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