cell & groups connected ones
There are many possible algorithms including pattern recognition
Feature Tracking
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Tracking = Identify relationships between features in different time steps
Again, there are many different algorithms, and knowing about how your
features interact helps
Events
• Merge
• (Birth)
• (Death)
• Split
• Other domain specific events like hard-body collision,
vorticity tube reconnect, etc. …
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
fdRegular
fdRefined
(AMR)
2 & 3D
of all
structured
FDTOOLS Design (Wendy Koegler SNL)
Output Interface
Data Interface
Director
Detector
Feature Manager
Analyzer
Tracker
FDTOOLS Component
Representer
Visualizer
Detection and tracking of autoignition features
FDTools (Koegler, 2002): evolution of ignition features
Hydroperoxy mass fraction
Feature graph tracks evolution of ignition features
time
Feature-borne analysis
2800
#47
Max Temperature (K)
2600
2400
#39
#46
#5
2200
2000
1800
1600
1400
#41,#68
1200
1000
#40 #27
800
0
0.02
0.04
0.06
Time (msec)
#45
#18,#52
#11
0.08
0.1
Ignition feature classification
Average. H 2 Conc. (mass fraction)
0.010
0.009
0.008
0.007
0.006
0.005
0.004
0.003
extinquishes
consumed
ignites
0.002
0.001
0.000
1090
1100
1130
1120
1110
Average Temperature (K)
1140
1150
Terascale virtual combustion analysis facility
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
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–
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Computational singular perturbation analysis
Reaction flux analysis
Principal component analysis
Spectral analysis
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
• 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