Dispersion of microbes in water distribution systems

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Dispersion of microbes in water distribution
systems

• Chris Choi
• Department of Agricultural and Biosystems
  Engineering
• The University of Arizona

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CAMRA National Homeland Security Center
Michigan State University
The University of Michigan
The University of California, Berkeley
Drexel University
Northern Arizona University
Carnegie Mellon University
The University of Arizona
Main Research Focus of Chois Group Water
Distribution Systems
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CAMRA National Homeland Security Center
UAs Research Responsibilities Exposure,
Detection, Fate and Transport of Agents - The
goal is to improve our ability to quantify
exposure to biological agents of concern
(Category A and B agents) in drinking water
systems and indoor air environments.
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Proposed Research Plan
RISK ASSESSMENT

• EPANET-based Simulation
• HD Model
• - WQ Model

ANN-based Prediction Models
Indicator Microorganisms (Gerba et al.)
Experimental Validation using Water-Distribution
Networks at the Water Village
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Collaboration Plan
Utilities (Tucson Water)
National Laboratories (Sandia National
Laboratories)
EPA
Water Distribution Laboratory
Bio-sensor Researchers
Quantitative Microbial Risk Assessment
Private Companies (Hach Event Monitor and
Bio-Sentry)
Microbiology (Dr. Gerbas Laboratory)
Accurate data sets are essential!
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What is EPANET?
EPANET models the hydraulic and water quality
behavior of water distribution piping systems.
EPANET is a free open source Windows program
written in C Delphi programming languages that
performs extended period simulation of hydraulic
and water-quality behavior within pressurized
pipe networks. A network can consist of pipes,
nodes (pipe junctions), pumps, valves and storage
tanks or reservoirs.
A Node-to-Node macroscopic Approach Remember
the cube Prof. Hass Introduced.
Node j
Node i
2D Control Volume
3D Control Volume
1D Control Volume
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EPANET is one of many WDS tools
Ref _at_ Angel Website
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Vulnerability of Water Distribution Systems
What if
Fire Hydrant
contaminants
100 pump
without backflow prevention devices
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Lab visits
Serious Engineering and Sensor Research Efforts
by Various Organizations Example at an EPA Lab
in Cincinnati
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Topics at 2006 WDSA Engineering Conference
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Field Trips
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Field Trips
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City of Tucson Water Distribution Network
UA Downtown Area
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Exemplary Case
Univ. of Arizona
Intrusion
Water In
Water Distribution Network near the University of
Arizona Campus
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Sample Water Distribution Network
Perfect Mixing Assumed at the Cross Joint for
Modeling Tools
to Subdivision A
to Subdivision B
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Biological Agent Intrusion Point
Water In
to Subdivision C
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Real World Systems Models
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Perfect Mixing Assumption
C 0.5?
Un-contaminated Water (C 0)
C 0.5?
Contaminated Water (C 1)
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Corresponding Risk Microbial Risk Assessment
Consequences
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Perfect Mixing Assumption
C 0.15
Un-contaminated Water (C 0)
C 0.85
Contaminated Water (C 1)
Courtesy of Sandia National Laboratories
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Corresponding Risk Microbial Risk Assessment
Consequences
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How to correct this problem?
Computational Approach CFDExperimental
ApproachUnderstanding of Fluid Mechanics
(turbulent flow, in particular) and Transport
Phenomena
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Laminar Turbulent Flow in Pipes
Experiment conducted by Osborne Reynolds (1842 -
1912)
Relt2100, laminar flow Regt2100, turbulent flow

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Governing Equations for Laminar Flow
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Key Parameters
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CFD Procedure
Discretized Governing Equation
2D Control Volume
Each box a infinitesimal control volume
Discretization
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Turbulent Flow
Navier-Stokes equations should apply, but this is
not usually solvable for random and inherently
unsteady (in a small scale) turbulent flows.
Suggested approach to time-average the N-S
equations and look at the effect of the unsteady
turbulent motions Reynolds Equation Introduce

Ref _at_ Angel Website
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Basic Equations for Turbulent Flow
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Governing Equations for Turbulent Flow(based on
k-e model)
where
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Boundary Conditions
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Key Parameters
Re f(Flow Speed)0 lt Re lt 60,000
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Key Parameters
Turbulent Schmidt Number (Sct)
In a k-e turbulent model, total diffusivity is
composed of the molecular and eddy diffusivities.
The eddy diffusivity is calculated through the
turbulent Schmidt number. Therefore, eddy
diffusivity is directly proportional to the eddy
viscosity computed at each node and inversely
proportional to Sct.
(Laminar Flow)
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Preparation of Sensors, Pumps, and Dataloggers
for Water Distribution Network Laboratory
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Role of Experimental Data
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Detailed Computational and Experimental Results
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Complex Oscillatory Flow Patterns Depending Upon
Flow Speed
Concentration Profiles
Velocity Vectors
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Detailed Computational and Experimental Results
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Mixing patterns along the interface
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Mixing patterns along the interface
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Water Village
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The Water Village
Unit B Water Distribution Network Lab
Water Quality Center Environmental Research
Laboratory
Mesquite Bosque
Data Education Building
Unit A Microbiology Lab
Rainwater Catchment
Edible Garden
Water Quality Laboratory
Project Office
Oasis Garden
Visitor Parking
Tucson International
Airport
Xeriscape Gardens
Main Office
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Unit A Microbiology POU Lab
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Unit B Water Distribution Network Laboratory
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Unit B Water Distribution Network Laboratory
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A schematic of the experimental setup

• Saltwater Tank
• Pump
• Gate Valve
• Electrical Conductivity Sensor
• Flow Sensor
• Freshwater Tank.

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Complexities Three Scenarios
Scenario 1 Equal inflows and outflows (ReS ReW
ReN ReE) Scenario 2 Equal outflows, varying
inflows (ReS ? ReW , ReN ReE) Scenario 3 Equal
inflows, varying outflows (ReS ReW , ReN ? ReE)
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Comparison An Example
NaCl mass rate splits from the experimental,
numerical and water quality model outcomes at
different ReE/N (East Outlet), when ReS ReW and
ReE ? ReN.
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Numerical Results based on Revised Sct
Correction Generalization
Dimensionless concentration of the experimental
and numerical results with corrected Turbulent
Schmidt Number (Sct) for the East Outlet when ReS
? ReW and ReE ReN.
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Revision of Water Distribution Model
CFD simulations based on four Reynolds numbers at
each node with Sct 0.135
Revise EPANET using C programming language based
on CFD results
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Comparison
Current WDS Model
Improved WDS Model
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Additional Reading Material
Ref _at_ Angel Website
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Artificial Neural Network 5x5

• A 5 x 5 network simulation data using EPANET
• 24 nodes with water demand
• 100 ft between pipes
• Two pumps, 1-point curve, 100 GPM 40 ft
• 5 sensor locations
• 4 potential release REGIONS

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1
5
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Sensor location Region
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Artificial Neural Network 10x10

• Implemented with EPANET, 10 x 10 nodes,
• 4 likely injection points ( )
• 4 sensors ( )
• All points have water demand (except from
  injection points and sensors)
• Three pumps supply water demands (1-point curve,
  80 GPM _at_ 50 ft)
• Pipe diameter 2 in, 100 ft between nodes, H-W
  Coef 100
• No elevation change,

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Injection of Surrogates into the Network
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Summary
CFD-based EPANET WQ Re-evaluation
Validation of Improved EPANET WQ Model
GROUP 3 RISK ASSESSMENT

• EPANET-based Simulation
• HD Model
• - Improved WQ Model, if possible

ANN-based Prediction Models
Simulation Data Validation using
Water-Distribution Network at the Water Village
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Research Sponsors and Major Collaborators