Data Management Requirements: Computational Combustion and Astrophysics

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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

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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

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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

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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

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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

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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
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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

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Local flame speed analysis

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

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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
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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

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NO measurement contd
NO A-X(0,0) Excitation
NO-
NO A-X(0,2) Excitation
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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
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Type Ia Supernovae

• Thermonuclear explosion of C/O white dwarf.
• Brightness rivals that of host galaxy, L ¼ 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
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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
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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

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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

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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?

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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

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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