an adaptive cyberinfrastructure for threat management in urban water distribution systems kumar...

AN ADAPTIVE CYBERINFRASTRUCTURE FOR THREAT MANAGEMENT IN URBAN WATER DISTRIBUTION SYSTEMS

Kumar Mahinthakumar

North Carolina State University

DDDAS Workshop, ICCS 2006, Reading, UKMay 28 - 31, 2006

2

Our Team

North Carolina State University Mahinthakumar, Brill, Ranji (PI’s) Sreepathi, Liu, Vankalaya (Grad Students) Zechman (Post-Doc)

University of Chicago Von Laszewski (PI) Haetgen (Post-Doc)

University of Cincinnati Uber (PI) Feng (Post-Doc)

University of South Carolina Harrison (PI)

Greater Cincinnati Water Works

3

Outline

Overall Scope of Project Test problem Preliminary Results

4

Many security threat problems in civil infrastructure systems

5

Water Distribution Security ProblemDiameter

6.00

12.00

24.00

36.00

in

6

Water Distribution Problem

7

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

8

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

9

DDDAS Aspects

Dynamic Data Optimization Simulation Workflow Computer Resources

Data Driven and Vice Versa Water Demand Data Water Quality Data

10

Grid Resource Broker and SchedulerGrid Resource Broker and Scheduler

Adaptive Simulation ControllerAdaptive Simulation Controller

Adaptive Optimization Controller

Sensors& Data

Our Cyberinfrastructure

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 Controller

Deci

sio

ns

Data

Adaptive Workflow

Portal

Algorithms & Models

Middleware & Resources

Mod

el

Para

me

ters

Mod

el

Ou

tpu

ts

11

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

12

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

13

Source Identification Problem

Find: 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

• unsteady• nonlinear• uncertainty/error

14

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

15

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

16

Resolving non-uniqueness

Search for different solutions that are far apart in decision space

x

f(x)

17

Resolving non-uniqueness…

x

f(x)

Effects of uncertainty

18

Resolving non-uniqueness…

x

f(x)

Search for solutions that are far apart in decision space and are within an objective threshold of best solution

19

Resolving non-uniqueness…EAs for Generating Alternatives (EAGA)

Create n subpopulations

Sub Pop 1

Evaluate objfunction values

Best solution (X*, Z*)

Evaluate pop centroid(C1) in decision space

Selection(obj fn values)& EA operators

STOP?

Best Solutions

Sub Pop 2

Evaluate objfunction values

Feasible/Infeasible?

Evaluate distance in decisionspace to other populations

Selection(feasibility, dist)& EA operators

STOP?N Y NY

.

.

.

.

.

.

20

Non-uniqueness

Concentration for node 115

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 24 48 72 96 120 144 168 192 216 240 264 288Time step

Co

nce

ntr

atio

n(m

g/L

)

Concentration for node 265

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0 24 48 72 96 120 144 168 192 216 240 264 288Time step

Co

ncen

trati

on

(mg

/L)

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

)

21

Simulation Code

EPANET from USEPA Originally for Windows environments Ported to Unix platforms including

TeraGrid architectures

22

Current Search Algorithms

Classical Random Search (CRS) Easy to implement Massively parallel Tight efficient coupling with EPANET (C)

Evolutionary Algorithms (EA) Stand alone optimization toolkit (Java)

Loose coupling with EPANET through files Specific Algorithms

Standard GA Dynamic Optimization (ADOPT)

23

Dynamic optimization Minimize Prediction Error

∑i,t || Cit(obs) – Ci

t(L(x,y), {Mt}, T0) ||

Cit(obs) – streaming data

Objective function is dynamically updated Dynamically update estimate of source

characteristics

24

Dynamic optimization…

Underlying premisePredict solutions using available information

at any time stepSearch for a diverse set of solutions (EAGA)Current solutions are good starting points for

search in the next time step

25

Preliminary Architecture

MPI

EPANET

EPANET

EPANET

Optimization Toolkit

Sensor Data

Karigen Workflow Engine Grid Resources

26

Example Test Problem

Single source contamination Unknowns

Source Location Start Time Release History

Algorithms Classical Random Search Evolutionary Computation

27

Single Source Contamination ProblemContaminant 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

)

28

Single source contamination problemContaminant 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

)

29

Single source contamination problem

Explain the contamination issues

Show animation

30

Solution Comparison

0

500

1000

1500

2000

2500

3000

3500

source start c1 c2 c3 c4 c5 c6

exact

CRS

ADOPT

GA

ERRORCRS = 6.5%, ADOPT = 5.7%, GA = 10.0 %

31

Preliminary Architecture

Parallel EPANET(MPI)

EPANET-Driver

Optimization Toolkit

Sensor Data

Karigen Workflow Engine Grid Resources

EPANET EPANET EPANET

32

Speedups on local clusterEPANET File based Vs. CRS

0500

10001500200025003000350040004500

1 2 4 8 16 22

Num of Processors

Tim

e(s

ec

)

Parallel EPANET-FileBased

Parallel EPANET-CRS Version

33

Java Toolkit Overhead

EA + Parallel EPANET

0500

10001500200025003000350040004500

1 2 4 8 16

Number of Processors

Tim

e(se

c) Total time

Epanet Time

EAGA time

34

Java Toolkit Overhead

EA + Parallel EPANET

0%10%20%30%40%50%60%70%80%90%

100%

1 2 4 8 16

Number of Processors

% o

f T

ota

l T

ime

EAGA time

Epanet Time

35

Speedups on local clusterParallel EPANET

0500

10001500200025003000350040004500

1 2 4 8 16 22Num of Processors

Tim

e(se

c)

File based coupling (EA)

Tight coupling (CRS)

36

Summary

A cyberinfrastructure is being developed for water distribution threat management

Adaptive dynamic optimization algorithm shows promise for detecting contamination sources

37

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

38

Questions?