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High Fidelity Numerical Simulations of Turbulent Combustion

High Fidelity Numerical Simulations of Turbulent Combustion. Jacqueline H. Chen (PI) Chun S. Yoo David H. Lignell Combustion Research Facility Sandia National Laboratories, Livermore, CA Ramanan Sankaran National Center for Computational Sciences Oak Ridge National Laboratory.

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High Fidelity Numerical Simulations of Turbulent Combustion

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  1. High Fidelity Numerical Simulationsof Turbulent Combustion Jacqueline H. Chen (PI)Chun S. Yoo David H. Lignell Combustion Research FacilitySandia National Laboratories, Livermore, CA Ramanan Sankaran National Center for Computational SciencesOak Ridge National Laboratory

  2. Direct numerical simulation (DNS) of turbulent combustion Turbulent combustionis a grand challenge DNS approach and role • Fully resolve all continuum scales without using sub-grid models. • Only a limited range of scales is computationally feasible. • Petascale computing = DNS with O(104) scales for cold flow. • DNS of small-scale laboratory flames. Investigate turbulence-chemistry interactions relevant in devices. Provide numerical benchmark data for predictive model validation and development. • Turbulent combustion involves coupled phenomena at a wide range of scales • O(104) continuum scales Combustor size ~1m Molecularreactions ~1nm

  3. S3D—First principles combustion solver • Used to perform first-principles-based DNS of reacting flows • Solves compressible reacting Navier-Stokes equations • High-fidelity numerical methods • Detailed reaction kinetics andmolecular transport models • Multi-physics (sprays, radiation and soot) from SciDAC-TSTC • Ported to all major platforms DNS provides unique fundamental insight into the chemistry-turbulence interaction Engineering CFD codes (RANS, LES) Physical models DNS

  4. Efficient parallel scaling Scales to 23,000 Cray XT processors 1000 SP3 (NERSC) Opteron (Sandia) XT (ORNL) Cost per grid point per time-step (µs) 100 Itanium (PNNL) SP5 (NERSC) Results from weak scaling test on various Office of Science platforms X1E (ORNL) 10 1 10 100 1000 10000 Number of processors

  5. Combustion science enabled by NCCS CO/H2 non-premixed flames (2005) 500M grid points Lifted flames (2007) 1B grid points Ethylene non-premixed flames (2007) 350M grid points Lean premixed flames (2006) 200M grid points Flame-wall interaction (2006)

  6. DNS of turbulent lifted H2/air jet flames in heated coflow • Determine stability and characteristics of a lifted flame • Understand flame stabilization mechanism • Effect of degree of fuel-air pre-mixing • Effect of turbulent flow • Effect of preheating and auto-ignition • Simulation performed on Jaguar on 9000 cores and 2.5 million cpu-hrs • ~1 billion grid points • Detailed H2/air chemistry with 14109 DOF • 9 resolved species and 21 elementary reaction steps • Jet Reynolds number = 11,000 Instantaneous volume rendering of the local mixing rate, by H. Yu and K. L. Ma of the SciDAC Institute for Ultrascale Visualization

  7. Flame stabilization primarily due to autoignition • Flame stabilizes in fuel-lean mixture where the temperature is high (toward the heated air coflow) and mixing rates are low. • Hydroperoxy radical (HO2): • Precursor of auto-ignition in hydrogen-air chemistry. • Builds up upstream of OH and other intermediate radicals (H and H2O2). • Indicates auto-ignition should be primary stabilization mechanism. Temperature versus mixture fraction at various axial positions Instantaneous volume rendering of HO2 (ignition marker) and OH (flame marker) by H. Yu and K. L. Ma of the SciDAC Institute for Ultrascale Visualization

  8. Stabilization depends on competition between autoignition and large-scale eddy passage • Near flame base, slightly negative or small positive axial velocity is observed (recirculation assists stabilization of flame base). • Stabilization point movement is cyclic with passage of large-eddies Isocontours of temperature with mixture fraction (dotted white line) and streamlines (arrowed line) averaged in time and z-direction Correlation of stabilization point and axial velocity and ignition

  9. Extinction and reignition in a nonpremixed ethylene/air turbulent jet flame • Goal: study extinction reignition processes in hydrocarbon flames • Autoignition • Premixed flame propagation • Edge flame propagation • 3D DNS of a slot jet at Re = 5120 • Reduced ethylene mechanism consisting of 19 transported and 10 quasi-steady state species, with 167 reactions (Lu and Law 2007) • 340 million grid points (8.16x109 DOF) • 2.0 million cpu-hours; 14,112 cores on Jaguar

  10. Temperature history in a spanwise slice reveals extinction and reignition (mm) (mm) (mm) (mm) (mm) (mm) Stoichiometric mixture fraction (black line)

  11. st st,  (1/s) Excessive mixing quenches the flame and flame reignites by premixed flame propagation 7000 300 2400 6000 T   200 2000 5000 100 4000 1600 Tst(K) Mean YCO2 surface Speed (cm/s) SD* SL 3000 0 1200 2000 -100 1000 800 0 -200 0 0.1 0.2 0.3 0.4 0.5 0.6 0.2 0.3 0.4 0.5 0.6 0.7 Time (ms) Time (ms) Conditional mean and variance of scalar dissipation rate (mixing rate) and temperature at the flame Propagation speed of ignition front compared with laminar flame speed

  12. Contacts Jacqueline H. Chen • Principal Investigator • Combustion Research Facility • Sandia National Laboratories • Livermore, CA 94550 • jhchen@sandia.gov • Ramanan Sankaran • Scientific Computing liaison • National Center for Computational Sciences • Oak Ridge National Laboratory • sankaranr@ornl.gov 12 Chen_S3D_SC07

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