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Use of Image Analysis to develop new benchmarking datasets for variable-density flow scenarios
Rohit R. Goswami1,2, and T. Prabhakar Clement1
1 Department of Civil Engineering, Auburn University, Auburn, AL
2 Geosyntec Consultants, Boca Raton, FL
Outline
Components of Image Analysis (IA) procedure
• Overview of IA
Benchmarking experiments
• Two experiments- rising plume, sinking plume
Numerical modeling
• Challenges
• Alternate approaches
Image Analysis- Background
Advancing Saltwater Wedge
5 mins 15 mins 55 mins
Goswami & Clement (2007)- Laboratory-scale investigation of saltwater intrusion dynamics- Water Resources Research (43)
Components of IA
Calibration Relationship: fluid property v/s image property• Calibration data- experimentally obtained• Regression analysis- selecting relationship
Estimation of concentration levels
0.0
4.03.0 2.0
1.00.5
BenchmarkingPopular benchmarks
• Henry problem- Henry (1964), Simpson & Clement (2004)• Elder problem- Elder (1967), Voss & Souza (1987)
Recent benchmarks- stable case
• Oswald & Kinzelbach (2004), Goswami & Clement (2007) Unstable case
• Salt lake problem • Instabilities• Concentration data ?
Proposed exercise• IA to obtain concentration data• Testing the numerical approach
Variable-density Experiments
Laboratory Setup• 6 MP CCD Camera• CFL bulbs• LTM
Porous media
• Homogeneous packing
Image analysis process
Two experiments- rising plume, sinking plume
LTM
Variable-density Experiments
Example flow-tank setup
Physical Model- Rising Plume
0 min 3 min
6 min 8 min
Physical Model- Sinking Plume
0 min 2 min 5 min
Conceptualization- Rising Plume
225 mm
180 mm
114 mm injection point
Porous Media
p=0p=0
153 mm
x
z
Conceptualization- Sinking Plume
225 mm
54 mm
injection point
Porous Media145
mm
constant h 174 mm
constant h 178 mm
x
z
Numerical Modeling
Generation of instabilities- two approaches• Use of particle-tracking methods (MOC) with low
dispersivity values• Use small scale heterogeneities• Which approach is appropriate and why ?
We will explore both approaches using the variable-density model SEAWAT
MOC Results- Rising
MOC Results- Sinking
Heterogeneity Generation
Flow Tank
TUBA MATLAB
1% variability
Heterogeneity Results
0 min 3 min
6 min 8 min
1.0% Variability
0 min 2 min 5 min
1% Variability
How Much Heterogeneity?
0 min 3 min
6 min 8 min
1.0% Variability
10% Variability0.1% Variability
Summary
Benchmarking datasets• We propose to use a combination of two unstable
problems involving a sinking and a rising plume• They offer a unique combination – one with unstable
fingers and one without fingers
Unstable benchmark problems can be simulated using two approaches – which is appropriate?• MOC/TVD with low dispersivity values• Heterogeneities
Heterogeneity approach appears to be more appropriate• How much heterogeneity to use is an open question
Acknowledgements
Mr. Bharath Ambale, PhD Candidate, Department of Electrical Engineering, Auburn University
Dr. Elena Abarca, Fulbright Fellow, MIT, formerly at Auburn University
Mrs. Linzy Brakefield, USGS, formerly at Auburn University
Department of Civil Engineering, Auburn University, AL
Geosyntec Consultants, Boca Raton, FL
Discussion