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High Fidelity Numerical Simulations of Turbulent Combustion

High Fidelity Numerical Simulations of Turbulent Combustion. Ramanan Sankaran Evatt R. Hawkes Chunsang Yoo David O. Lignell Jacqueline H. Chen (PI) Combustion Research Facility Sandia National Laboratories, Livermore, CA. DNS Approach and Role

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High Fidelity Numerical Simulations of Turbulent Combustion

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  1. High Fidelity Numerical Simulationsof Turbulent Combustion Ramanan Sankaran Evatt R. Hawkes Chunsang Yoo David O. Lignell Jacqueline H. Chen (PI) Combustion Research Facility Sandia National Laboratories, Livermore, CA

  2. DNS Approach and Role Fully resolve all continuum scales without using sub-grid models Only a limited range of scales is computationally feasible Terascale computing = DNS with O(103) scales for cold flow. DNS is limited to small domains. Device-scale simulations are out of reach Turbulent Combustion involves coupled phenomena at a wide range of scales O(104) continuum scales Direct numerical simulation (DNS) of turbulent combustion Turbulent Combustionis a Grand Challenge Combustor size~1m Molecularreactions ~ 1nm

  3. S3D – Sandia’s DNS solver • Solves compressible reacting Navier-Stokes equations • High fidelity numerical methods • 8th order finite-difference • 4th order explicit RK integrator • Detailed chemistry andtransport 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

  4. S3D - Parallel Performance 90% parallel efficiency on 5000 XT3 processors

  5. DNS of lean premixed combustion • Goals • Better understanding of lean premixed combustionin natural-gas-based stationary gas turbines • Model validation and development • Simulation details • Detailed CH4/air chemistry; 18 degrees of freedom • Slot burner configuration with mean shear • Laboratory configuration and statistically stationary -Better suited for model development • A unique and first-of-its-kind DNS • Three different simulations at increasing turbulence intensities to clarify the effect of turbulent stirringon flame structure and burning speed

  6. Effect on flame structure Fresh 1/4 • Progress variable defined based on O2 mass fraction • Instantaneous slices shown on the left for Case 1 • Progress variable from 0 (blue) to 1 (red) in color • Heat release rate as line contours • Considerable influence on preheat zone • Reaction zone relatively intact Burned u’/SL=3 u’/SL=6 1/2 3/4

  7. Scalar dissipationrate in DNSof a jet flame DNS of extinction/reignition in a jet flame Hawkes, Sankaran, Sutherland, Chen - 2006 • Understanding extinction/reignition in non-premixed combustion is key to flame stability and emission control in aircraft and power producing gas-turbines • The largest ever simulations of combustion have been performedto advance this goal: • 500 million grid points • 11 species and 21 reactions • 16 DOF per grid point • 512 Cray X1E processors • 30 TB raw data • 2.5M hours on IBM SP NERSC (INCITE); 400K hours on Cray X1E (ORNL) Burning Extinguished

  8. Wall heat fluxes in turbulent flame wall interaction at low Reynolds number DNS of turbulent flame-wall interaction A. Gruber, R. Sankaran, E. R. Hawkes, and J. H. Chen - 2006 • DNS of a premixed flame interacting with turbulencein a wall-bounded channel flow • Detailed H2/Air chemistry (14 D.O.F.) • Inflow turbulence obtainedfrom a separate simulationof inert channel flow • Cost: 100k hours on X1E • Goals • Material failure from fluctuating thermal stress in micro-gas turbines • Study spatial and temporal patterns of wall heat flux

  9. Motivation Soot is a pollutant and lowers combustion efficiencies in dieselengines Soot radiation is the major heat transfer mode Goals Quantify soot formationand transport mechanismsin turbulent flames Approach DNS of turbulent sootingflames with detailedchemistry, transport,radiation 2–3 moment soot particlemodel with semi-empiricalsoot chemistry 3M cpu-hours on Cray XT3at ORNL to observe slowsoot processes Air Fuel Air fuel air DNS of non-premixed sooting ethylene flames (planned) D. O. Lignell, P. J. Smith, and J. H. Chen

  10. Goal: Determine stabilization mechanisms in lifted, auto-igniting flames relevant to efficient, clean burning low-temperature compression ignition engines and flashback in aircraft gas turbine engines DNS of stabilization in lifted auto-igniting flames (planned) • Hydrogen lifted flame • 300K hydrogen turbulent jet; 1100K aircoflow jet • Important submechanism for n-heptane • Comparison with experiment (Chung, Cabra) • Auto-ignition may occur below the base of the lifted flame due to high co-flow temperature • Approximately 1.2 million CPU hoursper simulation on Jaguar at NCCS • 9 species; 21 elementary reaction steps(Li et al. 2006) • 24x20x3.6 mm3 domain size; 1600x1000 x240grid resolution= ~400M grids • 4–6 flow-though times; Umean = 350 m/s • Total 3~4 simulations (~3.0 million cpu-hourson CrayXT4 at ORNL 2006–2007) Heat release (gold) and vorticityin a lifted autoigniting H2/airjet flame

  11. Contacts Jacqueline H. Chen • Combustion Research Facility • Sandia National Laboratories • Livermore, CA 94550 • jhchen@sandia.gov • Ramanan Sankaran • National Center for Computational Sciences • Oak Ridge National Laboratory • sankaranr@ornl.gov 11 Sankaran_Combustion_0611

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