Simulation

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Simulation Optimization for Threat Management
in Urban Water Systems

• Sarat Sreepathi
• North Carolina State University

Internet2 SURAgrid Demo Dec 6, 2006
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Our Team

• North Carolina State University
• Mahinthakumar, Brill, Ranji (PIs)
• 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)

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Water Distribution Security Problem
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Water Distribution Problem
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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

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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

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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

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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

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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

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Source Identification Problem

• Find L(x,y), Mt, T0
• Minimize Prediction Error
• ?i,t Cit(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
• Cit(obs) observed concentration at sensors
• Cit(L(x,y), Mt, T0) concentration from system
  simulation model
• i observation (sensor) location
• t time of observation

• unsteady
• nonlinear
• uncertainty/error

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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

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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

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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

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Preliminary Architecture
Sensor Data
Parallel EPANET(MPI)
EPANET-Driver
Optimization Toolkit
Middleware
EPANET
EPANET
EPANET
Grid Resources
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Graphical Monitoring Interface
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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

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Whats 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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Our Cyberinfrastructure
Portal
Sensors Data
Mobile RF AMR Sensors
Static RF AMR Sensor Network
Static Water Quality Sensor Network
Adaptive Wireless Data Receptor and Controller
Adaptive Workflow
Decisions
Data
Adaptive Optimization Controller
Algorithms Models
Bayesian Monte- Carlo Engine
Optimization Engine
Resource Availability
Resource Needs
Adaptive Simulation Controller
Adaptive Simulation Controller
Model Parameters
Model Outputs
Simulation Model
Middleware Resources
Grid Resource Broker and Scheduler
Grid Resource Broker and Scheduler
Grid Computing Resources
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Questions?