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Direct Numerical Simulations of Turbulent CombustionDirect Numerical Simulations of Turbulent Combustion
Jacqueline H. Chen
Combustion Research Facility
Sandia National [email protected]
Data Management All-Hands Meeting
March 3-4, 2005
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 modelingChallenges in combustion understanding and modeling
Diesel Engine Autoignition, Laser IncandescenceChuck Mueller, Sandia National Laboratories
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 ...
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
HO2
CH3O
CH4
O
Oxidizer
Fuel
. S3D0: F90 MPP 3D
. S3D1: GrACE-based
. S3D2: CCA-compliant
Software design developments
. IMEX ARK
. IBM
. AMR
Numerical developments
. Thermal radiation
. Soot particles
. Liquid droplets
Model developments
CFRFS
CCA
Post-processors: flamelet, statistical
CMCS DM
MPP S3D
Arnaud Trouvé, U. Maryland Jacqueline Chen, SandiaChris Rutland, U. WisconsinHong Im, U. MichiganR. Reddy and R. Gomez, PSC
High-fidelity Simulations of Turbulent Combustion (TSTC) http://scidac.psc.edu
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
Office of Science INCITE award provides 2.5 million cpu-hours at NERSC for combustion science simulationOffice of Science INCITE award provides 2.5 million cpu-hours at NERSC for combustion science simulation
Direct simulation of a 3D turbulent flame with detailed chemistry (200 million grids, 12 species, 5 TB raw data, 5 TB derived data, 3000 cpus)
• Extinction-reignition dynamics
• Among largest simulations
• Benchmark data for testing models
• FY05 BES Joule PART goal
3D DNS performed at NERSC, ORNL, PNNL – preparatory runs of up to 40 million grid points, 20 dof
Extinction-Reignition DynamicsExtinction-Reignition Dynamics
Mechanisms for reignition: Edge flame propagation, flame propagation normal to isosurface, self-ignition
TNF Workshop: International Collaboration of Experimental and Computation Researchers TNF Workshop: International Collaboration of Experimental and Computation Researchers
• International Workshop on Measurement and Computation of Turbulent Nonpremixed Flames (since 1996)
– Framework for detailed comparison of measured and modeled results– Identify what does not work, define research priorities– Core groups: Berkeley, Cornell, TU Darmstadt, Imperial College, U Sydney
• Adds leverage and impact to BES Combustion Program– Built around Sandia experiments and CRF visitor program– New opportunities for numerical benchmarks – highly resolved LES and DNS
Reacting Turbulent Jet flow Simulation Reacting Turbulent Jet flow Simulation
Heat release rate
3D Turbulent Reactive Jet Flames – 40 Million Grids, 1 TB data, 480 cpus on MPP2 at PNNL3D Turbulent Reactive Jet Flames – 40 Million Grids, 1 TB data, 480 cpus on MPP2 at PNNL
Vorticity magnitude OH mass fraction
Volume Rendering by Kwan-Liu Ma
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
Experimental evidence of ignition front propagation Experimental evidence of ignition front propagation
PLIF of OH in HCCI engine at TDC, Richter et al. 2000
Hultqvist, et al. 2002 – chemiluminescence and fuel LIF imaging of time-
resolved sequence in a single cycle
Volumetric combustion early on, kernel evolution at discrete locations later
(discrete edges between burned/unburned, reaction fronts spreading at 15
m/s.
Objectives Objectives
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
Chen et al., submitted 2004, Sankaran et al., submitted 2004
Temperature skewness effect on heat release rateTemperature skewness effect on heat release rate
Heat release, HighT, positive skewness
2.0 ms
2.4 ms
2.6 ms
2.8 ms
Symm Hot core Cold core
Ignition front tracking methodIgnition front tracking method
cd
DtDss
od
*
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
Density-weighted displacement speed (Echekki and Chen, 1999):
Species balance and normalized front speed criteria for propagation mode Species balance and normalized front speed criteria for propagation mode
Black lines – s*d/sL < 1.1 (deflagration)White lines – s*d/sL > 1.1 (spontaneous ignition)
A – deflagration B, C – spontaneous ignition
A C
B
Heat release isocontours
SummarySummary
Addition of hot fluid parcel (temperature skewness) slows down heat release, so does increasing temperature variance – effective control of HCCI
Both spontaneous ignition and deflagrative propagation present for initial spectrum of ‘hot’ spots modulated by turbulent mixing
Significant effect of heat conduction and dissipation of temperature gradients along with front annihilation – increase propagation rate
New method for determining the speed of ignition fronts and criterion for deflagrative versus spontaneous front propagation (s*d/sl > 1)
Detection and tracking of autoignition features
FDTools (Koegler, 2002): evolution of ignition features
Hydroperoxy mass fraction
Feature graph tracks evolution of ignition featuresFeature graph tracks evolution of ignition features
time
Feature-borne analysisFeature-borne analysis
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
0 0.02 0.04 0.06 0.08 0.1Time (msec)
Max
Tem
pera
ture
(K) #5
#46#39#47
#45#40 #11
#18,#52#27
#41,#68
Terascale virtual combustion analysis facilityTerascale virtual combustion analysis facility
Data Management Challenges for CombustionData Management Challenges for Combustion
• Parallel data-analysis tools for combustion analysis– 3D iso-level set analysis normal and tangent to surface for thin flames– Conditional statistics– Reduced representations of combustion data (POD, PCA, topology of
vector and scalar fields) for model development and viz.– Tracking flame elements or fluid particles in time - interpolating
• Parallel feature detection and tracking of TB-scale data• Quantitative viz. coupled with analysis of TB-scale – vol. rendering• Mid-range platforms for preparing runs, analysis and visualization (10-fold
smaller than leadership class – 1 Tflop, $300-600K Opteron cluster, raid storage systems 1-10 TB)
• IO issues for postprocessing phase when temporal analysis is required.• Further remote analysis and viz. of numerical benchmark data and
comparison with experimental data by modelers at different locations – Framework or Virtual Facility??
• Jointly funded activities (?? FTE’s combustion; ?? FTE’s from Data Management ISIC both for research and deployment).
AcknowledgmentsAcknowledgments
SNL Postdoctoral fellows: SNL collaborators:
Evatt Hawkes Jonathan Frank
Shiling Liu John Hewson
Chris Kennedy Wendy Koegler
Ph.D. Student:
James Sutherland
External collaborators:
Prof. Stewart Cant (Cambridge U.) Prof. Heinz Pitsch (Stanford)
Prof. Hong Im (U. Michigan) Prof. Tarek Echekki (NC State)
Prof. Arnaud Trouve (U. Maryland) Ramanan Sankaran (U. Michigan)
Prof. Chris Rutland (U. Wisconsin) Reinhard Seiser (UCSD)
Prof. K. Seshadri (UCSD) R. Reddy and Wang (PSC)
Computing ResourcesComputing Resources
DOE NERSC – IBM SP
ORNL – IBM SP
PNL – Linux cluster
SNL – Intel Linux cluster, SGI Origin, Compaq Sierra Cluster