spatial reduction algorithm for numerical modeling of atmospheric pollutant transport
DESCRIPTION
Spatial Reduction Algorithm for Numerical Modeling of Atmospheric Pollutant Transport. Yevgenii Rastigejev, Philippe LeSager. Michael P. Brenner, Daniel J. Jacob. Harvard University. April, 2007. Fast Region. Slow Region. Motivation. - PowerPoint PPT PresentationTRANSCRIPT
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Spatial Reduction Algorithm for Numerical Modeling of Atmospheric
Pollutant Transport
Yevgenii Rastigejev, Philippe LeSager
Harvard University
Michael P. Brenner, Daniel J. Jacob
April, 2007
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Motivation
Problem: There is an urgent need to improve significantly computational performance of the existing GEOS-Chem code motivated by difficulties with numerical simulation of immerging applications
Solution: To create a multi-scale numerical algorithm which provides a significant (order of magnitude) reduction in computational cost
Slow Region
Fast Region
Our algorithm is inspired by the observation that atmospheric emissions have most of their fast reactants and their quickly decomposing reaction products localized near the emitter, typically on the ground. Far from the emitter, the fast reactants do not play a significant role.
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Solution: Spatial Reduction
In the Slow region
For the algorithm to be efficient:
size Slow Region >> size Fast Region
comp. cost Slow Region << comp. cost Fast Region
Fast Region – the species is fully accounted as adependent variable in the system of transport equationsSlow Region – the species concentration is found through extrapolation
or
Chemical source
r
Fast
Slow
D
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Gradual Spatial Reduction
Consider a two species reaction system, comprised of chemicals A and B
The central idea of our algorithm is to track a series of chemical boundary layers (CBL) within which the gradual, continuous spatial reduction is employed from the domain where the full mechanism is needed to the domain where the most simplified mechanism is sufficient.
Slow
Fast Fast
Slow
A B
A,B A
B
Domain partitioning for A and B
2 eqn
1 eqn
1 eqn
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Fast-Slow domain partitioning algorithm
Algorithms for Reduced Model construction
Matching procedure
Efficient methodology for adaptation in time
Prerequisites for Spatial Reduction Numerical Method
FastFast
SlowSlow
Fast if
Slow otherwise
Domain partitioning
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Matching conditions
matching procedure for concentration values and mass fluxes ensures good mass conservation and continuity properties of the numerical scheme
Reduced model – exponential extrapolation into Slow region
Fast
Slow exp. decay rate
distance
exp. decaying concentration
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Adaptive algorithm summary
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Chemical modeloxidation of a number of C3H6 in an OH oxidizing atmosphere, with NOx serving as catalyst through the cycling of HOx radicals
15 chemical species and 24 reactions
chemical flux
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Fast Slow
Fast – Slow domain partitioning for the atmosphere with horizontal shear layer flow at time
Comparison of species distribution obtained by the spatial reduction algorithm (spheres and cubes) and conventional method (solid line) depicted in blue and red colors at and at correspondingly at time
Max err = 2-3 %
Average err = 0.5 %
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Some preliminary conclusions
A gradual continuous spatial reduction computational method for Atmospheric chemistry has been developed.
Unlike conventional time-scale separation methods, the spatial reduction algorithm allows to speed up not only a "chemical solver" but also advection-diffusion numerical integration.
The method produced accurate results at 5-6 times lower computational cost for the 2-dimensional Atmospheric chemistry problems tested.
Currently we are working on the algorithm implementation into GEOS-Chem three-dimensional solver.
To be continued with three-dimensional description …
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G-C Implementation
Old SOLVER
(SMVGEAR)
New SOLVER
(DLSODES)
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Criteria for CBLs
[ n ] > Threshold Values
OR
Net Production > 10-3 Max Value
54% of ODE solved
averaged RMS error of 7.2%
Run Time = 0.66 Full Resolution Case
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Nb of Species
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Short lived species : Max error in the outskirts of concentration peaks
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Transported tracer: Max error further away from concentration peaks.
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To be continued…
1) Better CBLs (combining absolute/relative criteria, discriminating b/w species, fine tuning)
2) Extrapolation mechanism will• substantially reduce the error in the slow region• noticeably reduce the size of the fast region
3) Full advantage of sparse structure• substantial speed up