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

7

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

10

• 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

11

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

13

14

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

17

18

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

)