Data Management Requirements: Computational Combustion and Astrophysics - PowerPoint PPT Presentation

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Data Management Requirements: Computational Combustion and Astrophysics

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Title: Data Management Requirements: Computational Combustion and Astrophysics


1
Data Management Requirements Computational
Combustion and Astrophysics
  • John Bell
  • Center for Computational Sciences and Engineering
  • Lawrence Berkeley National Laboratory
  • jbbell_at_lbl.gov
  • http//seesar.lbl.gov
  • December 11, 2006

2
What we are trying to do
  • Combustion
  • Detailed analysis of premixed turbulent
    combustion
  • Lean premixed systems have potentially
    high-efficiency and low emissions
  • Design issues because premixed flames are
    inherently unstable
  • Astrophysics
  • Simulate white dwarf from convection through
    explosion
  • Type Ia supernovae are play a key role in modern
    cosmology but the explosion mechanism is not
    understood

3
Computational approach
  • Components of a computational model
  • Mathematical model describing the science in a
    way that is amenable to representation in a
    computer simulation
  • Approximation / discretization approximating an
    infinite number of degrees of freedom with a
    finite number
  • Solvers and software developing algorithms for
    solving the discrete approximation efficiently on
    high-end architecture
  • We attempt to exploit the special structure of
    the problems we are considering to compute more
    efficiently

4
Adaptive Mesh Refinement
  • Spatial discretization should exploit locality
  • Structured adaptive mesh refinement
  • Hierarchical patches of data
  • Dynamically created and destroyed
  • Combination of new numerical methodologies
    reduces computational effort by several orders of
    magnitude

5
V-flame
  • Simulate turbulent V-flame
  • Strategy Independently characterize nozzle and
    specify boundary conditions at nozzle exit
  • 12 12 12 cm domain
  • Methane at f 0.7
  • DRM 19, 20 species, 84 reactions
  • Mixture model for species diffusion
  • Mean inflow of 3 m/s
  • Turbulent inflow
  • lt 3.5mm, u' 0.18 m/sec
  • Estimated h 220 m m
  • No flow condition to model rod
  • Weak co-flow of air

6
Experimental comparisons
Simulation
Experiment
Instantaneous flame surface animation
Joint with M. Day, J. Grcar, M. Lijewski, R.
Cheng, M. Johnson and I.
Shepherd, PNAS, 2005
Flame brush comparisons
7
Thermo-diffusive Effects
  • Low swirl burner flames for different fuels
  • Experiments focused on effect of different fuels
    on flame behavior
  • Identical fueling rate and turbulence
  • Nearly the same stabilization nearly
    the same turbulent burning speed

8
Local flame speed analysis
  • Construct local coordinate system around flame
    and integrate reaction data
  • Other mathematical analysis paradigms
  • Stochastic particles
  • Pathline analysis

9
Diffusion flames
  • Study behavior of fuel bound nitrogen
    characteristic of biomass fuels
  • What do experimentalists measure
  • Exhaust gas composition
  • Planar laser-induced fluorescence
  • Temperature
  • NO concentration
  • NO measurements
  • Illuminate flame with a tuned laser sheet
  • NO absorbs a photon
  • Measure emission
  • Problem NO can lose photon in a collision
    before it is emitted -- Quenching

Joint with P. Glarborg, A. Jensen, W.
Bessler, C. Schulz
10
NO measurement
  • Quenching requires knowledge of local composition
    and temperature
  • fB,i Boltzmann population term
  • gl,i Linear shape profile
  • Qk(p,T,X) Electronic quenching
  • Experimentalists typically guess the composition
    for quenching correction
  • Generate synthetic PLIF images from simulation

11
NO measurement contd
NO A-X(0,0) Excitation
NO-
NO A-X(0,2) Excitation
12
NO Contd
  • Can use simulation data to compute quenching
    correction to experimental data
  • Simulation also provides a more detailed picture
    of nitrogen chemistry
  • Reaction path gives quantitative picture chemical
    behavior of the system

Proc. Comb. Inst., 2002
13
Type Ia Supernovae
  • Thermonuclear explosion of C/O white dwarf.
  • Brightness rivals that of host galaxy, L ΒΌ 1043
    erg / s
  • Large amounts of
  • Radioactivity powers the light curve
  • Light curve is robust
  • Standard candle in determining the expansion of
    the universe

SN 1994D
Computational Astrophysics Consortium Adaptive
Methods
14
Astrophysics issues
  • . . . Are about the same
  • Specialized treatment of fluid mechanics
  • Chemistry -gt Nuclear physics
  • Complex diffusive transport -gt radiation
  • Simulations
  • Common software framework
  • AMR

Turbulent Spectrum
Astrophysical Journal, 2006
15
Workflow
  • How do we extract science from the simulation
    data
  • We typically dont do visual analysis of the raw
    data
  • Our analyses typically start with some
    mathematical transformation of the data but , .
    . . to leading order, we cant a priori define
    what this means
  • I cant define requirements
  • Typically, we will do some prototype as part of
    defining what we want to look at
  • Data analysis tool needs to be able to ingest
    application specific information
  • Data analysis tool requirements
  • Work with hierarchical data
  • Incorporate problem physics
  • Hardened version of prototype tools
  • Integrated with visualization

16
Data management
  • How does the data flow through this process
  • Run simulation
  • Dump data in plotfiles (reasonable I/O)
  • Tar plotfiles and archive in mass storage
  • We only store data at end of coarse steps, and
    maybe not each coarse time step
  • Analyze data
  • Pull data from mass storage
  • Move data to analysis platform
  • Analysis must be done in parallel
  • Machine for computation may not be good for
    analysis
  • Untar plotfile data
  • Run analysis program (reasonable I/O demand
    driven)
  • This fundamentally does not scale
  • Have to move everything from mass storage to
    rotating disk
  • Need to shift data to appropriate platforms

17
I/O
  • How do we do I/O?
  • AMR Plotfiles
  • Model used to be each processor writes to disk
  • Level x Processors files
  • Now,
  • Identify number of desired channels
  • Tell processors when it is their turn to write
  • Level x desired channels files
  • Is this scalable? Will something work better?

18
Visualization / Analytics
  • AMRVIS?D
  • Reads plotfiles directly
  • Main visualization is slices through data
  • Limited functionality of contouring, vector
    fields, volume rendering
  • Supports a data spreadsheet capability
  • Fancier visualization done with TECPLOT
  • Not particularly scalable
  • Adopt VISIT as principal vis tool
  • Relation to AMRVIS replace? need spreadsheet
    capability
  • What functionality is missing

19
Requirements
  • Data management cartoon
  • Simulate at ORNL make large tar files
  • Move data to NERSC archive (manually)
  • Iterate on
  • Pull data from archive on DaVinci (manually)
  • Run analysis programs (manually)
  • We need to develop a schema to facilitate
    analysis without so much manual data movement
  • Automate data transfer
  • Archive data so we only read what we need and can
    stage retrieving data from storage
  • Automate processing large amount of data once
    prototypes are operational
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