PowerPoint Presentation - SDM group at LBNL

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					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

                                                    Stiffness: wide range of
                                                      length and time scales
                                                       –   turbulence
                                                       –   flames and ignition fronts
                                                       –   high pressure
                                                    Chemical complexity
                                                       –   large number of species and
                                                    Multi-physics complexity
                                                       –   multiphase (liquid spray, gas
                                                           phase, soot)
                                                       –   thermal radiation
                                                       –   acoustics ...

 Diesel Engine Autoignition, Laser Incandescence
 Chuck Mueller, Sandia National Laboratories
Direct Numerical Simulation (DNS) Approach

 High-fidelity computer-based          Turbulent methane-air diffusion flame
  observations of micro-physics of
  chemistry-turbulence interactions
 Resolve all relevant scales

 At low error tolerances, high-order
  methods are more efficient
                                                    HO2              CH4

 Laboratory scale configurations:
  homogeneous turbulence, v-flame
  turbulent jets, counterflow

 Complex chemistry - gas                          CH3O               O
  phase/heterogeneous (catalytic)
                  High-fidelity Simulations of Turbulent
                  Combustion (TSTC) http://scidac.psc.edu

       Software design developments              Numerical developments
       . S3D0: F90 MPP 3D                        . IMEX ARK
       . S3D1: GrACE-based                       . IBM
       . S3D2: CCA-compliant                     . AMR

CCA                                              Model developments
                                                 . Thermal radiation
                               MPP S3D           . Soot particles
                                                 . Liquid droplets
 DM                                         Arnaud Trouvé, U. Maryland
                                            Jacqueline Chen, Sandia
       Post-processors:                     Chris Rutland, U. Wisconsin
       flamelet, statistical                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
 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

              Volume Rendering by Kwan-Liu Ma

  Vorticity magnitude              OH mass fraction
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
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.

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
        spontaneous ignition
  Temperature skewness effect on heat release

         Symm   Hot core Cold core

2.0 ms

2.4 ms

2.6 ms

2.8 ms                               Heat release, HighT, positive skewness
Ignition front tracking method

Density-weighted displacement speed (Echekki and Chen, 1999):

                                DDt
          sd  s    *
                                             c
                                o 

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
Species balance and normalized front speed
criteria for propagation mode

  Heat release isocontours          A                   C


Black lines – s*d/sL < 1.1 (deflagration)
White lines – s*d/sL > 1.1 (spontaneous ignition)

A – deflagration B, C – spontaneous ignition

  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 features

Feature-borne analysis

                           2600                                 #47     #39
                           2400                                           #5
     Max Temperature (K)

                           1000                                  #18,#52
                                             #40 #27             #11
                                  0   0.02    0.04       0.06         0.08      0.1
                                                Time (msec)
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
       – Conditional statistics
       – Reduced representations of combustion data (POD, PCA,
          topology of vector and scalar fields) for model development and
       – 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
  •   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).

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 Resources

   PNL – Linux cluster
   SNL – Intel Linux cluster, SGI Origin, Compaq
   Sierra Cluster

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