Transcript Fast, pre-computed modeling blocks for facilitating timely

Evaluation of Emission Control Strategies
for Regional Scale Air Quality:
Performance of Direct and Surrogate Techniques
Presented at the 6th Annual CMAS Conference
Friday Center, UNC-Chapel Hill
October 1-3, 2007
S. Isukapalli, S. Wang, S. Napalenok*
T. Kindap, and P. Georgopoulos
Computational Chemodynamics Laboratory (CCL)
Environmental and Occupational Health Sciences Institute (EOHSI)
A Joint Institute of UMDNJ-RW Johnson Medical School and Rutgers University
170 Frelinghuysen Road, Piscataway, NJ 08854
*Atmospheric Sciences Modeling Division, Air Resources Laboratory, NOAA
in partnership with the USEPA
Acknowledgments
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Alper Unal, WRI
Talat Odman and Yongtao Hu, Georgia Institute of Technology
USEPA (Funding for Center for Exposure and Risk Modeling)
NJDEP (Base funding for the Ozone Research Center)
• OTC Modeling Group Centers (Emissions inventories, Meteorology, etc.)
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Overview
• Surrogate Modeling Techniques
• HDMR
• DDM
• Automatic Differentiation
• Response Surface Modeling
• Case Study
• Emissions and Regions
• Estimates vs Brute Force
• Results and Discussion
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Use of surrogate models for emission control analysis
• Emissions control analysis is a multi-dimensional problem
• Geographic regions [states/counties, etc.]
• Types of emissions [point, biogenic, area, mobile, etc.]
• Primary emissions [NOx, VOC, etc.]
• Some times, multi-objective problems
• Ozone, PM2.5, etc.
• Direct model simulation is expensive
• 2 hours/day for OTC-12 domain simulation (8 Opteron nodes)
• Surrogate models can provide significant speedups
• Construction of surrogate models is often parallelizable
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Use of surrogate models for emission control analysis
• Can provide a “Fast Equivalent Operational Model” (FEOM)
• Can also be used in Uncertainty Propagation
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Use of surrogate models for emission control analysis
Several techniques exist for surrogate modeling
• Response Surface Methods (Deterministic and Stochastic)
• High Dimensional Model Representations (HDMR)
• Local Gradient-Based Methods
• Decoupled Direct Method (DDM)
• Adjoint Sensitivity Analysis Method
• Automatic Differentiation
Features
• Black-box models (Response Surface; HDMR; etc.)
• Some changes to code (Automatic Differentiation)
• Extensive changes to model code/new modules (DDM; Adjoint Sensitivity)
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High Dimensional Model Representation (HDMR)
• System (a mathematical model; e.g. CMAQ):
- Input I:
- Output O:
• HDMR expresses model outputs as expansions of correlated
functions:
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• The expressions of HDMR component functions are
optimal choices for the output f (x) over the desired
domain of the input variable space such that the HDMR
expansion converges very rapidly
• Cut-HDMR:
• In practice, the HDMR expansion functions are represented
as a set of low dimensional look-up tables
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• Decomposition of variance:
• The total variance s2(g) attributable to all inputs
can be decomposed into individual contributions
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Automatic Differentiation (www.autodiff.org)
Chain Rule on Computer Instructions
y(1) = 1.0
y(2) = 1.0
do i = 1,n
if (x(i) > 0.0) then
y(1) = x(i) * y(1) * y(1)
else
y(2) = x(i) * y(2) * y(2)
endif
enddo
Problems:
ADIFOR does not support F90/F95
Commercial tools unproven
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dy(1) = 0.0
y(1) = 1.0
dy(2) = 0.0
y(2) = 1.0
do i = 1,n
if (x(i) > 0.0) then
dtemp = y(1)*dx(i)
temp = x(i) * y(1)
dy(1) = y(1)*dtemp
y(1) = temp * y(1)
else
dtemp = y(2)*dx(i)
temp = x(i) * y(2)
dy(2) = y(2)*dtemp
y(2) = temp * y(2)
endif
enddo
+ x(i)*dy(1)
+ temp*dy(1)
+ x(i)*dy(2)
+ temp*dy(2)
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Decoupled Direct Method (DDM)
• CMAQ-DDM 4.5 with CB4, Aero4, AQ
• Serial version from Talat Odman, Georgia Inst. of Technology
• Parallel version from Sergey Napalenok, USEPA
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Domain for the Case Study
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Case Study
• Impact of reductions in NOx
emissions from five states:
• DE, MD, NJ, NY, and PA
• Base Case:
• OTC12 BaseB
• Evaluate performance of HDMR and
DDM as surrogate models
• 10% overall
• 25% overall
• 75% overall except PA (zero reduction)
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Base Case (08/01/2002; Hours 14-17)
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10% overall reduction; HDMR (08/01/2002; Hours 14-17)
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25% overall reduction; HDMR (08/01/2002; Hours 14-17)
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75% overall reduction [except PA]; HDMR (08/01/2002; Hours 14-17)
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10% overall reduction; DDM (08/01/2002; Hours 14-17)
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25% overall reduction; DDM (08/01/2002; Hours 14-17)
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75% overall reduction [except PA]; DDM (08/01/2002; Hours 14-17)
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Discussion
• Approximations appear to break at about 25% changes in emissions
• Can be used for screening purposes for small variations
• Potential mix of “global” and “gradient-based” sensitivities
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