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Computational fluid dynamics for predicting performance of ultraviolet
disinfection - Sensitivity to particle tracking inputs
Article in Journal of Environmental Engineering and Science · February 2011
DOI: 10.1139/s06-045
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Computational fluid dynamics for predicting
performance of ultraviolet disinfection —
sensitivity to particle tracking inputs1
Alex Munoz, Stephen Craik, and Suzanne Kresta
Abstract: A three-dimensional (3-D) computational fluid dynamic model that predicts the performance of a full-scale
medium-pressure lamp ultraviolet (UV) reactor for disinfection of drinking water is described. The model integrates
velocity field, fluence rate distribution, and particle trajectory calculations with a microorganism inactivation kinetic
model to arrive at predictions of reduction equivalent dose and microorganism inactivation for MS2 coliphage. A rational
approach to determining an appropriate number of fluid particles that would generate the required computational precision
is presented. Predictions of inactivation and equivalent dose were found to be sensitive to computational mesh geometry
(hexahedral versus tetrahedral) but were less sensitive to the value of the Lagrangian empirical constant used in the
random walk model and to choice of turbulence model (κ − ε versus Reynolds stress). Non-steady-state (dynamic)
simulations produced results that were similar to those of steady-state simulations. Utility of the model for evaluating
different lamp operating modes and alternative physical arrangements of the baffles and lamps was demonstrated.
Key words: ultraviolet, UV reactor, disinfection, water, computational fluid dynamics, modeling.
Résumé : Cet article décrit un modèle tridimensionnel de dynamique des fluides numérique qui prédit le rendement
d’un réacteur UV, pleine échelle, à lampe à moyenne pression pour désinfecter l’eau potable. Le modèle intègre le
champ de vitesse, la distribution du taux de fluence et les calculs de la trajectoire des particules dans un modèle de
cinétique d’inactivation des microorganismes pour arriver à prédire la dose équivalente de réduction et d’inactivation
des microorganismes par rapport au coliphage MS2. Une approche rationnelle pour déterminer le nombre approprié de
particules de fluide qui généreraient la précision computationnelle requise est présentée. Les prévisions d’inactivation
et de la dose équivalente se sont avérées sensibles à la géométrie computationnelle (hexaèdre p/r tétraèdre) mais elles
étaient moins sensibles à la valeur de la constante empirique Lagrangienne utilisée dans le modèle de parcours aléatoire
et au choix du modèle de turbulence (κ et ε p/r à la tension de Reynolds). Les simulations en régime non permanent
(dynamique) ont produit des résultats similaires à ceux des simulations en régime permanent. L’utilité du modèle pour
l’évaluation des différents modes de fonctionnement des lampes et des autres dispositions physiques des déflecteurs et des
lampes a été démontrée.
Mots-clés : ultraviolet, réacteur UV, désinfection, eau, dynamique des fluides numérique, modélisation.
[Traduit par la Rédaction]
Introduction
Interest in application of ultraviolet (UV) light technology
for primary disinfection of potable water in large drinking water treatment plants has increased significantly in recent years.
This has been due in part to the recent discovery that UV is
effective against waterborne pathogens of regulatory interest,
particularly Cryptosporidium parvum (Clancy et al. 1998; Craik
et al. 2001) and Giardia lamblia (Campbell and Wallis 2002;
Linden et al. 2002). The United States Environmental Protection Agency (US EPA)’s Long Term 2 Enhanced Surface Water
Treatment Rule has identified UV as an acceptable technology for providing protection against these parasites in filtered
surface water (US Environmental Protection Agency 2003b).
One of the engineering challenges in design and operation of
full-scale (UV) reactor systems is that it is difficult to predict
and monitor the UV dose delivered to microorganisms and the
Received 1 September 2005. Revision accepted 13 July 2006. Published on the NRC Research Press Web site at http://jees.nrc.ca/ on 9 May
2007.
A. Munoz. Stantec Consulting Ltd., Regina, SK S4P 3P1, Canada.
S. Craik.2,3 Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 2W2, Canada.
S. Kresta. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2W2, Canada.
Written discussion of this article is welcomed and will be received by the Editor until 30 September 2007.
1
This article is one of a selection of papers published in this special issue on application of ultraviolet light to air, water, and wastewater
treatment.
2
Present Address: EPCOR Water Services, 10065 Jasper Avenue, Edmonton, AB T5J 3B1, Canada.
3
Corresponding author (e-mail: scraik@epcor.ca).
J. Environ. Eng. Sci. 6: 285–301 (2007)
doi: 10.1139/S06-045
© 2007 NRC Canada
286
level of protection provided. The concentrations of pathogens
in drinking water under normal circumstances are usually well
below the level that would permit a direct measurement of the
level of inactivation. In addition, the dose received by microorganisms that pass through a UV reactor system is determined
by the spatial fluence rate distribution within the reactor and
the hydrodynamic flow pattern. The resulting dose distribution
is a complex function of several interacting variables including reactor geometry, the number, spacing and output of the
lamps, lamp sleeve characteristics, baffle arrangements, water
velocity, and transmittance. Because of the level of uncertainty
surrounding determination of dose, regulatory agencies such as
the US EPA require that dose in UV reactors used for drinking
water disinfection be validated using full-scale bioassay tests
(U.S. Environmental Protection Agency 2003a). In such tests,
the UV reactor is challenged with a test microorganism under
well-defined operating conditions, and the level of inactivation
of the test microorganisms is measured directly. Specialized facilities are usually required, and bioassay testing is, therefore,
difficult and expensive to carry out. Moreover, bioassay testing
does not provide dose distribution information and provides little insight into the physical phenomena that occur inside a UV
reactor. Thus, it is of limited use in development of new reactor
designs.
Computational modeling approaches have frequently been
proposed as alternative means of predicting the performance of
UV reactors both in wastewater and drinking water treatment
systems. The reported approaches vary, but in general computational modeling of UV reactor systems involves integration of
a fluence rate distribution model that describes the spatial variation in UV intensity within the reactor with a hydrodynamic
model that describes the flow or velocity field. Liu et al. (2004)
compared the predictions of several fluence rate models to actinometric measurements and concluded that a multiple segment
source summation (MSSS) model that includes refraction and
reflection provided the most accurate depiction of the fluence
rate field. The MSSS is a variation of the multiple point source
summation (MPSS) model introduced by Jacob and Dranoff
(1970). In the MPSS model, a UV lamp is approximated as a
linear series of discrete point sources that emit light equally
in all directions. The fluence rate decreases with distance from
each point source because of dispersion and because of absorption within the water and the quartz sleeve that surrounds the
lamp and separates it from the water. The total fluence rate at a
given coordinate within the reactor is determined by calculating
and summing the contribution from each point source. Bolton
(2000) modified the MPSS model to account for the effects of
reflection and refraction at the air–quartz–water interfaces and
incorporated a germicidal weighting factor to account for polychromatic emission of medium-pressure mercury arc lamps.
The MPSS model, however, tended to over-predict the fluence
rate near the lamps. Bolton, therefore, introduced the multiple
segment source summation modification in which the UV lamp
is approximated as a series of identical cylindrical segments,
rather than spheres (Liu et al. 2004). In the MSSS model, the
intensity of emission is greatest in the direction perpendicular
J. Environ. Eng. Sci. Vol. 6, 2007
to the surface of each element and decreases according to the
cosine of the angle between the perpendicular and the direction
of emission.
Microorganism inactivation in continuous-flow UV reactors
is particularly sensitive to hydrodynamics and mixing patterns
within the irradiated reactor volume (Severin et al. 1984; Qualls
and Johnson 1985; Scheible 1987; Blatchley et al. 1995; Iranpour et al. 1999). Various approaches have been used to describe mixing patterns within UV reactor systems, including
the application of conceptual reactor models such as completely
mixed and ideal plug flow reactors, completely mixed reactors
in series, and plug flow reactors with axial dispersion (Severin et al. 1984; Scheible 1987). Residence time information
generated from tracer studies has also been used (Severin et
al. 1984; Qualls and Johnson 1985). Although simple to apply,
these models do not describe the complex flow patterns that
exist in large multiple-lamp UV reactors adequately, and they
require equipment-specific performance information (such as
tracer test information). Consequently, they are of limited use
for design and scale-up. The current trend in UV reactor modeling, therefore, is to use computational fluid dynamics (CFD).
In CFD analysis the velocity vector field (or flow field) within
the UV reactor is predicted by solution of the Navier–Stokes
equations of continuity and motion, in conjunction with an appropriate turbulence model (such as the κ − ε model), using a
finite volume method. Two general approaches have been used
to model microorganism inactivation in continuous-flow UV
reactors: the Lagrangian approach and the Eulerian approach
(Ducoste et al. 2005b). In the Lagrangian, or particle-tracking
approach, the microorganisms are considered as discrete particles, and the probable pathways of these particles through the
reactor are calculated either by solving a particle momentum
equation or by using a random-walk algorithm (Chiu et al. 1999;
Wright and Hargreaves 2001). The accumulated UV dose received by each microorganism-particle is then determined by
numerical integration of fluence rate field and particle trajectory
information. By repeating this calculation for many particles,
a UV dose distribution is produced. The computed dose distribution can be combined with a microorganism UV inactivation
kinetic model to generate a reduction equivalent dose (RED)
for that particular microorganism (Ducoste et al. 2005a). In
the Eulerian approach microorganisms are treated much like
a reacting tracer in a chemical reactor, and inactivation is determined using an advective-diffusion equation that includes a
reaction term (Lyn et al. 1999; Do-Quang et al. 2002; Ducoste
and Linden 2005). Ducoste et al. (2005b) recently compared
the Eulerian and Lagrangian approaches and found that the two
methods predicted similar levels of microorganism inactivation
if the turbulent diffusion term was removed from the microorganism advective-diffusion equation in the Eulerian model.
Microbial inactivation predictions generated by both types of
models also compared reasonably well to experimentally measured inactivation of two challenge microorganisms (Bacillus
subtilis spores and MS2 coliphage). Other studies have reported
that the particle-tracking CFD approach produces reasonable,
though not necessarily perfect, predictions of microorganism
© 2007 NRC Canada
Munoz et al.
287
Fig. 1. Physical dimensions of the computational domain of the 6 × 20 kW UV Reactor. (a) Longitudinal cross section showing the
principal axis. (b) Radial cross section (all dimensions in metres).
(a)
y
x
(b)
y
z
inactivation in continuous-flow UV reactor systems when compared with inactivation measured in bioassay experiments (Petri
and Olson 2002; Rokyer et al. 2002; Ducoste et al. 2005a).
In CFD modeling of UV reactors, the modeler is faced with
a number of choices regarding computational inputs such as
type of mesh geometry, turbulence model, etc. In Lagrangian
particle-tracking simulations, the modeler must consider additional inputs such as the number of particles used to simulate
the microorganisms. Careful selection of these inputs is particularly important for 3-D CFD simulations of large-volume,
multi-lamp UV reactors used in full-scale water treatment facilities where a large number of finite volume elements may be
required to describe the reactor accurately and computational
requirements can be significant. In this study, a 3-D Lagrangian
CFD model of a large UV reactor used for drinking water disinfection in a full-scale treatment plant is described. The model
assumed fully developed turbulent flow at the reactor inlet and
used a commercial CFD code in conjunction with a discrete
random-walk (DRW) model to compute the flow field and particle trajectories within the reactor. This information was integrated with a MSSS fluence rate distribution model and a
microorganism inactivation kinetic model to compute the dose
distribution, microorganism inactivation, and RED for MS2 coliphage, a common indicator microorganism. This modeling approach is similar to particle-tracking CFD models that have been
described by others (Petri and Olson 2002; Rokyer et al. 2002;
Ducoste et al. 2005a). The primary objective of this study was
to use this realistic reactor model as a test case to investigate
the effect of particle-tracking inputs, specifically the number of
particles injected and the value of the Lagrangian constant in the
DRW model, on CFD predictions of microorganism inactivation and equivalent dose. Others have reported that predictions
© 2007 NRC Canada
288
of fluence rate distribution were insensitive to selection of other
user-selected particle-tracking parameters such as particle size,
coefficient of restitution at fluid-solid boundaries, and the Lagrangian computational step size (Ducoste et al. 2005b). The
sensitivity of the model predictions to mesh geometry (hexahedral versus tetrahedral), choice of turbulence model (κ − ε
versus Reynolds stress model), and simulation mode (steady
versus nonsteady state) was also examined. A secondary objective was to demonstrate the utility of the CFD model for
predicting the impact of different lamp operating modes and a
modified lamp and baffle arrangement on computed RED.
J. Environ. Eng. Sci. Vol. 6, 2007
Fig. 2. Flow sheet of the integrated UV reactor computational
model.
Velocity flow field
RANS eqs. & turbulence
model (FLUENT 6.1)
UV fluence rate field
MSSS Method
(UV Calc 3D-200)
Particle Tracking
DPM & DRW models
Microorganism UV
inactivation kinetics
UV dose distribution
Methodology
Ultraviolet reactor description
The UV reactors installed at the E.L. Smith drinking water
treatment plant in Edmonton, Alberta, were used as the physical system for model evaluation. Relevant physical dimensions
of the computational domain are provided in Fig. 1. Each reactor consisted of three sets of 20 kW medium-pressure mercury lamps arranged in pairs and oriented transverse to the flow
within a 1.2 m diameter section of steel pipe. Six baffle plates
were fixed to the internal walls at the top and bottom of the
reactor immediately upstream of each pair of lamps and oriented at 90◦ to the flow. The purpose of the baffles was to
promote radial mixing and to direct water toward regions of
high UV fluence rate in the region near the lamps. Each lamp
is housed within a 0.068 m outside diameter (OD) cylindrical
quartz sleeve and was assumed to have an electrical output efficiency of 23.25% and an output spectrum equal to that of a
typical medium-pressure lamp (Bolton 2000). The computational domain includes entrance and exit regions; these were
the 1.2 m sections of pipe upstream and downstream of the reactor section. Reactors of this size provide a challenge for CFD
modeling, particularly 3-D modeling, because of the potentially
large computational effort required.
Computational model description
The computational model of the UV reactor involved the integration of a number of distinct computational components,
as illustrated in Fig. 2. The velocity field within the UV reactor was computed using the FLUENT 6.1 (FLUENT INC.
Lebanon, New Hampshire) commercial computational fluid dynamics software package. This program applies a finite volume
method to solve the Reynolds averaged Navier–Stokes (RANS)
equations in conjunction with a turbulence model at discrete locations within the physical domain. The computational meshes
evaluated were produced using GAMBIT 2.0 software. The UV
fluence field calculation and the microorganism inactivation kinetics are user-defined functions that were combined with the
CFD simulation. Relevant computational parameters for the
base-case simulation are summarized in Table 1.
Several computational meshes were evaluated, including both
fine and coarse and structured and unstructured hexahedral
meshes. Structured hexahedral meshes did not provide satisfactory convergence (normalized residuals of the continuity equation >1 × 10−3 ). This was attributed to periodic flow oscilla-
Computation of the survival
ratio of each particle, (N/N0 )i ,
and overall inactivation,
log10[1/nt Σ(N/N0)i ]
tion in the wake region immediately downstream of the lamps.
Axial velocity profiles computed using a coarse unstructured
hexahedral mesh (583 521 elements) were essentially identical to those computed with the fine unstructured hexahedral
mesh (1 027 008 elements) (Munoz 2004). The fine unstructured hexahedral mesh was selected because it converged satisfactorily and ensured grid independence. The mesh, described
in Fig. 3, was constructed with a progressively finer mesh resolution nearer the lamps, walls, and baffle surfaces to provide
better resolution near these flow obstacles. Two types of turbulence models were evaluated: the κ − ε model and the Reynolds
stress model. Both models produced the same general flow pattern and similar velocity profiles, with some minor differences
close to the reactor wall (Munoz 2004). The κ −ε model, which
required less computational time to converge, was used in subsequent simulations.
Boundary conditions were required at the inlet and outlet of
the computational domain and at all solid surfaces. To minimize the length of the inlet, axial profiles of the velocity (ux, ),
turbulent kinetic energy (k), and turbulent kinetic energy dissipation rate (ε) at the inlet were assumed to be equal to those of
fully developed turbulent flow. Published correlations and data
describing the profiles of velocity (Zagarola and Smits 1998),
turbulent kinetic energy (Rodi 1984), and turbulent kinetic energy dissipation rate (Versteeg 1995) profiles in a pipe were used
to specify the inlet boundary condition (Munoz 2004). These
imposed inlet profiles were used to reduce the computational
effort associated with modeling a straight inlet section of length
equivalent to several pipe diameters. In preliminary work, the
imposed inlet profiles were verified to be the same as those
which would be predicted by the CFD software if an inlet region equal to 10 pipe diameters was used and the profiles at the
start of the inlet region were uniform. In some practical cases,
there may be elbows, valves, or other hydraulic restrictions located less than 10 pipe diameters upstream of the reactor. In
these cases, the assumption of fully developed turbulent profiles may not be valid. The outlet y–z surface was chosen to be
at a location downstream of the third set of baffles equivalent
© 2007 NRC Canada
Munoz et al.
289
Table 1. Summary of baseline model computational parameters.
Parameter
Velocity field computations
Computational mesh type
No. of mesh elements
Turbulence model
Numerical algorithm
Discretization of the convective term
Convergence criterion
Inlet boundary condition
Outlet boundary condition
Near wall treatment
Fluid properties
Particle trajectory computations
Discrete phase model
Particle diameter
Maximum number of steps
Length scale
Lagrangian empirical constant
Number of injected particles
Particle inlet boundary condition
Particle outlet boundary condition
Particle near wall boundary condition
Particle properties
UV fluence rate model
Lamp power efficiency
Lamp sleeve radius
Lamp length
Air refractive index
Water refractive index
Lamp sleeve refractive index
Lamp emission spectrum*
Lamp sleeve absorbance spectrum*
Lamp shadowing calculation
Water UV transmittance*
Description
Unstructured hexahedral
Entrance region, 197 904; lamp region, 555 168; exit region, 273 936; total, 1 027 008
κ −ε
SIMPLE C
Upwind differencing scheme (2nd order Taylor series)
Normalized residuals <1 × 10−3
Fully developed turbulent velocity profile
Zero gauge pressure
No-slip condition
ρ = 998.2 kg/m3 , µ = 1.003 × 10−3 kg/(m s), T = 293.15 K
Discrete random walk (DRW)
1.0 × 10−4 m
43 500
2.0 × 10−4 m
0.15
57 668
Fully developed turbulent flow velocity profile
Escape
Reflection
ρ = 998.2 kg/m3 , T = 293.15 K
23.25%
0.03378 m
1.1971 m
1.0
1.372
1.516
Provided by Bolton Photosciences Inc.
Provided by Bolton Photosciences Inc.
On
Provided by Epcor Water Services Edmonton, Alberta
* λ range 200 to 300 nm.
Fig. 3. Side view of the unstructured hexahedral mesh generated for velocity field computations in the 6 × 20 kW UV reactor (inlet and
outlet regions partially shown).
y
x
© 2007 NRC Canada
290
J. Environ. Eng. Sci. Vol. 6, 2007
to at least 10 baffle heights. The pressure at the outlet was set
equal to atmospheric pressure. The no-slip boundary condition
was used at all internal surfaces (i.e., baffles, lamps, and reactor
walls).
Microorganisms in the water entering the UV reactor were
considered to be contained within discrete spherical particles
of neutrally buoyant fluid. The trajectory of each fluid particle
was computed using the discrete phase model (DPM) subroutine in conjunction with a discrete random walk (DRW) model.
A diameter of 1 × 10−4 m was chosen such that the fluid particles were smaller than the smallest turbulent eddies yet much
larger than a typical microorganism. The assumption was that
10−4 m size fluid particles were small enough to capture the hydrodynamic effects within the reactor while not being so small
as to unnecessarily prolong the simulations. Particle path step
size must be dramatically reduced as particle size is reduced to
accurately simulate the individual particle paths. The density
of the particles was set equal to the density of the fluid (998
kg/m3 ). In each particle trajectory simulation, a large number,
np , of particles was introduced into the reactor inlet as follows.
The inlet y-z reactor cross-section of area A was divided into
m concentric rings of equal area A/m. The volumetric concentration of particles in each ring was set to a constant specified
value cp = np /Q, where Q was the total volumetric flow rate
entering the reactor. The volumetric flow rate in each concentric
ring j (j = 1, 2, , …, m) was approximated by integrating the
turbulent axial velocity profile ux (r) according to
[1]
rj +1
Qj =
ux (r)2πr dr
rj
where rj and rj +1 were the inside and outside radii of concentric ring j, respectively. The number of particle addition
points in each concentric ring was then determined according
to nj = cp Qj . The nj particle addition points were then randomly distributed across the surface of each concentric circle.
This method produced an inlet injection particle injection pattern that was randomized with each simulation but weighted
according to the turbulent velocity profile. An example pattern
is provided in Fig. 4. The “reflect” boundary condition, in which
the normal and tangential coefficients of restitution are set equal
to zero and one, was used at the wall. This minimizes the incidents of particles being trapped by the wall (velocity equal
to zero) and being bounced off the wall (coefficient of restitution = 1). The simulation was run until 98% of the particles
left the computational domain. The maximum number of time
steps needed was 43 500.
The spatial UV fluence rate distribution was computed using the UVCalc 3D-200 commercial program (Bolton Photosciences Inc., Edmonton, Alberta). This program uses the multiple segment source summation (MSSS) method to calculate
the fluence rate, Eλ,xyz , at discrete x–y–z points in a multiple
lamp UV reactor for each wavelength λ. In UVCalc 3D-200,
each lamp is divided into a series of 1000 equally spaced cylindrical segments. The contributions of each segment from each
of the six lamps were added to produce the UV fluence rate at
a given x–y–z coordinate within the reactor. The 3-D fluence
rate field was generated by repeating this calculation for x–y–z
coordinates that matched the grid points of the fine unstructured hexahedral mesh used in the velocity field calculations.
The UVCalc 3D-200 model accounts for the UV absorbance
spectrum of the water (between 200 and 300 nm), the spectral
output of the medium-pressure lamps, reflection and refraction
at the air–quartz and quartz–water interfaces, and the variation
in intensity with angle of emission (Liu et al. 2004). Lamp
input variables required for the UVCalc 3D-200 program, including the electrical output efficiency, emission spectrum of
the medium-pressure lamp, and the absorbance spectrum of the
quartz sleeve, were provided by Bolton Photosciences Inc. The
water absorbance spectrum was based on a measurement of
the absorbance spectrum of a sample of filtered drinking water
at the E.L. Smith drinking water treatment facility in Edmonton, Alberta, and was provided by EPCOR Water Services. The
transmittance of the water at 254 nm for a 10 mm path length
was 95.91%. To simulate high and low transmittance, the transmittance at 254 nm was set to 93% and 80%, respectively, and
the transmittance at other wavelengths was reduced proportionately. Fluence rate calculations were computed at wavelength
intervals of 5 nm between 200 and 300 nm. The germicidal
effectiveness of each wavelength, λ, was weighted according
to the absorbance spectrum of DNA to produce a germicidal , at each x–y–z coordinate (Bolton
weighted fluence rate, Exyz
2000). This weighting assumes that the absorbance spectrum of
MS2 coliphage is similar to that of DNA. For the purposes of
the sensitivity analysis presented in this study, lamp and sleeve
variables, water absorbance spectrum, and the microorganism
action spectrum were assumed to be accurate. To make rigorous
compare of model predictions to experimental bioassay results,
the modeler should verify the accuracy of these inputs by direct
measurement or by using rigorously validated information.
The UV dose distribution for each simulation was determined
by integrating the particle trajectory information produced by
DPM with the fluence rate field information generated by UV
Calc 3D-200. The accumulated UV dose received by a single
particle, Di , was computed by summing the germicidal fluence
rate along the path traveled by the particle through the UV
reactor according to
[2]
Di =
t=tf
t=0
Et t
where t is the Lagrangian time coordinate (the time of travel of
the particle within the computational domain), t is the computational time increment, and tf is the total simulation time.
Values of Et determined using UV Calc 3D-200 served as input into FLUENT 6.1. The particle dose, Di , for each injected
particle was then computed within FLUENT 6.1 by specifying
a user-defined function in the DPM computational module. To
ensure accurate computation of Di for each particle, a length
scale of 2 × 10−4 m (equivalent to one half of the particle relaxation time of 5.5 ×10−4 s) was used in the particle trajectory
computations.
© 2007 NRC Canada
Munoz et al.
291
Fig. 4. Example of the fluid particle injection pattern at inlet y–z cross-section. In this example, 1000 particles are injected using 100
concentric circles of equal area (A/m = 0.0113 m2 ). Dimensions are in metres.
0.8
y
0.6
0.4
0.2
z
0
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
-0.2
-0.4
-0.6
-0.8
The probability of survival of a microorganism was computed
using a specified UV-dose inactivation model. For the purposes
of the reactor model evaluation, MS2 coliphage was used as the
test microorganism because MS2 coliphage is relatively resistant to UV inactivation and is one of the microorganisms recommended for bioassay evaluations of UV reactors (US Environmental Protection Agency 2003a). Ultraviolet dose-inactivation
characteristics of MS2 coliphage have been well characterized
in collimated beam UV exposure experiments (Blatchley III et
al. 2000) and can be estimated by
N
[3]
− log10
= 0.00365D + 0.42
N0
vival ratio (N/N0 )o determined using eq. [4] into the eq. [3].
These calculations were carried externally to FLUENT 6.1 using spreadsheet software.
where N0 and N represent the number of live microorganisms
in volume of liquid before and after exposure to a UV dose, D.
Inactivation is expressed as the negative logarithm in base 10
of the survival ratio, N/N0 . The survival ratio in the ith fluid
particle, (N/N0 )i , was computed by substituting the accumulated particle dose, Di , determined using eq. [2], as the value
of D in eq. [3]. The overall survival ratio at the outlet of the UV
reactor, (N/N0 )o , was computed by summation of the survival
ratio (N/N0 )i of each of the np fluid particles injected into to
reactor inlet according to
[5]
[4]
N
N0
1 N =
np N0 i
i=np
o
The computational accuracy of the FLUENT 6.1 DPM userdefined function used to compute particle dose and the MS2
RED computations was verified by computing the mean particle dose, D i , for a hypothetical idealized case of an unbaffled
straight pipe (1.2 m OD) with a fully developed turbulent velocity profile and in which the axial germicidal fluence rate
distribution, E(r), was assumed to be exactly proportional to
the axial velocity profile, i.e,
u(r)
Emax
umax
where Emax
and umax were the fluence rate and axial velocities
at the center (y = z = 0) of the pipe, respectively. For the test
case, using assumed values, of Emax
and umax , the theoretical
value of the mean particle dose based on eq. [5] was 400 J/m2 .
The mean particle dose computed based on particle trajectory
calculations was 399.99 J/m2 (s.d. = 3.4 J/m2 ). Dose computations were, therefore, considered to be sufficiently accurate.
The simulation mean MS2 RED, D eqv arising from ns identical reactor simulation runs was computed according to
i=0
The MS2 reduction equivalent dose (RED or Deqv ) of the reactor was determined by substituting the overall reactor sur-
E (r) =
[6]
D eqv =
ns
Deqv
l=1
ns
l
© 2007 NRC Canada
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J. Environ. Eng. Sci. Vol. 6, 2007
The standard deviation of D eqv was given by:
ns
Deqv 2 − D 2eqv
l
[7]
SDeqv = ns − 1
l=1
The size of the 95% confidence interval on the mean MS2 RED
was computed using the student t-distribution according to
√
[8]
EDeqv = tns −1,0.025 SDeqv / ns
The theoretical UV dose, Dth , for each simulation was computed by assuming perfect plug flow (no dispersion) and complete radial mixing in the reactor. Under these idealized conditions, each particle would have the same residence time (ti =
Q/V where V is the irradiated volume) and would be exposed
where E was the mean
to the same average fluence rate Eavg
avg
at each x–y–z location in the reactor. The reactor
of the Exyz
hydraulic efficiency was defined as the ratio of the MS2 RED
to the theoretical dose, ηH = Deqv /Dth .
Results and discussion
Base-case simulation results
One advantage of a computational fluid dynamic (CFD) approach to UV reactor modeling over conceptual models is that it
permits examination of the expected flow characteristics within
the irradiated volume of the reactor. Computational fluid dynamics can be used to identify potential regions of high local
fluid velocity and by-pass flow, or regions of high recirculation
that may result in inefficient use of the reactor volume. An example of the computed flow field for the 6 × 20 kW reactor is
provided in Fig. 5. The operating conditions for the simulation
of Fig. 4 are those of the base-case assumed for this study. These
are (1) volumetric flowrate of 1.74 m3 /s, which corresponds to a
mean superficial velocity of 1.54 m/s and a Reynolds number of
1.83 × 106 , (2) water UV transmittance (UVT) at 254 nm and
10 mm path length of 93%, and (3) all six lamps in operation at
100% electrical power with no lamp sleeve fouling. The spatial
fluence rate distribution and the dose distribution for the base
case are provided in Figs. 6 and 7. The MS2 RED and inactivation predicted from the base cases simulation are 492 J/m2
and 2.2 log, respectively. These performance predictions are in
the range that would be expected for a typical drinking water
treatment facility.
The function of the baffles is to direct the water from low
fluence rate regions near the wall of the reactor towards the
high fluence rate regions in the vicinity of the lamps. This promotes cross-mixing across the fluence rate gradients, which
tends to result in a narrower dose distribution and improved
disinfection. The flow simulations also show that the baffles
result in high-velocity regions near the center of the reactor
and low-velocity regions near the reactor walls (Fig. 5). The
low-velocity recirculation regions downstream of the baffles
result in the long asymmetric tail of the dose distribution at
higher doses (Fig. 7). Although the MS2 RED for the base-case
simulation was 492 W/m2 , approximately 13% of the particles
received doses in the 250 to 350 W/m2 range (Fig. 7). However,
few particles received UV doses of less than 250 W/m2 , suggesting that there was very little short-circuiting through zones
of low fluence rate. Although this flow pattern prevents poor
inactivation due to short-circuiting, it results in an effective exposure time that is much less than the theoretical exposure time
based on the superficial mean velocity. The local velocities in
the central region are almost twice the mean superficial velocity
of 1.54 m/s, and the hydrodynamic efficiency is ηH = 0.55.
Evaluation of particle injection criteria
When the discrete random walk (DRW) is used to calculate
particle trajectories in DPM, the predictions of dose distribution, microorganism inactivation, and RED will differ with each
simulation owing to the random particle velocity components
inherent in the model. For example, Fig. 8 shows three different particle trajectories calculated for particles injected at the
same inlet point for three different simulations. As indicated in
the figure, these particles follow entirely different trajectories
and have different accumulated UV doses. The statistical significance of a simulation result can be improved by increasing
the total number of particles used in each simulation, np . Use
of too few particles will result in low reproducibility, poor accuracy and low statistical insignificance. Use of an excessively
large number of particles, on the other hand, results in unnecessary computational effort and data file sizes. The modeler must
select the value of np with care.
Graham and Moyeed (2002) proposed an efficient method to
determine the number of particles that are needed to produce
results within certain defined confidence limits in Lagrangian
simulations. A similar approach was adopted in this study for
determining an appropriate value of np for simulating microorganism inactivation in the 6 × 20 kW UV reactor. Two sets of
replicated particle tracking simulations were run using DPM in
conjunction with the DRW model as follows. In the first set, the
total number of simulations, ns , was kept constant at 30 while np
was varied. In the second, np was held constant at 1000, while ns
was varied. When the magnitude of the 95% confidence interval computed using eq. [8] was plotted against the total number
of particles used in all simulations np ns , the variability in the
computed inactivation was found to be inversely proportional
to np ns (Fig. 9). This is consistent with the findings of Graham
and Moyeed (2002) who reported that variability was proportional to {1/(np ns )0.5 } for simulation of particle-laden air flows
in ducts. At np ns less than approximately 20 000, the computed
MS2 RED was sensitive to both the product np ns and the values
of np and ns . For given value of np ns in this range, the variability was lower and the reproducibility was better, when more
simulations were used with fewer particles per simulation. As
np ns increased to greater than 30 000 particles, the variation
stabilized at approximately 2.0 J/m2 and was independent of
the values of np and ns . This variation is less than 1% of the reduction MS2 RED of 492 J/m2 and is an acceptable uncertainty
relative to the expected uncertainty for bioassay results. This
finding implies that at sufficiently high np ns (i.e., >30 000),
similar variability can be expected using one simulation with
© 2007 NRC Canada
Munoz et al.
293
Fig. 5. Velocity magnitude contours in the 6 × 20 kW reactor for the base-case CFD simulation. The view is in a vertical x–y plane
with z = 0.
Fig. 6. Fluence rate distribution for the 6 × 20 kW reactor for the base-case fluence simulation. The view is in a vertical x–y plane with
z = 0. Germicidal fluence rates, Exyz
, are given in W/m2 .
30 000 particles or 10 simulations with 3 000 particles each.
This is of significance to the modeler because it is generally
more convenient to run a single simulation with a large number of particles rather than several simulations with a small
number of particles. The results of these simulations suggest
that, for the 6 × 20 kW UV reactor, single simulations using
30 000 or more particles will produce satisfactory precision.
In general, the optimum number of particles will depend on
system specific variables such as reactor geometry, flow rate,
water UV absorbance, lamp characteristics, and UV resistance
© 2007 NRC Canada
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J. Environ. Eng. Sci. Vol. 6, 2007
Fig. 7. Dose distribution computed for the base-case simulation with np = 57 668. Computed equivalent dose is Deqv = 492 J/m2 .
40
Deqv
35
Relative frequency (%)
30
25
20
15
10
5
2900
2700
2500
>3000
2
2300
2100
1900
1700
1500
1300
1100
900
700
500
300
< 200
0
Particle Dose, Di (J/m )
Fig. 8. Example of trajectories computed in separate simulations for three fluid particles released at the same location at the inlet of the
UV reactor computational domain. Colors indicate accumulated UV fluence in mJ/cm2 .
of the particular microorganism under consideration. Although
the exercise described above will provide some general guidance, a similar particle number study should be carried out for
specific UV reactor systems to ensure a stable solution.
Evaluation of the Lagrangian empirical constant, CL
The computational modeler must select an appropriate model
to describe the interaction between the dispersed phase (i.e.,
the particles) and the continuous phase (i.e., carrier fluid) when
carrying out particle trajectory simulations. In this study, the
discrete-random walk (DRW) model was used to describe this
interaction. When using the DRW model, the modeler must
specify a value for the Lagrangian time constant, CL . This empirical constant is related to the time period that a particle is
allowed to interact with an eddy in turbulent flow. The value
of CL determines the degree of particle dispersion within the
carrier fluid and may have an important influence on the computed inactivation and MS2 RED in a UV reactor. MacInnes and
© 2007 NRC Canada
Munoz et al.
295
Fig. 9. Size of the confidence interval on equivalent dose, EDeqv , as a function of the total number of particles (np × ns ) for two sets of
simulations. Base-case simulation conditions (Table 1) were used.
8
ns = 30, np variable (simulations)
np = 1000, ns variable (simulations)
ns = 30, np variable (curve fit)
np = 1000, ns variable (curve fit)
7
6
2
EDeqv (J/m )
5
4
E Deqv = 1335.1(n p x n s )
-0.62
2
3
r = 0.9738
2
E Deqv = 329.92(n p x n s )
-0.49
2
1
r = 0.8999
0
0
10 000
20 000
30 000
40 000
50 000
60 000
npx ns
Table 2. Matrix of experimental simulations used to determine the effect of the Lagrangian empirical constant on UV reactor
performance predictions.
Simulation input
Run
*
1
2
3
4
5
6
7
8
9
10
11
12
Simulation outputs
NLO
(kW)
Q (m /s)
EED (kW h/m )
UVT (%)
CL
Dth (J/m2 )
-log10 (N/N0 )
Deqv (J/m2 )
ηH
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
+
+
-
+
+
+
+
-
+
+
+
+
+
+
-
+
+
+
+
+
+
900.8
900.8
310.5
310.5
600.0
600.0
206.8
206.8
594.0
594.0
204.7
204.7
2.22
2.26
0.84
0.86
1.73
1.74
0.74
0.74
1.711
1.749
0.743
0.760
492.2
505.3
113.9
120.5
359.1
362.5
86.3
86.7
353.7
364.1
88.4
93.3
0.546
0.561
0.367
0.388
0.598
0.604
0.417
0.419
0.595
0.613
0.432
0.455
3
3
Note: Experimental input ranges: NLO, lamps 1 and 2 (-) or all 6 (+) lamps; , 6.7 (-) or 20 (+) kW Q, 0.87 (-) or 1.74 (+) m3 /s; EED,
0.0128 (-) or 0.0192 (+) kW h/m3 ; UVT, 80% (-) or 93% (+) at 254 nm and 10 mm; CL : 0.15 (-) or 0.30 (+).
* Base-case simulation.
Bracco (1992) found there was little consensus in the literature
on values of the Lagrangian time constant used in homogeneous
turbulent flow models, with reported values ranging from 0.06
to 0.63. It was of some interest, therefore, to determine the sensitivity of predicted inactivation and RED to the value of the
Lagrangian time constant in the DRW model.
The effect of reducing the Lagrangian empirical constant
from 0.30 to 0.15 (the values recommended by FLUENT 6.1
for use with the DRW model) was determined at various low
(–) and high (+) combinations of electrical energy dose (EED)
and UV transmittance of the water (UVT). Conditions for the
simulations and corresponding simulation results are provided
in Table 2. The electrical energy dose, EED, was determined
by the number of lamps in operation NLO), individual lamp
power (), and flow rate (EED = NLO × /Q) and is a measure of the energy input per volume of water treated. The high
EED condition (0.0192 kW h/m3 ) was specified by setting all
six lamps to 100% power with the water flow rate at a low
© 2007 NRC Canada
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J. Environ. Eng. Sci. Vol. 6, 2007
Table 3. Least-squares coefficients of regression model
relating predictions of equivalent dose, electrical energy
dose, water UV transmittance and Lagrangian empirical
constant.
Coefficient
Input
Coefficients
P-value
α0
α1
α2
α3
α12
Intercept
EED
UVT
CL
UVT × EED
266.1
41.9
163.2
3.2
27.6
1.3 × 10−9
9.4 × 10−14
0.012
2.3 × 10−8
Reduced Model: Deqv = α0 + α1 (EED) + α2 (UVT) + α3 (CL )
+ α12 (EED × UVT)
level (0.87 m3 /s) (simulation runs 1–4). The low EED condition (0.0128 kW h/m3 ) was specified in two low flow operating
modes: with all six lamps in operation at 33% power each (simulation runs 5–8) or with 2 lamps in operation (lamps 1 and 2 as
shown in Fig. 1) at 100% power each (simulation runs 9–12).
These operating conditions were chosen to reflect a wide range
of operating conditions that would be encountered in operation
of this type of UV reactor in a water treatment plant.
Least-squares linear regression was used to further examine
the effects of the inputs (EED, UVT, and CL ) and their interactions on MS2 RED, Deqv . As expected, the reduced regression
model (Table 3), indicates that EED, UVT, and the interaction
EED × UVT had statistically significant effects on the MS2
RED (i.e., the associated p-value was less than 0.05). The value
of the Lagrangian time constant, CL , was determined to have
a statistically significant effect on dose (p-value = 0.012). On
average, decreasing the value of CL from 0.30 to 0.15 corresponded to a reduction in the predicted MS2 RED of 6.5 J/cm2 ,
which is only 1.2% of the average computed MS2 RED. Although statistically significant, this effect may be practically
unimportant. The interactions between CL and EED or UVT
were not statistically significant, which suggests that the magnitude of the CL effect can be expected to be essentially fixed
over the normal UV reactor operating range. Use of the default values of CL provided by FLUENT 6.1 should provide
reasonably accurate dose predictions for large UV reactors.
Evaluation of mesh geometry, turbulence model and
unsteady flow
Numerous other computational parameters must be selected
by the modeler when carrying out CFD simulations of large UV
reactors. Additional simulations were carried out to examine the
effect of selected computational parameters (mesh geometry,
type of turbulence model, and simulation mode) on computed
MS2 RED in the 6 × 20 kW reactor including. Each parameter was varied independently, and the resulting computed MS2
RED was compared with that of the base case (492 J/m2 ). Results are provided in Table 4.
The unstructured tetrahedral mesh is more easily and readily
generated than the unstructured hexahedral mesh that was used
in the base case. This advantage, however, may be offset by a
decrease in the accuracy of the predictions. Selection of an unstructured tetrahedral mesh versus the unstructured hexahedral
mesh resulted in a 7% increase in computed MS2 RED even
though 22% more mesh elements were used for the unstructured
tetrahedral mesh to ensure the same level of grid independence.
The unstructured tetrahedral mesh was found to predict lower
velocities in the central region of the reactor when compared
with the unstructured hexahedral mesh (data not shown). This
is consistent with higher predicted microorganism average residence time, MS2 inactivation, and RED. Under-prediction of
velocity will consistently over-predict dose, so modelers should
use tetrahedral meshes with caution when simulating large UV
reactors.
In most reported studies on CFD modeling of UV reactors,
the κ − ε model has been used as the turbulence model, i.e.,
Blatchley et al. (1998); Lyn et al. (1999). The Reynolds stress
model (RSM), an alternative to the κ − ε model, should provide
a better description of nonhomogeneous flows because it solves
for each component of the Reynolds stresses rather than the total
kinetic energy (Wright and Hargreaves 2001). The difference
in the predicted MS2 RED generated using the two turbulence
models (κ − ε and RSM) was less than 4%. A higher order
turbulence model may be needed only if a very high level of
precision is required. Depending on the level of accuracy desired, modelers should, therefore, make the choice of turbulence
model carefully.
Typically, UV reactor simulations are done in steady-state
mode. In reality, the velocity field within a UV reactor changes
with time as large eddies expand and collapse. The unsteadystate simulation feature of FLUENT 6.1 was used to predict
the impact of these transient characteristics on computed inactivation. Although the unsteady-state state simulations provided interesting information regarding transient flow phenomena within the reactor, such as the generation and collapse of
eddies downstream of the baffles and lamps, the computed inactivation and MS2 RED changed by less than 2% from the
steady-state simulation. The steady-state simulation was, therefore, considered adequate for modeling of this large UV reactor.
Evaluation of lamp operation mode
Computational fluid dynamics modeling may be used to predict the influence of different operating modes on UV reactor
performance and to assist in determining the most efficient or
cost-effective operating strategies for changing water flow or
quality conditions. For example, at a given flow rate, the desired electrical energy dose may be achieved by controlling either the number of lamps in operation or the power to each lamp.
In simulation runs 5 to 12 in Table 2, the EED was maintained
at 0.0128 kW h/m3 for a flow rate of 0.87 m3 /s by operating with
either lamps 1 and 2 (Fig. 1) both at 100% power, or with all six
lamps in operation, each at 33% power. A regression analysis
revealed that the effect on computed MS2 RED and hydraulic
efficiency was not statistically significant. In this case, the decision to operate the reactor in either mode should be based on
considerations such as optimizing lamp life. If only two lamps
are used, however, the efficiency may depend on which two of
the six lamps are in operation. Table 5 shows the results of a set
of simulations in which different pairs of lamps were operated
© 2007 NRC Canada
Munoz et al.
297
Table 4. Effect of additional computational parameters on computed equivalent dose.
Run
*
1
13
14
15
Mesh type
Turbulence model
Simulation mode
-log10 (N/N0 )
Deqv (J/m2 )
Hexahedral
Tetrahedral
Hexahedral
Hexahedral
κ −ε
κ −ε
RSM
κ −ε
Steady state
Steady state
Steady state
Unsteady state
2.21
2.34
2.15
2.19
492.7
527.7
475.3
485.4
Note: RSM, Reynolds stress model.
* Base-case simulation.
Table 5. Effect of various combinations of lamp operation on predicted UV
reactor performance.
Run
NLO∗
CL
Dth (J/m2 )
-log10 (N/N0 )
Deqv (J/m2 )
ηH
5
6
5b
6b
5c
6c
1, 2
1, 2
1, 4
1, 4
1, 6
1, 6
0.15
0.30
0.15
0.30
0.15
0.15
600.0
600.0
608.8
608.8
600.6
600.6
1.731
1.743
1.645
1.644
1.612
1.591
359.1
362.5
335.6
335.2
326.7
320.8
0.598
0.604
0.558
0.558
0.544
0.534
Note: Operating conditions: = 20 kW, Q = 0.84 m3 /s, EED = 0.0128 kW h/m3 ,
UVT = 93% at 254 nm at 10 mm path length.
∗
Lamp numbers shown in Fig. 1.
at identical flow, EED, and UVT conditions. The greatest hydraulic efficiency and MS2 RED were achieved when the two
lamps in operation were selected from the same vertical bank.
Operation of the two lamps in different vertical banks increases
the probability that a microorganism travels only through regions of low fluence rate and bypasses regions of high fluence.
The vertical banks are hydraulically equivalent, so the bank
chosen will not affect the Deqv .
Evaluation of a modified ultraviolet reactor design
Computational fluid dynamics in conjunction with fluence
rate modeling can be used to examine the effect of design variables and modifications on UV reactor performance and to aid
in the development of new reactor designs. The dose distribution computed for the 6 × 20 kW reactor with baffles removed
(Fig. 10) was much broader than the one computed for the reactor with baffles in place (Fig. 7). Most significantly, many more
particles received a low UV dose ( <250 J/m2 ) when the baffles were removed resulting in a considerable reduction in MS2
coliphage inactivation (1.40 vs. 2.22), RED (267 vs. 492 J/m2 ),
and hydraulic efficiency (0.296 vs. 0.546). Baffles are important for ensuring appropriate mixing across fluence rate gradients and good hydraulic efficiency; however, they also increase
hydraulic pressure drop across the reactor.
Figure 11 describes a hypothetical 8 × 15 kW modified reactor design. In this reactor, eight 15 kW medium-pressure lamps
were arranged perpendicular to the bulk flow and perpendicular
to each other. The modified reactor contained 16 small baffles
oriented perpendicular to the flow. Although there were more
baffles, the total baffle area was considerably less than that of
the 6 × 20 kW reactor. The lamp and baffle arrangement in the
modified reactor was selected because it is expected to provide a
better spatial distribution of the fluence rate with lower pressure
drop in comparison to the 6 × 20kW UV reactor. Disadvantages
of the design are that the lamp arrangement may make it difficult to remove some lamps for maintenance or replacement
and the extra two lamps will demand additional monitoring and
mechanical cleaning systems.
Simulations were carried out with the modified reactor design
at various sets of operating conditions. The simulation results
are compared with a similar set of simulations on the original
6 × 20 kW reactor in Table 6. The predicted hydraulic efficiency
for the 8 × 15 kW UV reactor was 4% percent less than that of
the 6 × 20 kW UV reactor when the water UV transmittance
was high (compare runs 1 and 5 with 18 and 20). On the other
hand, the predicted hydraulic efficiency for the 8 × 15 kW UV
reactor was 6%–10% greater than that of the 6 × 20 kW UV
reactor at low UV transmittance (compare runs 3 and 7 with
19 and 21). The simulation results indicate that the benefit of
the modified reactor design only appears in low transmittance
water.
Baffles played a much less important role in the modified
8 × 15 kW UV reactor than in the 6 × 30 kW reactor. Removal
of baffles in the 6 × 20 kW reactor resulted in a reduction of
hydraulic efficiency from 0.546 to 0.296 (runs 1 and 17). For
the modified 8 × 20 kW reactor, removal of the baffles resulted
in a reduction in hydraulic efficiency from 0.502 to 0.490 (runs
18 and 22). The pressure drops predicted by FLUENT 6.1 for
the 6 × 20 kW UV reactor, with and without baffles, were 3 and
1 kPa, respectively. For the 8 × 15 kW UV reactor, the predicted
pressure drops, with and without baffles, were 1.8 and 1.2 kPa,
respectively. The modified reactor design may have important
© 2007 NRC Canada
298
J. Environ. Eng. Sci. Vol. 6, 2007
Fig. 10. Dose distribution computed using eq. [3] for the base-case simulation with baffles removed from the reactor. The number of
particles used was np = 58 397 particles. (Dth = 900.7 J/m2 , −log(N/N0 ) = 1.4, Deqv = 267.1 J/m2 , ηH = 0.296).
40
35
Relative frequency (%)
30
Deqv
25
20
15
10
5
2900
2700
2500
2300
>3000
2
2100
1900
1700
1500
1300
1100
900
700
500
300
<200
0
Particle Dose, Di (J/m )
Fig. 11. Physical dimensions of the computational domain of a modified 8 × 15 kW UV reactor. (a) Longitudinal cross section showing
the principal axis. (b) Radial cross section. (all dimensions in metres).
© 2007 NRC Canada
Munoz et al.
299
Table 6. Simulated performance comparison of a 6 × 20 kW UV reactor and a hypothetical 8 × 15 kW reactor.
Simulation inputs
Run
NLO
3
Q (m /h)
Simulation outputs
3
EED (kW h/m )
6 × 20 kW lamp UV reactor
1*
+
+
+
3
+
+
+
5
7
17
+
+
+
8 × 15 kW lamp UV reactor
18
+
+
+
19
+
+
+
20
21
22
+
+
+
UVT (%)
Baffles
Dth (J/m2 )
-log10 (N/N0 )
Deqv (J/m2 )
ηH
+
+
-
+
+
+
+
-
900.8
310.5
600.0
206.8
900.7
2.22
0.84
1.73
0.74
1.40
492.2
113.9
359.1
86.3
267.1
0.546
0.367
0.598
0.417
0.296
+
+
-
+
+
+
+
-
900.8
310.0
594.5
204.6
900.8
2.07
0.91
1.66
0.80
2.03
452.0
133.1
339.2
104.3
441.7
0.502
0.429
0.570
0.510
0.490
Note: Experimental input ranges: NLO, lamps 1 and 2 (-) or all 6 (+) lamps; Q, 0.87 (-) or 1.74 (+) m3 /s; EED, 0.0128 (-) or
0.0192 (+) kW h/m3 ; UVT, 80% (-) or 93% (+) at 254 nm and 10 mm; CL : 0.15 (-) or 0.30 (+).
* Base-case simulation.
advantages in UV reactor installations where the available pressure drop is very low (such as in a retrofit of an existing water
treatment facility).
In the absence of extensive experimental bioassay testing,
deductions such as this are not necessarily obvious or intuitive.
Computational fluid dynamics modeling helps to provide additional insight into reactor operation that can be used to predict
optimal operating conditions.
Conclusions
A 3-D CFD model that predicts the performance of full-scale
UV disinfection reactors in drinking water treatment processes
was described. The model integrates velocity field predictions
generated by CFD, fluence rate distribution predictions, dispersed phase particle trajectory calculations generated using a
random walk model, and a microorganism inactivation kinetic
model to arrive at predictions of microorganism inactivation and
reduction equivalent dose. The model was applied to the case
of an existing full-scale, multi-lamp medium-pressure lamp UV
reactor with fully developed turbulent flow at the reactor inlet.
The effect of various computational inputs on the predictions of microorganism inactivation and equivalent dose was
examined by carrying out a series of simulations. Model prediction variability was a function of the number of fluid particles
introduced for particle-tracking simulations and decreased as
the number of fluid particles injected per simulation increased.
A rational approach to determining an appropriate number of
particles that would generate the required precision based was
presented. Model predictions were found to be sensitive to computational mesh geometry (hexadedral versus tetrahedral) but
were less sensitive to the value of the Lagrangian empirical constant used in the random walk model and choice of turbulence
model (κ − ε versus Reynolds stress). The results of steadystate simulations were comparable to those of unsteady-state
(dynamic) simulations, suggesting that the computational ef-
fort required for the latter was not justified for modeling of
this reactor. The model was also used to evaluate the different
lamp operating modes and alternative physical arrangements of
the baffles and lamps. Results generated were not necessarily
intuitive, thus demonstrating the utility of CFD modeling.
This study examined the effect of a number of computational
inputs and UV reactor design and operational variables on the
integrated model predictions of MS2 coliphage inactivation and
equivalent dose in one type of large-scale UV reactor. The computational issues addressed would likely apply to UV reactors
of similar scale and design; however, the modeler should be
cautious about generalizing the results to other UV reactors
operating at different conditions.
Acknowledgements
The authors would like to acknowledge Dr. James Bolton,
who provided invaluable assistance and advice in the use of the
UV Calc-3D 200 program and in modeling of fluence rate in
general, Ms. Vesselina Roussinova for her help with computational modeling, and Mr. Craig Bonneville of EPCOR Water
Services Inc. who provided information regarding the UV reactors at the EPCOR facilities. Funding for this work was provided through the Natural Sciences and Engineering Research
Council of Canada (NSERC).
References
Blatchley, E.R.I., Wood, W.L., and Schuerch, P. 1995. UV pilot testing:
intensity distributions and hydrodynamics. J. Environ. Eng. 121:
258–262.
Blatchley, E.R.I., Do Quang, Z., Janex, M.L., and Laine, J.M. 1998.
Process modeling of ultraviolet disinfection. Water Sci. Technol.
38: 63–69.
Blatchley, E., III., Emerick, R.W., Hargy, T.M., Hoyer, O., Hultquist,
R.H., Sakaji, R.H., Schmelling, D.C., Soroushian, F., and
Tchobanoglous, G. 2000. In Ultraviolet disinfection guidelines for
© 2007 NRC Canada
300
drinking water and water reuse, Chapter 3: protocols. National Water Research Institute and American Water Works Research Foundation, Fountain Valley, Calif. p. 64.
Bolton, J.R. 2000. Calculation of ultraviolet fluence rate distributions
in an annular reactor: significance of refraction and reflection. Water
Res. 34: 3315–3324.
Campbell, A.T., and Wallis, P. 2002. The effect of UV irradiation on
human-derived Giardia lamblia cysts. Water Res. 36: 963–969.
Chiu, K., Lyn, D.A., Savoye, P., and Blatchley, E.R. 1999. Integrated
UV disinfection model based on particle tracking. J. Environ. Eng.
125: 7–16.
Clancy, J.L., Hargy, T.M., Marshall, M.M., and Dyksen, J.E. 1998. UV
light inactivation of Cryptosporidium oocysts. J. Am. Water Works
Assoc. 90: 92–102.
Craik, S.A., Finch, G.R., Bolton, J.R., and Belosevic, M. 2001. Inactivation of Cryptosporidium parvum oocysts using medium- and
low-pressure ultraviolet radiation. Water Res. 35: 1387–1398.
Do-Quang, Z., Janex, J.L., and Perrin, R. 2002. Predictive tool for UV
dose distribution assessment: validation of CFD models by bioassays. In American Water Works Association Annual Conference.
American Water Works Association, New Orleans, La., 16–20 June
2002.
Ducoste, J., and Linden, K. 2005. Determination of ultraviolet sensor
location for sensor set-point monitoring using computational fluid
dynamics. J. Environ. Eng. Sci. 4: S33-S43.
Ducoste, J., Linden, K., Royker, D., and Liu, D. 2005a. Assessment
of reduction equivalent fluence bias using computational fluid dynamics. Environ. Eng. Sci. 22: 615–628.
Ducoste, J., Liu, J.J., and Linden, K. 2005b. Alternative approaches
to modeling fluence distribution and microbial inactivation in ultraviolet reactors: Lagrangian versus Eulerian. J. Environ. Eng. 131:
1393–1403.
Graham, D.I., and Moyeed, R.A. 2002. How many particles for my
Lagrangian simulations. Powder Technol. 125: 179–186.
Iranpour, R., Garnas, G., Moghaddam, O., and Taebi, A. 1999. Hydraulic effects an ultraviolet disinfection: modification of reactor
design. Water Environ. Res. 71: 114–118.
Jacob, S.M., and Dranoff, J.S. 1970. Light intensity profiles in a perfectly mixed photoreactor. Am. Inst. Chem. Eng. J. 16: 359–363.
Linden, K.G., Shin, G.A., Faubert, G.M., Cairns, W.L., and Sobsey,
M.D. 2002. UV disinfection of Giardia lamblia cysts in water. Environ. Sci. Technol. 36: 2519–2522.
Liu, D.L., Ducoste, J., Shanshan, J., and Linden, K. 2004. Evaluation
of alternative fluence rate distribution models. J. Water Sup. Res.
Technol. — AQUA, 53: 391–408.
Lyn, D.A., Chiu, K., and Blatchley, E.R. 1999. Numerical modeling of
flow and disinfection in UV disinfection channels. J. Environ. Eng.
125: 17–26.
Macinnis, J.M., and Braco, F.V. 1992. Stochastic particle dispersion
modeling and tracer particle limit. Phys. Fluids A4: 2809–2824.
Munoz, A. 2004. Development of a CFD model for predicting disinfection in a large UV reactor. M.Sc. thesis. University of Alberta,
Edmonton, Alta.
Petri, B.M., and Olson, D.A. 2002. Bioassay validation of computational disinfection models used for reactor design and scale-up. In
Disinfection 2002: Health and safety achieved through disinfection.
Water Environment Federation, St. Petersburg, Fla.
Qualls, R.G., and Johnson, J.D. 1985. Modeling and efficiency of ultraviolet disinfection systems. Water Res. 19: 1039–1046.
J. Environ. Eng. Sci. Vol. 6, 2007
Rodi, W. 1984. Turbulence models and their application — a state
of the art review. 2nd ed. International Association for Hydraulic
Research. A.A. Balkema, Rotterdam, Netherlands.
Rokyer, D., Valade, M., Keesler, D., and Borsykowsky, M. 2002. Computer modeling of UV reactors for validation purposes. In American
Water Works Association — Water Quality and Technology Conference, Seattle, Wash. p. 21
Scheible, O.K. 1987. Development of a rationally based design protocol for the ultraviolet light disinfection process. J. Water Pollut.
Control Fed. 59: 25–31.
Severin, B.F., Suidan, M.T., and Engelbrecht, R.S. 1984. Mixing effects in UV disinfection. J. Water Pollut. Control Fed. 56: 881–888.
US Environmental Protection Agency. 2003a. Long term 2 enhanced
surface water treatment rule — toolbox guidance manual. EPA 815D-03-009. Environmental Protection Agency, Washington, D.C.
US Environmental Protection Agency. 2003b. National primary drinking water legislation: long term 2 enhanced surface water treatment
rule; proposed rule. Fed. Reg. 68: 47639–47688.
Versteeg, H. 1995. An introduction to computational fluid dynamics:
finite volume method approach. 1st ed. Prentice-Hall, Englewood
Cliffs, N.J.
Wright, N.G., and Hargreaves, D.M. 2001. The use of CFD in the
evaluation of UV treatment systems. J. Hydroinform. 3: 59–70.
Zagarola, M.V., and Smits, A.J. 1998. Mean-Flow scaling of turbulent
pipe flow. J. Fluid Mech. 373: 33–79.
List of symbols
A
cp
CL
D
Di
Deqv
Dth
Di
D eqv
Eλ
E
Eλ,xyz
Exyz
E(r)
Eavg
Emax
EED
i
j
k
l
y–z cross-sectional area at UV reactor inlet, m2
volumetric particle concentration, no./m3
Lagrangian empirical constant
UV dose, J/m2
UV dose received by a single particle i, J/m2
reduction equivalent UV dose (RED), J/m2
theoretical reactor UV dose, J/m2
mean of UV dose received by all np particles used a
reactor simulation
mean of reactor MS2 equivalent computed from several
simulations
UV fluence rate at wavelength λ, W/m2
germicidal-weighted UV fluence rate, W/m2
UV fluence rate at wavelength λ and at x–y–z
coordinate, W/m2
germicidal-weighted UV fluence rate at coordinate
x–y–z, W/m2
germicidal-weighted UV fluence rate at radial distance r
from the principal axis of the reactor, W/m2
average germicidal-weighted UV fluence in the reactor,
W/m2
germicidial-weighted UV fluence rate at principal axis
of the reactor (y = z = 0), W/m2
electrical energy dose, kW h/m3
subscript representing individual fluid particles, i = 1,
2, …, np
subscript representing individual concentric rings at
reactor inlet, j = 1, 2, …, m
turbulent kinetic energy
subscript representing each simulation in a series, l = 1,
2, …, ns
© 2007 NRC Canada
Munoz et al.
nj number of fluid particles added to concentric ring
k
np total number of fluid particles injected per simulation
ns number of simulations
N number or concentration of live organisms after
exposure to UV light
N0 number or concentration of live organisms prior to
exposure to UV light
N/N0 survival ratio
(N/N0 )i survival ratio in fluid particle i
(N/N0 )o overall survival ratio at the outlet of the reactor
NLO number of lamps in operation
p-value probability of a type I error
Q volumetric flow rate, m3 /s
Qj approximate volumetric flow rate into jth concentric ring at reactor inlet, m3 /s
r radial distance from principal axis of the reactor,
m
t Lagrangian time coordinate, s
tf total simulation time, s
ti residence time of particle i, s
t Lagrangian time interval in discrete phase model
calculations, s
tns ,−1,0.025 Student t-statistic
301
T fluid temperature, K
ux axial velocity, m/s
ux (r) axial velocity at radial distance r from principal
axis of reactor, m/s
umax axial velocity at principal axis of the reactor
(y = z = r = 0), m/s
SDeqv standard deviation of the MS2 reduction equivalent dose computed from repeated simulations,
W/m2
V irradiated volume, m3
x Cartesian coordinate in direction parallel to
main water flow
y Cartesian coordinate in direction perpendicular
to lamps and main water flow
z Cartesian coordinate in direction parallel to axis
of UV lamps
α0 , α1 , α2 , α3 , α12 least-squares regression coefficients
ε dissipation rate of turbulent kinetic energy,
m2 /s3
ηH hydraulic efficiency
λ wavelength, nm
µ fluid viscosity, kg/ (m s)
ρ fluid or particle density, kg/m3
power output per lamp, kW
© 2007 NRC Canada
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