1. Use of Image Analysis to develop new benchmarking datasets for variable-density flow scenarios Rohit R. Goswami 1,2, and T. Prabhakar Clement 1 1 Department of Civil Engineering, Auburn University, Auburn, AL 2 Geosyntec Consultants, Boca Raton, FL

2. Outline  Components of Image Analysis (IA) procedure Overview of IA  Benchmarking experiments Two experiments- rising plume, sinking plume  Numerical modeling Challenges Alternate approaches

3. Image Analysis- Background Advancing Saltwater Wedge 5 mins15 mins55 mins  Goswami & Clement (2007)- Laboratory-scale investigation of saltwater intrusion dynamics- Water Resources Research (43)

4. 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 4.03.02.0 1.00.5

5. Benchmarking  Popular 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

6. 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 Flow Tank CCD Camera Translucent Sheet Lighting

7. Variable-density Experiments  Example flow-tank setup

8. Physical Model- Rising Plume 0 min3 min 6 min8 min

9. Physical Model- Sinking Plume 0 min2 min 5 min

10. Conceptualization- Rising Plume 225 mm 180 mm 114 mm injection point Porous Media p=0 153 mm x z

11. Conceptualization- Sinking Plume 225 mm 54 mm injection point Porous Media 145 mm constant h 174 mm constant h 178 mm x z

12. 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

13. MOC Results- Rising

14. MOC Results- Sinking

15. Heterogeneity Generation Flow Tank TUBAMATLAB 1% variability

16. Heterogeneity Results 0 min 3 min 6 min8 min 1.0% Variability 0 min2 min 5 min 1% Variability

17. How Much Heterogeneity? 0 min 3 min 6 min8 min 1.0% Variability 10% Variability 0.1% Variability

18. 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

19. 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

20. Discussion