Direct Numerical Simulations of Turbulent Combustion Jacqueline H. Chen Combustion Research Facility Sandia National Laboratories 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 modeling Diesel Engine Autoignition, Laser Incandescence Chuck 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 CH 3 O  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, Sandia Chris Rutland, U. Wisconsin Hong Im, U. Michigan R. Reddy and R. Gomez, PSC High-fidelity Simulations of Turbulent Combustion (TSTC)

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 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 Dynamics Mechanisms for reignition: Edge flame propagation, flame propagation normal to isosurface, self-ignition

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 Heat release rate

3D 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 PLIF of OH in HCCI engine at TDC, Richter et al  Hultqvist, et al – 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  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 rate Heat release, HighT, positive skewness 2.0 ms 2.4 ms 2.6 ms 2.8 ms SymmHot coreCold core

Ignition front tracking method  Y H2 = 8.5x10 -4 isocontour – location of maximum heat release  Laminar reference speed, s L 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 Black lines – s* d /s L < 1.1 (deflagration) White lines – s* d /s L > 1.1 (spontaneous ignition) A – deflagration B, C – spontaneous ignition AC B Heat release isocontours

Summary  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 /s l > 1)

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

Terascale virtual combustion analysis facility

Data 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, $ K 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).

Acknowledgments SNL Postdoctoral fellows:SNL collaborators: Evatt HawkesJonathan Frank Shiling LiuJohn Hewson Chris KennedyWendy 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 Resources  DOE NERSC – IBM SP  ORNL – IBM SP  PNL – Linux cluster  SNL – Intel Linux cluster, SGI Origin, Compaq Sierra Cluster