1. Sarat Sreepathi North Carolina State University Internet2 – SURAgrid Demo Dec 6, 2006

2. Our Team North Carolina State University Mahinthakumar, Brill, Ranji (PI’s) Sreepathi, Liu (Grad Students) Zechman (Post-Doc) University of Chicago Von Laszewski (PI) University of Cincinnati Uber (PI) Feng (Post-Doc) University of South Carolina Harrison (PI) 2 Greater Cincinnati Water Works

3. Water Distribution Security Problem 3

4. Water Distribution Problem 4

5. Why is this an important problem? Potentially lethal and public health hazard Cause short term chaos and long term issues Diversionary action to cause service outage Reduction in fire fighting capacity Distract public & system managers 5

6. What needs to be done? Determine Location of the contaminant source(s) Contamination release history Identify threat management options Sections of the network to be shut down Flow controls to Limit spread of contamination Flush contamination 6

7. DDDAS Aspects Dynamic Data Driven Application Systems Dynamic Data Optimization Simulation Workflow Computer Resources Data Driven and Vice Versa Water Demand Data Water Quality Data 7

8. Key DDDAS Developments Algorithm and Model Development Dynamic Optimization Bayesian Data Sampling and Probabilistic Assessment Model Auto Calibration Model Skeletonization Network Assessment using Back Tracking Middleware Development Adaptive Workflow Engine Adaptive Resource Management Controller Designs Cincinnati Application Scenario Development Source Identification Sensor Network Design Flow control design 8

9. Water Distribution Network Modeling Solve for network hydraulics (i.e., pressure, flow) Depends on Water demand/usage Properties of network components Uncertainty/variability Dynamic system Solve for contamination transport Depends on existing hydraulic conditions Spatial/temporal variation time series of contamination concentration 9

10. Source Identification Problem Find: L(x,y), {M t }, T 0 Minimize Prediction Error ∑ i,t || C i t (obs) – C i t (L(x,y), {M t }, T 0 ) || where L(x,y) – contamination source location (x,y) M t – contaminant mass loading at time t T 0 – contamination start time C i t (obs) – observed concentration at sensors C i t (L(x,y), {M t }, T 0 ) – concentration from system simulation model i – observation (sensor) location t – time of observation 10 unsteady nonlinear uncertainty/error

11. Interesting challenges Non-unique solutions Due to limited observations (in space & time) Resolve non-uniqueness Incrementally adaptive search Due to dynamically updated information stream Optimization under dynamic environments Search under noisy conditions Due to data errors & model uncertainty Optimization under uncertain environments 11

12. Resolving non-uniqueness Underlying premise In addition to the “optimal” solution, identify other “good” solutions that fit the observations Are there different solutions with similar performance in objective space? Search for alternative solutions 12

13. Where we are now… Optimization Algorithms for Source Characterization Dynamic optimization (ADOPT) – WDSA06 Non-uniqueness (EAGA) – WDSA06 Implementation Coarse-grained parallelism Real-time visualization Seamless job submission on Teragrid Simple workflow Demo at I2 meeting Project Website: www.secure-water.org 13

14. Preliminary Architecture 14 Parallel EPANET(MPI) EPANET-Driver Optimization Toolkit Sensor Data Grid Resources EPANET Middleware

15. Graphical Monitoring Interface 15

16. Challenges Problem complexity Improved search algorithms for multiple sources, non-uniqueness, dynamic source characteristics Using Grid resources Adaptive resource query and allocation Adaptive work migration Integration into workflow engine 16

17. What’s Next? Dynamic optimization for determining optimal location of sensors and optimal sampling frequency True integration of workflow engine into the cyberinfrastructure Backtracking to improve source identification search efficiency 17

18. Our Cyberinfrastructure 18 Grid Resource Broker and Scheduler Adaptive Simulation Controller Adaptive Optimization Controller Sensors & Data Mobile RF AMR Sensors Static RF AMR Sensor Network Static Water Quality Sensor Network Resource Needs Resource Availability Bayesian Monte- Carlo Engine Optimization Engine Simulation Model Grid Computing Resources Adaptive Wireless Data Receptor and Controller Deci sions Dat a Adaptive Workflow Portal Algorithms & Models Middleware & Resources Model Param eters Model Outputs

19. Questions? 19