1. AN ADAPTIVE CYBERINFRASTRUCTURE FOR THREAT MANAGEMENT IN URBAN WATER DISTRIBUTION SYSTEMS Kumar Mahinthakumar North Carolina State University DDDAS BOF, SC06, Tampa, FL Nov 14, 2006

2. 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) Greater Cincinnati Water Works

3. 3 Water Distribution Security Problem 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 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

4. 4 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

5. 5 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.orgwww.secure-water.org

6. 6 Preliminary Architecture Parallel EPANET(MPI) EPANET-Driver Optimization Toolkit Sensor Data Grid Resources EPANET Middleware

7. 7 Graphical Monitoring Interface

8. 8 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

9. 9 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

10. 10 Questions?