Groundwater Modeling, Inverse Characterization, and Parallel Computing
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Groundwater Modeling, Inverse Characterization, and Parallel Computing Kumar Mahinthakumar NC State University
description
Groundwater Modeling, Inverse Characterization, and Parallel Computing. Kumar Mahinthakumar NC State University. My Background. Numerical modeling of groundwater flow and transport Developed PGREM3D – P arallel G roundwater REM ediation model – 3D finite element - PowerPoint PPT Presentation
Transcript of Groundwater Modeling, Inverse Characterization, and Parallel Computing
Groundwater Modeling, Inverse Characterization, and Parallel
Computing
Kumar MahinthakumarNC State University
My Background• Numerical modeling of groundwater flow and transport
– Developed PGREM3D – Parallel Groundwater REMediation model – 3D finite element
– GW2D – two dimensional educational models for groundwater flow and transport
• High Performance Computing– Parallel algorithms, Solvers, Parallel performance analysis
• Optimization and Inverse modeling– Groundwater source identification– Hydraulic conductivity inversion– Water distribution source identification and leak detection– Population based optimization algorithms (GA, PSO)– Markov Chain Monte Carlo Methods
Groundwater Remediation Modelingusing PGREM3D
Savannah River Site Investigation 1997
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Groundwater Source Identification: 3-Source release history reconstruction
sampling points
SourcesC1(t), C2(t), C3(t) are the unknown release
histories
1 2 3
4 5 6
78 9
10 11 12
13 14 15
16 17 18
flow direction
(x1,y1,z1)
(x2,y2,z2)
C1(t)
C2(t)
C3(t)
333 m
167 m
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Plume and Recovered History
0 50 100 150 200 250 300 350 400 450 5000
50
100
150
200
250
300
0
10
20
30
40
50
60
70
80
90
(1,1
)
(1,2
)
(1,3
)
(1,4
)
(1,5
)
(1,6
)
(1,7
)
(1,8
)
(1,9
)
(1,1
0)
(2,1
)
(2,2
)
(2,3
)
(2,4
)
(2,5
)
(2,6
)
(2,7
)
(2,8
)
(2,9
)
(2,1
0)
(3,1
)
(3,2
)
(3,3
)
(3,4
)
(3,5
)
(3,6
)
(3,7
)
(3,8
)
(3,9
)
(3,1
0)
Source (Number, Duration)
Rel
ease
Con
cent
ratio
n (m
g/l)
ActualRecovered (RGA-HKJ)Recovered (RGA-PWl)Recovered (RGA-CG)
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5-source release history reconstruction
0
10
20
30
40
50
60
70
80
90
100
(1,1
)(1
,3)
(1,5
)(1
,7)
(1,9
)(2
,1)
(2,3
)(2
,5)
(2,7
)(2
,9)
(3,1
)(3
,3)
(3,5
)(3
,7)
(3,9
)(4
,1)
(4,3
)(4
,5)
(4,7
)(4
,9)
(5,1
)(5
,3)
(5,5
)(5
,7)
(5,9
)
Source(number, duration)
Rel
ease
co
nce
ntr
atio
n (
mg
/l)
Actual
RGA-CG
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RGA-LS results for a 5-source problem
Hydraulic Conductivity Inversion using the Pilot Point Method
True K-field Prior
Inversion without Regularization Inversion with Regularization
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Parallel Computing: Multi-level Hybrid GA-LS-FEM framework
G A
GA
FEM 0 1
2
p procs
FEM 1 1
2
p procs
FEM P 1
2
p procs
MGA
MGA
FEM 1
2
p procs
FEM 1
LS n Powells
LS 1 simplex
LS 2 simplex
LS 3 Hookes
FEM 2
FEM P
FEM 1
FEM 2
FEM P
FEM
FEM
FEM 1
2
p procs
Global Search Local Search
Scalability of PSO on ORNL’s Jaguar Supercomputer
Jaguar PF: 299,008 AMD CoresWeak Scaling of our PSO Simulation-Optimization
framework Showing Over 80% efficiency up to 200,000 cores
WSC Project Tasks
• Hydrologic Modeling (4.3)– PIHM – Penn-State Integrated Hydrologic Model for
groundwater surface water interaction– SWAT-MODFLOW simulations
• Water Infrastructure Models (4.4)– Groundwater pumping effects (MODFLOW or
PGREM3D)– Reservoir model
• Parallel computing– Ensemble reservoir stream flow calculations
