Combustion Science Data Management NeedsCombustion Science Data Management Needs

Jacqueline H. Chen

Combustion Research Facility

Sandia National Laboratoriesjhchen@sandia.gov

DOE Data Management Workshop

SLAC

Stanford, CA

March 16-18, 2004

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 modelingChallenges in combustion understanding and modeling

Diesel Engine Autoignition, Laser IncandescenceChuck Mueller, Sandia National Laboratories

Stiffness: wide range of length and time scales

– turbulence

– flames and ignition fronts

– high pressure

Chemical complexity– large number of species and

reactions

Multi-physics complexity – multiphase (liquid spray, gas

phase, soot)

– thermal radiation

– acoustics ...

Direct Numerical Simulation (DNS) Approach

High-fidelity computer-based observations of micro-physics of chemistry-turbulence interactions

Resolve all relevant scales

At low error tolerances, high-order methods are more efficient

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

Complex chemistry - gas phase/heterogeneous (catalytic)

Turbulent methane-air diffusion flame

HO2

CH3O

CH4

O

Oxidizer

Fuel

. S3D0: F90 MPP 3D

. S3D1: GrACE-based

. S3D2: CCA-compliant

Software design developments

. IMEX ARK

. IBM

. AMR

Numerical developments

. Thermal radiation

. Soot particles

. Liquid droplets

Model developments

CFRFS

CCA

Post-processors: flamelet, statistical

CMCS DM

MPP S3D

Arnaud Trouvé, U. Maryland Jacqueline Chen, SandiaChris Rutland, U. WisconsinHong Im, U. MichiganR. Reddy and R. Gomez, PSC

High-fidelity Simulations of Turbulent Combustion (TSTC) http://scidac.psc.edu

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

A Computational Facility for Reacting Flow Science (CFRFS)A Computational Facility for Reacting Flow Science (CFRFS)

• Develop a flexible, maintainable, toolkit for high-fidelity Adaptive Mesh Refinement (AMR) Massively-Parallel low Mach number reacting flow computations

• Develop an associated CSP data analysis and reduction toolkit for multidimensional reacting flow

• Use CSP and a PRISM tabulation approach to enable adaptive chemistry reacting flow computations

– PRISM = Piecewise Reusable Implementation of Solution Mapping (M. Frenklach)

CCA GUI showing connections

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 oscillations

Objectives Objectives

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

deflagration

spontaneous ignition

detonation

Chen et al., submitted 2004, Sankaran et al., submitted 2004

Initial conditionsInitial conditions

x (cm)

y(c

m)

0 0.1 0.2 0.3 0.40

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

T (K)110711011096109110861080107510701065105910541049104310381033

Baseline symmetric case

x (cm)

y(c

m)

0 0.1 0.2 0.3 0.40

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

T (K)110110951090108410781072106610601054104810421036103010241018

Cold core gas

x (cm)

y(c

m)

0 0.1 0.2 0.3 0.40

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

T (K)111411091103109810931088108210771072106610611056105110451040

Hot core gas

Same mean T (1070K) Different T skewness and variance (15,30K) Pressure 41 – 55 atm Lean hydrogen/air

Temperature skewness effect on heat release rateTemperature skewness effect on heat release rate

Heat release, HighT, positive skewness

2.0 ms

2.4 ms

2.6 ms

2.8 ms

Symm Hot core Cold core

Temperature skewness effect on ignition delay and burn timeTemperature skewness effect on ignition delay and burn time

Temperature distribution influences ignition and duration of burning.

Hot core gas Ignited earlier Burns longer

Cold core gas Ignited later Slow end gas combustion

t [ms]In

teg

rate

dH

RR

[MW

/g]

1 1.5 2 2.5 3 3.5

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

BaselineHot coreCold core

DNS Cases Baseline Hot Cold

Burn time [ms] 0.894 1.085 0.953

Ignition front tracking methodIgnition front tracking method

cd

DtDss

od

*

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 surface

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

Species balance and normalized front speed criteria for propagation mode Species balance and normalized front speed criteria for propagation mode

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

A – deflagration B, C – spontaneous ignition

A C

B

Heat release isocontours

Fraction of front length and burnt gas area production due to deflagrationFraction of front length and burnt gas area production due to deflagration

•Solid line front length

•Dashed line – burnt area production

Comparison of experimental and DNS data for ignition/edge flame dataComparison of experimental and DNS data for ignition/edge flame data

H2 + O = OH + HO2 + H = O + OHslow OH recombination

RP

LP

DF

OH

H st

Normalized OH Expt

H2

Heated air

H2/N2

Normalized OH DNS Flow divergence effect – (Ruetsch et al. 1994) upstream divergence of flow due to increase in normal component of flow resulting from heat release

Curvature – preferential diffusion focusing effect at leading edge

Apriori testing of reaction models using DNS of turbulent jet flamesApriori testing of reaction models using DNS of turbulent jet flames

Sutherland et al., submitted 2004

CO/H2/air jet flame, scalar dissipation rate

Joint experiment/computation of turbulent premixed methane/air V-flameJoint experiment/computation of turbulent premixed methane/air V-flame

Stationary statistics required for turbulent premixed flame model development LES/RANS

Flame topology – curvature stretch statistics

Complex chemistry versus simple or tabulated chemistry (heat release, radicals, minor species)

Is preheat zone thickening due to small scales or higher curvatures in thin reaction zone regime? V-flame, expt. Renou 2003

and DNS, Vervisch 2003

Data management challenges for combustion scienceData management challenges for combustion science

• 2D complex chemistry simulations today: 200 restart files (x,y,Z1,…Z50) skeletal n-heptane 41 species, 2000x2000 grid, 1.6 Gbytes/time x200 files = 0.32 Tbyte, 5 runs in parametric study 1.6 Tbytes raw data

• Processed data: 2 Tbyte data

• 3D complex chemistry simulations in 5 years: 200 restart files (x,y,Z1,…Z50) skeletal n-heptane 41 species, 2000x2000x2000 grid, 3.2 Tbytes/time x 200 files = 640 Tbytes per run, 5 runs = 3.2 Petabytes raw data

• Processed data: 3 Petabytes

• Combustion regions of interest are spatially sparse

• Feature-borne analysis and redundant subsetting of data for storage

• Provenance of subsetted data

• Temporal analysis must be done on-the-fly

• Remote access to transport subsets of data for local analysis and viz.

FeaturesFeatures

• Feature is an overloaded word

• A feature in this context is a subset of the data grid that is interesting for some reason.

• Might call it a “Region of Interest” (ROI)

• Also might call it a “structure”

Why Feature Tracking?Why Feature Tracking?

• Reduce size of data– How do you find small ROI’s in a large 3D domain?

– Retrieve and analyze only what you need

• Provide quantification– Can exactly define ROI chosen & do specific statistics

• Enhance visualization– Can visualize features individually

– Can color code features

• Facilitate event searching– Events are feature interactions

Feature DetectionFeature Detection

• Detection = Identify features in each time step

• FDTOOLS tests each cell & groups connected ones

• There are many possible algorithms including pattern recognition

Feature TrackingFeature Tracking

• Tracking = Identify relationships between features in different time steps

• Again, there are many different algorithms, and knowing about how your features interact helps

EventsEvents

• Merge

• (Birth)

• (Death)

• Split

• Other domain specific events like hard-body collision, vorticity tube reconnect, etc. …

Design Goal: Flexible & ReusableDesign Goal: Flexible & Reusable

• Callable from running programs

• Independent of visualization package

• Modular– Detector plug-ins

– Tracker plug-ins

– Other plug-ins …

• CCA compatible

• Output interface for further analysis

DataSet TypesDataSet Types

fdRegular 2 & 3D of all

fdRefined structured(AMR)

FDTOOLS Design (Wendy Koegler SNL)FDTOOLS Design (Wendy Koegler SNL)

FDTOOLS Component

Director Feature Manager

Tracker

RepresenterDetector

Data Interface Output Interface

Analyzer

Visualizer

Detection and tracking of autoignition features

FDTools (Koegler, 2002): evolution of ignition features

Hydroperoxy mass fraction

Feature graph tracks evolution of ignition featuresFeature graph tracks evolution of ignition features

time

Feature-borne analysisFeature-borne analysis

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

2800

0 0.02 0.04 0.06 0.08 0.1Time (msec)

Max

Tem

pera

ture

(K) #5

#46#39#47

#45#40 #11

#18,#52#27

#41,#68

Ignition feature classificationIgnition feature classification

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.010

1090 1100 1110 1120 1130 1140 1150Average Temperature (K)

Ave

rage

. H2 C

onc.

(mas

s fr

acti

on)

extinquishes

consumed

ignites

Terascale virtual combustion analysis facilityTerascale virtual combustion analysis facility

Data management framework for combustion science – IData management framework for combustion science – I

• Distributed data mining tools: feature ID and tracking

• Distributed analysis tools operating on regions of interest– Reaction source term and Jacobian evaluation

– Conditional statistics

– Isolevel surface of multiply-connected 3D surfaces• Interpolate, integrate, differentiate in principle directions to surface

– Computational singular perturbation analysis

– Reaction flux analysis

– Principal component analysis

– Spectral analysis

Data management framework for combustion science – IIData management framework for combustion science – II

• Data objects, which interface to metadata and data– Enabling writing and reading data with various flexible formats

– Standard data formats

– Automatic conversion utilities

• Flexible, user-configurable, user-friendly GUI’s to enable user to specify desired operations on data

• General structured and unstructured adaptive mesh data

• Real-time feature-borne detection, tracking and analysis for computational steering (e.g. adaptive IO, temporal statistics)

Data management framework for combustion science – IIIData management framework for combustion science – III

• Distributed visualization tools scalar and non-scalar data

• Non-scalar data, i.e. vector or tensor• Heterogeneous data – combined experimental

and computational data• Iso-surface rendering and interpolating data

onto user-specified slices• Streamlines, information overlays• Uncertainty• Viz reduced-order representations of flow and

combustion features