Post on 20-Jan-2021
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
XPACC: The Center for ExascaleSimulation of Plasma-CoupledCombustion
Jonathan B. Freund
Parallel Computing Institute (PCI)Mechanical Science & EngineeringAerospace EngineeringUniversity of Illinois
Oxydizer
O - Ar; Air2
Fuel
H ; CH2 4
Coaxial
DBD
Plasma
Pin-to-Pin Spark Discharge
Boundary Layer
Turbulence
Pipe-!ow Jet
Turbulence
E-"elds
A new DOE/NNSA-funded ASC PSAAP MSC Center
1
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
DOE/NNSA/ASC/PSAAP Motivation
I NNSA — DOE National Nuclear Security Administration
I ASC — Office of Advanced Simulation and Computing
“Established in 1995, the ASC Program supports the U.S. De-fense Programs’ shift in emphasis from test-based confidence tosimulation-based confidence”
I PSAAP — Predictive Science Academic Alliance Program
• Multidisciplinary Simulation Centers (MSC) (3 $3.2M/year)• Single-discipline Centers (SDC) (3 $1.6M/year)
“We expect the PSAAP alliances will continue to help developthe predictive science field and the workforce of the future,wherein simulations will be pervasive and instrumental in im-portant, high-impact decision-making processes” [emphasis added]
— Robert Meisner, director of NNSA ASC
2
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
XPACC MSC Specific Motivation
I Truly predictive simulations will accelerate fundamentaladvances in the use of plasmas to improve combustion
I Computer Science advances that enable such massive-scalepredictive simulations will have broad impact
3
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Specific Target Application
Oxydizer
O - Ar; Air2
Fuel
H ; CH2 4
Coaxial
DBD
Plasma
Pin-to-Pin Spark Discharge
Boundary Layer
Turbulence
Pipe-!ow Jet
Turbulence
E-"elds
I Predict the ignition threshold of a jet in crossflow viaspark-discharge and dielectric-barrier-discharge plasmas
I Iconical combustion flow with new ‘knobs’ for mediatingignition
4
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Coupled-Physics Predictive Science Challenge
I Plasmas introduce chemical-reaction-coupling radicals
I Plasma heating triggers chemistry
I Local heating alter flow/turbulence
I Electric fields alter diffusion of charged species
I Electric fields exert body forces, destabilizing flames
I Electrodes age and alter plasma generation efficacy
I Turbulence mixes reactants
I Flows alters plasma structure
I · · ·
5
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
A Multi-Scale Predictive Science Challenge
I LENGTH:
• nm-scale◦ electrode surface physics◦ mean-free-path reactions
• µm-scale◦ plasma structures◦ flame thickness
• mm-scale ◦ turbulence• m-scale ◦ device
I TIME:
• ns-scale◦ plasma breakdown◦ ion impacts
• µs-scale ◦ reaction kinetics• ms-scale ◦ mixing rates• s-to-∞ ◦ electrode aging
Length Scales
Time Scales
Electrode
Plasma
Flow/Flame
~ m - device
~ mm - turbulence
~ μm - flame thickness
~ μm - plasma sheaths
~ nm - chemical reactions
~ nm - electrode surface
~ s - electrode aging
~ ms - turbulence mixing
~ μs - plasma/reaction kinetics
~ ns - plasma breakdown
6
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
A Multi-Character Predictive Science Challenge
I Physics/math character ⇒ algorithms ⇒ resource utilization
I Hyperbolic
• Convection: Turbulent mixing
I Elliptic
• Diffusion: Small-scale,molecular mixing
• Electric field• Radiation
I Atomistic/Particle
• Electrode damagemechanisms
• Particle plasma models
I Quantum
• Electrode work functions
Electrode
Plasma
Flow/Flame
Diffusion
Atomistic
Quantum
Diffusion
Atomistic
E & M
Convection
Diffusion
Reaction
Convection
Radiation
7
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Extreme-Scale Computing Synopsis
I NOW
• Petascale resources: 1015 operations/sec• Homogeneous: nodes and network• Power hungry
I FORECAST (5+ years)
• Exascale resources: 1018 operations/sec• Ever greater concurrency• Heterogeneity
∗ more varied specialized processing elements∗ CPUs + GPU-like accelerators + · · ·∗ limited local memory
• Limited full-system bandwidth• More components: more potential faults• Input/Output (I/O): costly-to-impractical• Power management essential
8
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Use of Exascale (×103 Petascale)
I Vertical: 103 × more physics
• more scales• increasingly fundamental models• ⇒ robustness for extrapolation to true prediction
I Lateral: 103 × more simulations
• establish input–output statistics:model parameters → predictions
• quantified uncertainty adds predictive value
I Both lead to better Quantity of Interest (QoI) prediction
9
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
XPACC: An Exascale-Simulation Problem
I Models can couple reliably only if firmly grounded in physics
• calibrated sub-models do not guarantee accurate integration• represent coupled-physics in detail for low-uncertainty
prediction
I Representing physics means, in part, representing length andtime scales
• integrating physics broadens the scale range• not computationally additive: depends upon min/max of
represented scales
I For low uncertainty, integrating petascale sub-physics modelswill require exascale
I We will build toward exascale using reduced models, expectingsignificant-but-quantified initial uncertainty
10
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Exascale-Mapping Predictive Science Challenge
I Different physical models ⇒ different discretization ⇒different implementation challenges
I Numerics and implementation design to minimize overhead ofmapping onto forthcoming, heterogeneous exascale resources
{
{
Existing CS
MPI, OpenMP
conventional I/O
Needed CS
load balancing
fault tolerance
I/O management
data localization
heterogeneity tools
power awareness
Petascale System
(homogeneous)
Exascale System
(heterogeneous)
Length Scales
Time Scales
Electrode
Plasma
Flow/Flame
Diffusion
Atomistic
Quantum
Diffusion
Atomistic
E & M
Convection
Diffusion
Reaction
Convection
Radiation
GLOBAL
LOCAL
VECTOR
SCALAR
CPU Suited
GPU Suited
Custom
hardware
~ m - device
~ mm - turbulence
~ μm - flame thickness
~ μm - plasma sheaths
~ nm - chemical reactions
~ nm - electrode surface
~ s - electrode aging
~ ms - turbulence mixing
~ μs - plasma/reaction kinetics
~ ns - plasma breakdown
I General, portable CS advances are necessary to complete thismapping
11
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Exascale: Criteria of Success
I 5-years outlook: trans-petascale/pre-exascale
I Metrics of success toward exascale (and beyond):
• scale well to levels of concurrency expected in exascale systems• scale well in the presence of performance irregularity in
computing elements and network• perform well, including operations per Joule, with more
specialized computing elements and less hardware support• produce correct results even in the presence of faults• tools must not hinder physics modeling development and
simulation
12
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
The Team
13
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
The Team
Principal Investigator/CS Lead
W. Gropp (CS/PCI)
Application Lead
J. Freund (MechSE/AE)
Computer Science
W. Hwu (ECE/PCI)
S. Kale (CS)
D. Padua (CS)
[A. Kloeckner (CS)]
Application Simulations
H. Johnson (MechSE)
C. Pantano (MechSE)
[M. Panesi (AE)]
Experimental Lead
G. Elliott (AE)
Application Experiments
I. Adamovich (MAE at OSU)
N. Glumac (MechSE)
W. Lempert (Chem, MAE at OSU)
External Advisory Board
Campbell Carter (AFRL)
Andrew Chien (UChicago)
Jack Dongarra (UTenn/ORNL)
Max Gunzburger (FSU)
Bob Moser (UT Austin)
Elaine Oran (ONRL)
Chief Software Architect
D. Bodony (AE)
Hardware-Algorithms Integration
Lead: L. Olson (CS)
Executive Committee
14
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Specific Configuration
Oxidizer
O - Ar; Air2
Fuel
H ; CH2 4Coaxial DBD Plasma
• Combustion radicals
• Minor heating
Pin-to-Pin Spark Discharge:
• Combustion radicals
• Localized intense heating
Boundary Layer
Turbulence
Pipe-!ow Jet
Turbulence
E-"elds:
• Body forces
• Biased di#usion (in !ames)
I Fuel jet (5 mm; H2, CH4) in cross-flow (50 m/s; O2-Ar, air)
• A canonical configuration• Represents broad class of application configurations
I Plasmas near exit mediate ignition/combustion
• Spark discharge: radicals and thermal• Dielectric barrier discharge: primarily non-thermal via kinetics
I Operate in coupled-physics regime:plasma essential for ignition
15
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Plasma Regime
I Collisional
I Non-thermal: dielectric barrier andnanosecond pulse discharges
I Weakly-thermal/thermal: microsecond pulsedischarges
I Temperatures: Tn/i ≈ 300− 3000 K;∼ 1 to 10 eV electrons
I Pressure:
• low (∼ 50 Torr) in some calibrationexperiments
• higher (1 atm) in target application(practical, rich phenomenology)
I Non-magnetic: Larmor radius � mean freepath (rg = v⊥/ωg � λ)
• Electrostatic: Poisson equation∇ · (ε∇)φ = −e(Zini − ne)
Coaxial DBD Plasma • Combustion radicals
• Minor heating
Pin-to-Pin Spark Discharge: • Combustion radicals
• Localized intense heating
Boundary Layer
Turbulence
Pipe-!ow Jet
Turbulence
E-"elds: • Body forces
• Biased di#usion (in !ames)
16
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Specific Prediction
Bu
rnN
o B
urn
Burn/No-Burn
Boundary
Parameter Uncertainty
PDFs from physics-targeted
simple-geometry MC assessment
rnN
o B
uN
“Find” Burn/No-Burn Boundary
Identify Key Uncertainties
Adjoint: Gradient
I Predict ignition with uncertainty estimate for
• Flow conditions (jet and crossflow)• Spark operation (kV, duration, rate)• DBD operation (kV, rate)
17
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Current Status
}Compressible flow
LES: Smagorinsky SGS
Hard-coded reduced H - air chemistry
Thermomechanically coupled “wall”
Crude heat/radical sources
2START
18
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Current Flow Model
(Bodony, Freund)
I Compressible Navier–Stokes
I Reactive mixtures of thermally perfect gases
I Temperature-dependent specific heats
∂
∂t
(Q
J
)+∂F
∂ξ+∂G
∂η+∂H
∂ζ=
S
J
I Solution Q; fluxes {F ,G ,H}; mapping Jacobian JI S source:
• sub-grid-scale models (large-eddy simulation, ...)• chemical/plasma reactions (...)• body forces on charged species (...)• radiation ‘sink’ (...)
I Coupled with thermo-mechanical (electrode) model
I Validated in hypersonic aerodynamics applications
19
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Year 0 Demonstration Simulation
Air free-stream Mach number M∞ 0.5H2 jet Mach number Mj 0.5Jet diameter d 5 mmBoundary layer δ99 5 mmReynolds number Re = ρ∞U∞d/µ∞ 46,000
air
H2
Mesh 1: bound. layer 501× 106× 401Mesh 2: r ≈ 0 81× 306× 81Mesh 3: cylindrical 161× 91× 306
No plasma, reaction, electrodes, E -fields, ...
20
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Model Development & Integration(Pantano, Adamovich, Bodony, Freund, Johnson, Panesi)
OSU Plasma
Combustion Kinetics
DD-LTE-LEE Plasma
DT Plasma
DS Plasma ( f )
PMC Plasma
Calibrated
2-Reaction Model
SGS Plasma
Transport Models
SGS Combustion/
Mixing ModelsCalibrated Phenomenological
Electrode Aging Model
Physics-based
Electrode Aging Model
DFT Work
Functions Model Refinement
(and De-refinement)
to Target QoI
Uncertainty
Y0}
Compressible flow
LES: Smagorinsky SGS
Hard-coded reduced H - air chemistry
Thermomechanically coupled “wall”
Crude heat/radical sources
2
Y1: QoI
Y2: QoI
Y3: QoI
Y4: QoI
Y5: QoI
START
21
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Spark Discharge Electrodes: Aging
I Spark characteristics depends upon surface details of electrode
I Electrodes age:
New 30 Minutes 1 hour
I True predictions will require aging models
I Ion impacts alter mechanical and electronic properties of theelectrode surface
• Roughness focuses electric field, altering ions impacts• Damage alters electron work functions, affecting efficacy
22
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Physics-Based Electrode Aging Model(Johnson, Freund)
I Generalize our time-scale bridging crater-function model
• MD ion bombardment• Statistical response used in continuum model• Reproduced ripple-like surface patterns on Si
MD: Ion Bombardment
+Slow
Surface
Evolution
Electrostatics/
E-!eldion trajectories,
surface shape
Density Functional
Theory
Work Functions
unit process/
crater function
E-!eld coupled
Electronic/First-principles Atomic/Mechanical Continuum
INPUT: Electrode material, geometry, voltage, current
OUTPUT: Plasma condition over long damage timescales
damage
morphology
23
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Physics-Based Electrode Aging Model(Johnson, Freund)
I Add:
• Electrostatic alteration oftrajectories (build on AFM tipsharpening effort)
• DFT calculations of electron workfunctions
∗ used extensively to quantifyelectronic properties of defectsand nanostructures:Johnson et al.
I Close interaction with experiments
• Physics-targeted experiments onelectrode aging and its effects
• Diagnostics of electrode surfaces(e.g. MRL facilities)
I Build into ‘boundary condition’ forapplication simulation
24
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Uncertainty Quantification (UQ): StrategyI Quantified uncertainty increases predictive value
I Quality data and two-way interaction with experiments
• local experiments designed and refined for UQ objective• low-cost, physics-targeted• expert quantitative assessment of measurement uncertainties
I Thorough calibration
• Bayesian inference and sampling• fast, low-dimensional, physics-targeted simulations• develop and employ model-inadequacy models• calibrate models for aleatoric uncertainty (spark discharges)
I Dimensionality reduction
• sensitivity: remove unimportant uncertain parameter• speed: select fidelity for overall QoI uncertainty goals• speed: seek surrogates that respect burn/no-burn boundary
I True QoI prediction
• with quantified uncertainty• compared predictively against experiment
25
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
UQ: Two-Stage Approach
I Simple configuration, physics-targeted calibration experiments
• published experimental results• designed to emphasize particular physical interactions• designed for specific modeling/simulation needs
I Uncertainty propagation to full-system predictions
26
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Physics-Targeted Calibration Experiments(Elliott, Adamovich, Glumac, Lempert)
Quasi-0D
Plasma-Induced IgnitionUse: plasma models, plasma-coupled ignition ki-netics Status: diagnostic capabilities and flowchamber up and running at OSU (Adamovich,Lempert)
27
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Physics-Targeted Calibration Experiments(Elliott, Adamovich, Glumac, Lempert)
Quasi-1D
Plasma–PremixedUse: plasma ignition validation, quantificationof electrode aging and effect, aleatoric modelingof sparks Status: inert analog up and runningat Illinois (Elliott, Glumac)
Counterflow Diffusion Flame
+
gnd
Use: kinetics models and effects of aug-mented radical transport; Status: stagnationflow flat-flame variation up and running at OSU(Adamovich, Lempert)
28
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Physics-Targeted Calibration Experiments(Elliott, Adamovich, Glumac, Lempert)
Quasi-2D
Plasma–Inert FluidUse: plasma–flow coupling, quantification ofelectrode aging and effect; aleatoric model ofsparks Status: zero-flow version up and runningat Illinois (Elliott, Glumac)
Plasma-Induced Ignition & PropagationUse: flame propagation predictions; Status: di-agnostic capabilities and flow chamber up andrunning at OSU (Adamovich, Lempert)
29
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Application / Validation Experiments(Elliott, Adamovich, Glumac, Lempert)
Fully 3D
Plasma-coupled Burning Turbulent JetUse: validation of ignition threshold predic-tion (and other predictions) Status: tunnel andnecessary diagnostics available at Illinois (El-liott,Glumac)
30
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Overall Roadmap
Year 3 Year 4Year 2Year 1 Year 5
+
gnd
Prediction UQCalibration UQ
Increasing Sampling
Single Simulation As Physics Models/UQ Necessitates
Ignition (~0D)
Flame (~2D)
Flame kinetics
Ignition kinetics
Spark-flow coupling
Electrode aging
E-field forces/diffusion
Flame propagation/ stability
Complex geometry/turbulence
~0D
~1D
~2D
3D
Data tiling
Over-decompositio
n
GGCG
G G
GM
GPU
accelera
tion
Source-to-source
Load balancing
?
??
??
?
Model Fidelity
GeometricComplexity
Terascale
Petascale
Exascale
}}}
31
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
UQ: Calibration
(Freund, Panesi)
I Bayesian inference
• low-dimensional, physics-targeted experiments• fast Monte Carlo parameter calibration simulations
I Priors: select to reflect expectations without restricting range
I Include model-inadequacy model calibration(e.g. Kennedy & O’Hagan, 2001; Moser et al. 2012)
32
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
QoI Uncertainty
(Freund, Panesi)
I QoI: ignition threshold
• find using adjoint-based gradient
I Propagate ~p and ~σ uncertainty toapplication predictions
I Adjoint-based sensitivity estimateof impact of uncertain parameterson QoI
σα∂J
∂pα• Eliminate unimportant
parametric uncertainties• Reduce dimensionality
Bu
rnN
o B
urn
Burn/No-Burn
Boundary
Parameter Uncertainty
PDFs from physics-targeted
simple-geometry MC assessment
rnN
o B
uN
“Find” Burn/No-Burn Boundary
Identify Key Uncertainties
Adjoint: Gradient
I QoI uncertainty: quadrature in uncertainty space
• start: sparse, adaptive, MC• quantify dependencies and seek surrogates, GP, ...
33
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Adjoint-Based Sensitivity
(Freund, Bodony, Padua)
I Building on experience withadjoint-based control of unsteady,compressible, free shear flows for noisecontrol (AFOSR)
• Freund et al. (2003,...,2011)• Freund & Bodony et al. (2009,...)
to ControlSensitivity
Flow Solution
Noise
SolutionAdjoint
Time
Aco
ust
ic P
ow
er
0 10 20 30 40 50 60 70 80 902
4
6
8
10
12
Before ControlWith Control
Mach 1.3, Turbulent Jet:
10− 11 10− 10 10− 9 10− 8 10− 7 10− 6 10− 5 10− 4 10− 3 10− 210− 5
10− 4
10− 3
10− 2
10− 1
100
101
Discretized AnalyticFully Discrete
Distance in Control Space
Err
or:
Ad
join
t m
inu
s F.
D.
Numerical Precision Failure
(con!rmed via -real16)
I Developed readily coded exact discreteadjoint for flow-like transport
I Considering a compiler-based approachfor general discrete adjoint (Padua)
34
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Adjoint-Based Sensitivity
(Freund, Bodony, Padua)
I Building on experience withadjoint-based control of unsteady,compressible, free shear flows for noisecontrol (AFOSR)
• Freund et al. (2003,...,2011)• Freund & Bodony et al. (2009,...)
to ControlSensitivity
Flow Solution
Noise
SolutionAdjoint
Time
Aco
ust
ic P
ow
er
0 10 20 30 40 50 60 70 80 902
4
6
8
10
12
Before ControlWith Control
Mach 1.3, Turbulent Jet:
10− 11 10− 10 10− 9 10− 8 10− 7 10− 6 10− 5 10− 4 10− 3 10− 210− 5
10− 4
10− 3
10− 2
10− 1
100
101
Discretized AnalyticFully Discrete
Distance in Control Space
Err
or:
Ad
join
t m
inu
s F.
D.
Numerical Precision Failure
(con!rmed via -real16)
I Developed readily coded exact discreteadjoint for flow-like transport
I Considering a compiler-based approachfor general discrete adjoint (Padua)
34
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Example: XPACC in 1D(thanks David Buchta)
I Goal: predict ignition (temperature) versus plasma strength
• Radical addition (R)• Plasma heating (T )
I Physics-targeted experiments → a higher-fidelity model
• Reduced H2–air mechanism of Boivin et al. (2011)• Based on more detailed 21-reaction, 8-species description
35
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Example: XPACC in 1D(thanks David Buchta)
I Goal: predict ignition (temperature) versus plasma strength
• Radical addition (R)• Plasma heating (T )
I Physics-targeted experiments → a higher-fidelity model
• Reduced H2–air mechanism of Boivin et al. (2011)• Based on more detailed 21-reaction, 8-species description
2 2.2 2.4 2.6 2.8 3
-9
-8
-7
-6
-5
-4
-3
3000.0
2472.4
2037.5
1679.2
1383.9
1140.5
939.9
774.6
638.4
526.1
433.6
357.3
294.5
242.7
200.0
3000K
1000K
200K
500K
2000K
35
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Example: XPACC in 1D(thanks David Buchta)
I Goal: predict ignition (temperature) versus plasma strength
• Radical addition (R)• Plasma heating (T )
I Physics-targeted experiments → a higher-fidelity model
• Reduced H2–air mechanism of Boivin et al. (2011)• Based on more detailed 21-reaction, 8-species description
I Calibrate simple model:
I : F + O → P + R
II : R + M → F + M
kI = AITnI e−T/T a
I kII = AIITnII e−T/T a
II
35
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Example: XPACC in 1D
yk — T (t = 125 µs) 2-reaction trial predictionsdk — T (t = 125 µs) Boivin et al. ‘data’T i
k — initial temperature for datapoint kR i
k — initial radical concentration (atomic-H in Boivin model)k — samples: k = 1, ...,N = 25 in log R i–log T i plane
I Likelihood:
P({yk − dk}|θ, {Rk ,Tk}, I ) =1
σ√
2πexp
[−∑
(yk − dk)2
2σ2
]I Calibrate θ = {AI ,AII , nI , nII ,T
aI ,T
aII , σ} using Bayesian
inference via MCMC
36
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Calibration
37
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Prediction
2 2.2 2.4 2.6 2.8 3
-9
-8
-7
-6
-5
-4
-3
3000.0
2472.4
2037.5
1679.2
1383.9
1140.5
939.9
774.6
638.4
526.1
433.6
357.3
294.5
242.7
200.0
3000K
1000K
200K
500K
2000K
0 1000 2000 30000
0.002
0.004
0.006
0.008
0.01
0.012
38
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Prediction
2 2.2 2.4 2.6 2.8 3
-9
-8
-7
-6
-5
-4
-3
3000.0
2472.4
2037.5
1679.2
1383.9
1140.5
939.9
774.6
638.4
526.1
433.6
357.3
294.5
242.7
200.0
3000K
1000K
200K
500K
2000K
0 1000 2000 30000
0.002
0.004
0.006
0.008
0.01
0.012
38
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Prediction
2 2.2 2.4 2.6 2.8 3
-9
-8
-7
-6
-5
-4
-3
3000.0
2472.4
2037.5
1679.2
1383.9
1140.5
939.9
774.6
638.4
526.1
433.6
357.3
294.5
242.7
200.0
3000K
1000K
200K
500K
2000K
0 1000 2000 30000
0.002
0.004
0.006
0.008
0.01
0.012
I Challenge at burn/no-burn boundaryI Inadequate model-inadequacy model?
• single σ value• biased toward many ‘easy’ predictions away from threshold
I Calibration on joint-PDF (not marginals) essential
• Marginal PDF yields erroneous, broad, bi-model T -predictiondue to correlations (e.g. nI –TI )
38
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Overall Approach Summary
I Data with any associated uncertainty
I Candidate physical model
I Inadequacy model
I Calibrate parameters of bothI Identify important uncertainties for QoI
• E.g. 0-D calibration → 1-D prediction sensitivities:
Ti = 447 K Ri = 10−3 σinad. = 48.2
σnI
∂J
∂nI= 20.1 σTI
∂J
∂TI= −33.7 σAI
∂J
∂AI= 6.8
σnII
∂J
∂nII= 11.1 σTII
∂J
∂TII= −16.8 σAII
∂J
∂AII= 0.8
I Propagate important uncertainty to QoI
I Validation: QoI predicted within combined predicted and dataQoI data uncertainty
39
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Summary
I Start date.... Jan 1, 2014...
maybe... hopefully
• looking for personnel
I XPACC is simulation based....predictive science enabled by exascale
• bigger simulations: physics faithful across scales• more simulations: rigorously integrated for UQ• ∼ half effort on CS research for utilization of exascale
I Experiments are essential
• within XPACC: for calibration and QoI validation• beyond XPACC: foundational studies will continue to guide
design and integration of models (e.g. AFOSR PAC MURI!)
I XPACC aims to impact combustion beyond its principalPSAAP Predictive-Science goals
40
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Summary
I Start date.... Jan 1, 2014... maybe...
hopefully
• looking for personnel
I XPACC is simulation based....predictive science enabled by exascale
• bigger simulations: physics faithful across scales• more simulations: rigorously integrated for UQ• ∼ half effort on CS research for utilization of exascale
I Experiments are essential
• within XPACC: for calibration and QoI validation• beyond XPACC: foundational studies will continue to guide
design and integration of models (e.g. AFOSR PAC MURI!)
I XPACC aims to impact combustion beyond its principalPSAAP Predictive-Science goals
40
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Summary
I Start date.... Jan 1, 2014... maybe... hopefully
• looking for personnel
I XPACC is simulation based....predictive science enabled by exascale
• bigger simulations: physics faithful across scales• more simulations: rigorously integrated for UQ• ∼ half effort on CS research for utilization of exascale
I Experiments are essential
• within XPACC: for calibration and QoI validation• beyond XPACC: foundational studies will continue to guide
design and integration of models (e.g. AFOSR PAC MURI!)
I XPACC aims to impact combustion beyond its principalPSAAP Predictive-Science goals
40
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Summary
I Start date.... Jan 1, 2014... maybe... hopefully
• looking for personnel
I XPACC is simulation based....predictive science enabled by exascale
• bigger simulations: physics faithful across scales• more simulations: rigorously integrated for UQ• ∼ half effort on CS research for utilization of exascale
I Experiments are essential
• within XPACC: for calibration and QoI validation• beyond XPACC: foundational studies will continue to guide
design and integration of models (e.g. AFOSR PAC MURI!)
I XPACC aims to impact combustion beyond its principalPSAAP Predictive-Science goals
40
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Summary
I Start date.... Jan 1, 2014... maybe... hopefully
• looking for personnel
I XPACC is simulation based....predictive science enabled by exascale
• bigger simulations: physics faithful across scales• more simulations: rigorously integrated for UQ• ∼ half effort on CS research for utilization of exascale
I Experiments are essential
• within XPACC: for calibration and QoI validation• beyond XPACC: foundational studies will continue to guide
design and integration of models (e.g. AFOSR PAC MURI!)
I XPACC aims to impact combustion beyond its principalPSAAP Predictive-Science goals
40
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Summary
I Start date.... Jan 1, 2014... maybe... hopefully
• looking for personnel
I XPACC is simulation based....predictive science enabled by exascale
• bigger simulations: physics faithful across scales• more simulations: rigorously integrated for UQ• ∼ half effort on CS research for utilization of exascale
I Experiments are essential
• within XPACC: for calibration and QoI validation• beyond XPACC: foundational studies will continue to guide
design and integration of models (e.g. AFOSR PAC MURI!)
I XPACC aims to impact combustion beyond its principalPSAAP Predictive-Science goals
40
PCIParallel ComputingInstitute
CSE Computational Science & Engineering XPACC
Center for Exascale Simulation
of Plasma-Coupled Combustion
Summary
I Start date.... Jan 1, 2014... maybe... hopefully
• looking for personnel
I XPACC is simulation based....predictive science enabled by exascale
• bigger simulations: physics faithful across scales• more simulations: rigorously integrated for UQ• ∼ half effort on CS research for utilization of exascale
I Experiments are essential
• within XPACC: for calibration and QoI validation• beyond XPACC: foundational studies will continue to guide
design and integration of models (e.g. AFOSR PAC MURI!)
I XPACC aims to impact combustion beyond its principalPSAAP Predictive-Science goals
40