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Sarat SreepathiNorth Carolina State University
Internet2 – SURAgrid DemoDec 6, 2006
Our TeamNorth 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
Diameter
6.00
12.00
24.00
36.00
in
4
Why is this an important problem?Potentially lethal and public health
hazardCause short term chaos and long term
issuesDiversionary action to cause service
outageReduction in fire fighting capacityDistract public & system managers
5
What needs to be done?Determine
Location of the contaminant source(s)Contamination release history
Identify threat management optionsSections of the network to be shut downFlow controls to
Limit spread of contamination Flush contamination
6
DDDAS AspectsDynamic Data Driven Application SystemsDynamic
DataOptimizationSimulationWorkflowComputer Resources
Data Driven and Vice VersaWater Demand DataWater Quality Data
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Key DDDAS DevelopmentsAlgorithm and Model Development
Dynamic OptimizationBayesian Data Sampling and Probabilistic
AssessmentModel Auto CalibrationModel SkeletonizationNetwork Assessment using Back Tracking
Middleware DevelopmentAdaptive Workflow EngineAdaptive Resource ManagementController Designs
Cincinnati Application Scenario DevelopmentSource IdentificationSensor Network DesignFlow control design
8
Water Distribution Network ModelingSolve for network hydraulics (i.e., pressure,
flow)Depends on
Water demand/usage Properties of network components
Uncertainty/variabilityDynamic system
Solve for contamination transportDepends on existing hydraulic conditionsSpatial/temporal variation
time series of contamination concentration
9
Source Identification ProblemFind: L(x,y), {Mt}, T0
Minimize Prediction Error∑i,t || Ci
t(obs) – Cit(L(x,y), {Mt}, T0) ||
where L(x,y) – contamination source location (x,y) Mt – contaminant mass loading at time t T0 – contamination start time Ci
t(obs) – observed concentration at sensors Ci
t(L(x,y), {Mt}, T0) – concentration from system simulation model
i – observation (sensor) location t – time of observation
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• unsteady• nonlinear• uncertainty/error
Interesting challengesNon-unique solutions
Due to limited observations (in space & time) Resolve non-uniqueness
Incrementally adaptive searchDue to dynamically updated information
streamOptimization under dynamic environments
Search under noisy conditionsDue to data errors & model uncertainty
Optimization under uncertain environments
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Resolving non-uniquenessUnderlying 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
Where we are now…Optimization Algorithms for Source
CharacterizationDynamic optimization (ADOPT) – WDSA06Non-uniqueness (EAGA) – WDSA06
ImplementationCoarse-grained parallelismReal-time visualizationSeamless job submission on TeragridSimple workflowDemo at I2 meeting
Project Website: www.secure-water.org
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Parallel EPANET(MPI)EPANET-Driver
Optimization Toolkit
Sensor Data
Grid Resources
EPANET EPANET EPANETMiddleware
Graphical Monitoring Interface
15
ChallengesProblem complexity
Improved search algorithms for multiple sources, non-uniqueness, dynamic source
characteristicsUsing Grid resources
Adaptive resource query and allocationAdaptive work migrationIntegration into workflow engine
16
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
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Grid Resource Broker and SchedulerGrid Resource Broker and Scheduler
Adaptive Simulation ControllerAdaptive Simulation Controller
Adaptive Optimization Controller
Sensors& Data
Mobile RF AMR SensorsStatic RF AMR
Sensor NetworkStatic Water Quality
Sensor Network
Resource Needs
Resource Availability
Bayesian Monte-Carlo Engine
Optimization Engine
Simulation Model
Grid Computing Resources
Adaptive Wireless Data Receptor and ControllerDecision
s
Data
Adaptive Workflow
Portal
Algorithms & Models
Middleware & Resources
Model
Paramete
rs
Model Output
s
19
Contaminant source profile
0
500
1000
1500
2000
2500
3000
3500
1 26 51 76 101 126 151 176 201 226 251 276Time step
So
urc
e m
ass
(mg
/min
)