Dispersion of microbes in water distribution systems
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Transcript of Dispersion of microbes in water distribution systems
Dispersion of microbes in water Dispersion of microbes in water distribution systemsdistribution systems
Chris ChoiChris Choi
Department of Agricultural and Biosystems EngineeringDepartment of Agricultural and Biosystems Engineering
The University of ArizonaThe University of Arizona
Michigan State UniversityMichigan State University
Drexel UniversityDrexel University
The University of MichiganThe University of Michigan
Carnegie Mellon UniversityCarnegie Mellon University
The University of ArizonaThe University of Arizona
The University of The University of California, BerkeleyCalifornia, Berkeley
Northern Arizona UniversityNorthern Arizona University
Main Research Focus of Choi’s Group: Main Research Focus of Choi’s Group: Water Distribution SystemsWater Distribution Systems
CAMRA – National Homeland Security CenterCAMRA – National Homeland Security Center
CAMRA – National Homeland Security CenterCAMRA – National Homeland Security Center
UA’s Research ResponsibilitiesUA’s Research Responsibilities Exposure, Detection, Fate and Transport of Exposure, Detection, Fate and Transport of AgentsAgents - 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.
EPANET-based SimulationEPANET-based Simulation
-HD ModelHD Model
- WQ Model- WQ Model
Experimental Validation using Experimental Validation using Water-Distribution Networks at Water-Distribution Networks at
the Water Villagethe Water Village
ANN-based ANN-based Prediction ModelsPrediction Models
RISK RISK ASSESSMENTASSESSMENT
Indicator Indicator MicroorganismsMicroorganisms
(Gerba et al.)(Gerba et al.)
Proposed Research PlanProposed Research Plan
Collaboration PlanCollaboration Plan
Water Distribution Laboratory
Microbiology
(Dr. Gerba’s Laboratory)
Quantitative Microbial Risk Assessment
Utilities (Tucson Water)
National Laboratories (Sandia National Laboratories)
EPA
Bio-sensor Researchers
Private Companies (Hach Event Monitor and Bio-Sentry)
Accurate data sets are essential!Accurate data sets are essential!
What is EPANET?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 A “Node-to-Node” macroscopic Approach – Remember the cube Approach – Remember the cube
Prof. Hass Introduced.Prof. Hass Introduced.
3D Control Volume 1D Control Volume1D Control Volume
Node i Node j2D Control Volume
EPANET is one of many WDS tools
Ref @ Angel Website:
Vulnerability of Water Distribution SystemsVulnerability of Water Distribution Systems
What if…What if…
$ 100 pump$ 100 pump
contaminantscontaminants
Fire HydrantFire Hydrant
without backflow without backflow prevention devicesprevention devices
Serious Engineering and Sensor Research Efforts by Various Serious Engineering and Sensor Research Efforts by Various Organizations: Example at an EPA Lab in CincinnatiOrganizations: Example at an EPA Lab in Cincinnati
Lab visitsLab visits
Topics at 2006 WDSA Engineering ConferenceTopics at 2006 WDSA Engineering Conference
Field Trips
Field Trips
City of Tucson Water Distribution NetworkCity of Tucson Water Distribution Network
UA & Downtown AreaUA & Downtown Area
Water Distribution Network near the University of Arizona CampusWater Distribution Network near the University of Arizona Campus
Univ. of Arizona
Exemplary Case
Water In
Intrusion
Sample Water Distribution NetworkSample Water Distribution Network
Water In Biological Agent Intrusion Point
Perfect Mixing Assumed at the Cross Joint for Modeling Tools
to Subdivision A
to Subdivision B
to Subdivision C
50 %
50 %
Real World Systems ModelsReal World Systems Models
Contaminated Water (C = 1)Contaminated Water (C = 1)
Un-contaminated Un-contaminated Water (C = 0)Water (C = 0)
C = 0.5?C = 0.5?
C = 0.5?C = 0.5?
Perfect Mixing AssumptionPerfect Mixing Assumption
Corresponding Risk Microbial Risk Assessment & ConsequencesCorresponding Risk Microbial Risk Assessment & Consequences
Contaminated Water (C = 1)Contaminated Water (C = 1)
Un-contaminated Un-contaminated Water (C = 0)Water (C = 0)
C = 0.85C = 0.85
C = 0.15
Perfect Mixing AssumptionPerfect Mixing Assumption
Courtesy of Sandia National Laboratories
Corresponding Risk Microbial Risk Assessment & ConsequencesCorresponding Risk Microbial Risk Assessment & Consequences
How to correct this problem?
Computational Approach – CFDExperimental ApproachUnderstanding of Fluid Mechanics (turbulent flow, in particular) and Transport Phenomena
Experiment conducted by Osborne Reynolds (1842 - 1912)
Laminar & Turbulent Flow in PipesLaminar & Turbulent Flow in Pipes
Re<2100, laminar flow
Re>2100, turbulent flow
Governing Equations for Laminar FlowGoverning Equations for Laminar Flow
0
yx
u
y
x
gyxy
p
yxu
t
gy
u
x
u
x
p
y
u
x
uu
t
u
2
2
2
2
2
2
2
2
1
1
Ny
C
x
CD
y
C
x
Cu
t
C
2
2
2
2
Sxx
uxt jj
jj
Key ParametersKey Parameters
2D Control Volume
Each box a infinitesimal control volume
baanb
nbnbPP Discretized Governing Equation
Discretization
CFD ProcedureCFD Procedure
Turbulent Flow
uuu
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 @ Angel Website:
Basic Equations for Turbulent FlowBasic Equations for Turbulent Flow
u
x
u
yx
p
y
u
x
uu
A
AAB
AA Cx
CD
yy
C
x
Cu
Governing Equations for Turbulent FlowGoverning Equations for Turbulent Flow(based on (based on model) model)
)(1
Puu
0 u
kjk
t
ji
i
Gx
k
xuk
x
k
CGk
Cxx
ux k
j
t
ji
i
2
21
it
tABi Y
ScDYu
t
tt D
Sc
where
Boundary Conditions
Key Parameters
Re = f(Flow Speed)0 < Re < 60,000+
Turbulent Schmidt Number (Sct)
Key Parameters
In a k-ε 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.
t
tt D
Sc
(Laminar Flow)
Preparation of Sensors, Pumps, and Dataloggers for Preparation of Sensors, Pumps, and Dataloggers for Water Distribution Network LaboratoryWater Distribution Network Laboratory
Role of Experimental Data
t
tt D
Sc
Detailed Computational and Detailed Computational and Experimental ResultsExperimental Results
Complex Oscillatory Flow Patterns Complex Oscillatory Flow Patterns
Depending Upon Flow SpeedDepending Upon Flow Speed
Concentration ProfilesConcentration Profiles
Velocity VectorsVelocity Vectors
Detailed Computational and Detailed Computational and Experimental ResultsExperimental Results
Mixing patterns along the interfaceMixing patterns along the interface
Lagrangian Lagrangian Particle Particle TrajectoriesTrajectories
Mixing patterns along the interfaceMixing patterns along the interface
Water VillageWater Village
The Water VillageThe Water Village
Oasis GardenOasis Garden
Data & Data & Education Education BuildingBuilding
Water Water Quality Quality LaboratoryLaboratory
Main OfficeMain Office
Water Quality CenterEnvironmental Research Laboratory
Rainwater Catchment
Xeriscape Gardens
Project OfficeProject Office
Edible Garden
Visitor Parking
Mesquite Bosque
TucsonTucson International International AirportAirport
Unit A:Unit A: Microbiology LabMicrobiology Lab
Unit B: Unit B: Water Distribution Water Distribution
Network LabNetwork Lab
Unit A:Unit A: Microbiology & POU Lab Microbiology & POU Lab
Unit B:Unit B: Water Distribution Network Laboratory Water Distribution Network Laboratory
Unit B:Unit B: Water Distribution Network Laboratory Water Distribution Network Laboratory
A schematic of the experimental setup
(A)Saltwater Tank(B)Pump(C)Gate Valve(D)Electrical Conductivity Sensor(E) Flow Sensor(F) Freshwater Tank.
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)
Complexities & Three ScenariosComplexities & Three Scenarios
Comparison – An ExampleComparison – An Example
0
20
40
60
80
100
0 0.5 1 1.5 2 2.5 3 3.5 4
ReE/N
NaC
l mas
s ra
te a
t E
ast
ou
tlet
,
CFD
Experimental
Experimental (van Bloemen Waanders et al. 2005)
EPANET
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.
Numerical Results based Numerical Results based on Revised on Revised ScSctt
0.0
0.2
0.4
0.6
0.8
1.0
0.2 0.4 0.6 0.8 1.0
Reynolds number ratio
Dim
en
sio
nle
ss c
on
cen
trat
ion
Eas
t O
utl
et
Numerical Sct = 0.135
Experimental
Dimensionless concentration of the experimental and numerical results with corrected Turbulent Schmidt Number (Sct) for the East Outlet when ReS ≠ ReW and ReE = ReN.
Correction & GeneralizationCorrection & Generalization
Revision of Water Distribution ModelRevision of Water Distribution Model
CFD simulations based CFD simulations based on four Reynolds on four Reynolds numbers at each node numbers at each node with with ScSctt = 0.135= 0.135
Revise EPANET using C Revise EPANET using C programming language programming language based on CFD resultsbased on CFD results
Comparison
Current WDS Model
Improved WDS Model
Additional Reading Material
Ref @ Angel Website:
Artificial Neural Network 5x5
Sensor location
Region
1
2
3
4
5
• 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
Artificial Neural Network – 10x10
IP-1
S-1
S-2
S-3
S-4
IP-2
IP-3
IP-4
• 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 @ 50 ft)
• Pipe diameter = 2 in, 100 ft between nodes, H-W Coef = 100
• No elevation change,
Injection of Surrogates into the Network
EPANET-based SimulationEPANET-based Simulation
- HD ModelHD Model
- - ImprovedImproved WQ ModelWQ Model, if possible, if possible
Simulation Data Validation Simulation Data Validation using Water-Distribution using Water-Distribution
Network at the Water VillageNetwork at the Water Village
ANN-based Prediction ModelsANN-based Prediction Models
CFD-based EPANET CFD-based EPANET WQ Re-evaluation WQ Re-evaluation
Validation of Improved Validation of Improved EPANET WQ ModelEPANET WQ Model
GROUP 3: RISK GROUP 3: RISK ASSESSMENTASSESSMENT
SummarySummary
Research Sponsors and Major CollaboratorsResearch Sponsors and Major Collaborators