Proceedings of 2013 IAHR Congress © 2013 Tsinghua University Press, Beijing

ABSTRACT: With the aim of optimising contact tank design through numerical model simulations,

results are presented herein of an experimental and computational fluid dynamics (CFD) study in a scaled

laboratory model. Three-dimensional numerical simulations of flow and transport characteristics were

conducted using a Reynolds Averaged Navier-Stokes equation approach. Experimental results were

obtained through Acoustic Doppler Velocimetry measurements and a series of conservative tracer

experiments. Focus is given on turbulent structures and undesirable flow patterns that lead to a reduced

disinfection efficiency, through phenomena such as short circuiting and recirculation zones. The

laboratory data analysis indicates extensive three-dimensionality as a result of the current inlet geometry

with a confirmed negative impact on the disinfection performance of the contact tank model, as

demonstrated by Residence Time Distribution curves. Disinfection performance is evaluated through

hydraulic efficiency indicators commonly used in the industry to monitor field-scale disinfection facilities.

Correlations between CFD and experimental data confirm the adequate reproduction of hydrodynamic

conditions and reinforce the predictive capabilities of numerical models as tools to simulate field scale

tanks or optimize existing designs.

KEY WORDS: Contact Tanks, Acoustic Doppler Velocimetry, Tracer Experimentation, RANS,

Disinfection Performance

1 INTRODUCTION

Serpentine contact tank units suggest plug flow to be the ideal hydrodynamic condition at which

disinfection performance is maximized (Falconer and Tebbutt, 1986; Markse and Boyle, 1973). Under

such flow conditions, disinfectant transport becomes ideal by remaining in the tank for a uniform time

interval whilst achieving the desired disinfection. However, previous studies (e.g. Teixeira, 1993) indicate

that flow exhibits a residence time distribution (RTD) which can be significantly different from what is

dictated by plug flow. The shape of the tracer RTD curves can provide an insight into the hydrodynamic

and mixing conditions, as explained by Levenspiel (1999). For example, the residence time at 10%

cumulative RTD constitutes a crucial indicator (t10) for the evaluation of disinfection efficiency and

design suitability at water treatment works. Digression from plug flow can be attributed to the complex

hydrodynamics, namely short-circuiting and recirculation zone formation (Kim et al., 2010).

Short-circuiting occurs when particles pass through a reactor quicker than the theoretical hydraulic

residence time. Recirculation zones not only promote short-circuiting (since they occupy a considerable

part of the tank volume) but they also trap solutes and particles (or pathogens), which are then retained in

the tank for a longer period than the theoretical hydraulic residence time. The occurrence of such flow

patterns has a detrimental effect on the overall efficiency, because contact times of pathogens with the

CFD Study of Flow and Transport Characteristics in Baffled

Disinfection Tanks

Athanasios Angeloudis

Research Student, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK, Email:

angeloudisa@cf.ac.uk

Thorsten Stoesser

Professor, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK. Email: stoesser@cf.ac.uk

Dongjin Kim

Research Assosiate, School of Civil and Environmental Engineering ,Georgia Institute of Technology ,

Atlanta, GA 30332,USA. E-mail: dkim46@gatech.edu

Roger Alexander Falconer

Professor, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK. Email:

FalconerRA@cf.ac.uk

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disinfectant are either too short (insufficient treatment) or too long, which can cause disinfection

by-products.

Computational Fluid Dynamics (CFD) techniques have been implemented widely to simulate flow

conditions and mixing processes during disinfection tank operation (e.g. Kim et al., 2010; Khan et al.,

2006; Wang and Falconer, 1998). Even though the performance of computational models has vastly

improved, there is still concern with regards to the accuracy and ability of numerical models to predict the

turbulence characteristics as well as the actual shape of RTD curves. For reliable simulation results, CFD

models require validation by comparisons against experimental data. However, the availability of such

data in water treatment facilities through in situ experimentation is scarce or limited to “black-box” type

tracer injections due to difficulties in obtaining hydrodynamic or solute transport measurements inside

field scale tanks.

Results are presented herein of an experimental and CFD study in a scaled baffled tank laboratory

model. 3D numerical simulations of flow and tracer transport characteristics were conducted using a

Reynolds Averaged Navier-Stokes (RANS) equation approach. Experimental results were obtained

through ADV measurements and a series of conservative tracer experiments in a scaled baffled contact

tank model in Cardiff University. In this study, tracer monitor points are not only limited at the outlet

section of the tank but are spread out across the geometry for a more thorough overview of mixing

processes. Core objectives of this study were to: (a) inform modelling practises by presenting

hydrodynamic and tracer transport results from a physical model, (b) compare hydrodynamic and solute

transport simulations with available laboratory data so as to examine the validity and potential of the 3D

simulations, and (c) discuss the potential of refining and tailoring CFD models to be used as tool for the

design and retrofitting of water disinfection infrastructure.

Figure 1 (a) Schematic view of laboratory model tank configuration including vectors indicating main streamwise flow direction ; (b) channel inlet and compartment 1 cross-section (dimensions in mm). Experimental data availability is illustrated in both sketches.

2 METHODOLOGY

2.1 Laboratory Model Setup and Experimental Data

Experimental data was acquired from a scaled disinfection tank, which exhibits standard features of

a baffled contact tank, i.e. it is separated into a certain number of compartments where the flow meanders

due to the baffles being arranged in an alternating fashion. The model (denoted as CT-1 in this study) is Lt

= 3.0 m long, Wt = 2.0 m wide and Zt = 1.2 m deep. Internal baffles, 12 mm thick, divide the tank into 8

compartments of Wc = 0.365 m width. The inlet is located in the northeast corner of the tank and consists

of an open channel of width Wc and depth Hi of 0.30 m, which is approximately 1/4 of the tank depth.

Honeycombs located in the approach channel promote uniformity when the flow enters the system. The

water level, controlled via a rectangular sharp crested weir at the outlet, was measured to be at Ht = 1.02

m. A constant flow rate of 4.72 l/s was set when collecting velocity measurements corresponding to a bulk

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velocity Ub of 12.5 mm/s and a tank theoretical retention time (T=V/Q) of 1265 s. Hydrodynamic

measurements were collected via a 3D ADV technique manufactured by Nortek AS yielding a total of

3200. Pulse tracer experiments involved Rhodamine WT injections at the inlet, while Cyclops-7

submersible sensors monitored fluorescence levels for a sufficient duration (10,000s) at designated

locations. Sensitivity analysis and Instrument Calibration tests were performed prior to any

experimentation to ensure the validity of the measurements. Figure 1 illustrates the laboratory model’s

main geometric features and also depicts velocity measurement and tracer sampling locations. The data

set of the experimental results is denoted here as CT-EXP.

2.2 Numerical Methods and Computational Model Setup

The RANS simulations of the hydrodynamics are performed using an in-house code implementing a

finite-volume approach on a structured orthogonal grid. A Semi-Implicit Method for Pressure-Linked

Equations (SIMPLE) is applied to couple the pressure to the velocity field and the standard k-ε turbulence

closure to compute the Reynolds Stresses. The geometry and boundary conditions of the CFD simulations

are chosen in accordance with the laboratory experiment. At the inlet surface, a Dirichlet boundary

condition is applied for velocity and turbulence parameters. Transversal V and vertical W velocity

components are set to zero and a uniform streamwise velocity U is assumed with a magnitude based on

the experimental study flow rate. A Neumann condition with mass conservation is imposed at the outlet

surface for all variables. The water surface is treated as a rigid, frictionless lid with no shear stress. Side

walls, tank bottom and internal baffles are considered smooth to match the low roughness surfaces of the

laboratory model and low Reynolds number wall functions are employed.

Once the steady state flow field is obtained, the transport of a conservative tracer scalar quantity C is

simulated by solving the three-dimensional Reynolds-averaged advection-diffusion equation. The

turbulent Schmidt number is given the standard value of Sc = 0.7 as suggested in Launder (1978). The

low-diffusive and oscillation-free Hybrid Liner/Parabolic Approximation (Zhu, 1991) approximates the

convective term and the central differencing scheme the diffusion term. The implicit Euler scheme is

employed to integrate the equation in time. Tracer transport is based on the values of velocity Ui and

viscosity vt which are obtained from the converged steady-state hydrodynamics simulation. The tracer

injection is simulated at the inlet of the contact tank for 40 time steps of Δt = 0.25 sec in order to

reproduce the injection interval of the experimental study.

The simulation reproducing the experimental results in CT-1, referred to here as CT-S, were

produced using a non-uniform grid of 161×77×42 cells featuring a variable geometrical grid step to

ensure a more refined grid in areas of interest (i.e. near baffles, walls and inlet flow). The mesh was

subject to a convergence analysis to ensure mesh independence for hydrodynamic and tracer transport

results (Angeloudis et al, 2013).

3 RESULTS AND DISCUSSION

3.1 Flow and Tracer Transport Validation

Figure 2 Velocity vectors in the centre-plane of (a) Compartment 1 and (b) Compartment 2. The blue vectors represent CT-S simulation results and the black vectors represent the ADV (CT-EXP) data

The capacity of the numerical models to reproduce accurately hydrodynamics of the experimental

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study is examined in Figures 2 and 3. From the vector plot in Figure 2(a) it is clear that the first

compartment is dominated by one large recirculation zone and significant two-dimensionality in the

longitudinal plane. This flow pattern is a result of the inlet configuration which causes the flow to enter

the first chamber by means of a high momentum jet. The recirculation zone occupies a region between

0.20< x/Wt < 1.00 and is centered at (0.60, 0.40). The region between 0.00< x/Wt < 0.20 and 0.00< z/Ht <

0.80 is dictated by very low flow velocities and can arguably be classified as either a dead zone or a

slower portion of the larger vertical recirculation. The conditions of compartment 1, result in subsequent

recirculation zones occurring in compartments downstream (Figure 2b) which combined with the

expected recirculation zones around baffle lees demonstrate the complexity of the tank hydrodynamics. In

terms of compliance between experimental and computational results, main hydrodynamic features are

captured accurately (i.e. flow pattern and magnitude). This is more quantitatively demonstrated in Figure

3, from which it can be seen that the simulated magnitudes of streamwise velocity (U) and turbulent

kinetic energy (k) matches closely the experimental data. From the velocity profile in particular, the

presence of reverse flow is dominant in compartments 1 and 2 but diminishes toward the outlet as seen by

the profile in compartment 8 where experimentally and computationally it resembles uniform flow.

Figure 3 Vertical profiles of a) U/Ub and b) k/Ub2 in compartments 1, 2 and 8 as measured experimentally (CT-EXP)

and predicted computationally (CT-S). Positive are considered the velocities that follow the streamwise direction in each compartment.

Figure 4 RTD curves produced for experimental and computational data at (a) Compartment 1 and (b) Compartment 3: experimental RTDs (left), computational RTDs (right).

Figure 4 presents tracer RTD results obtained experimentally (left) and as predicted computationally

(right) for concentration sampling locations in compartments 1 and 3 respectively. The numerically

predicted RTDs are much smoother than the measured ones, the latter exhibiting the unsteadiness of the

flow. Tracer injected at the inlet is clearly transported by means of convection, as shown by the

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correlation of figures 2a and 4a. The flow in 2a initially appears only in the top layer of the vertical

profile by means of the inlet jet flow. It then appears in the bottom layer (reverse flow), due to the vertical

recirculation zone, and appears in the middle layer even later. This pattern appears to apply for tracer

transport, by observation of concentration peaks in 4a. The concentration fluctuations in compartment 1

suggest unsteadiness in the flow which is not captured by the RANS simulation. However, for

compartment 3 (Figure 4b), as flow becomes more uniform and the initial three-dimensionality caused by

jet mixing diminishes, RANS simulations produce more accurate RTD predictions which consistently

improve towards the outlet. This is particularly interesting as RTDs are determinant for evaluating the

performance of disinfection facilities by deriving Hydraulic Efficiency Indicators (HEI).

Figure 5 Correlation between experimental and computational results through Hydraulic Efficiency Indicator (HEI) prediction: (a) Dispersion Index (σ2), (b) short-circuiting indicator t10 and (c) Morrill Index (Mo)

Figure 5 presents HEI results for each compartment with a view to monitoring hydraulic

performance in each compartment of the tank. The indicator t10, corresponds to the normalized time since

injection for the passage of 10% of the injected tracer mass through the monitoring section and serves as a

measure for severity of short-circuiting. The HEI Mo (Morrill index), defined as Mo = t90/t10, where t90 is

the normalized time since injection for the passage of 90% of injected tracer mass. The dispersion index

σ2, defined σ

2 = σt

2/tg

2 where σt

2 is the variance of the RTD curve and tg is the normalized time to reach

the centroid of the effluent curve (Markse and Boyle, 1973). HEIs Mo and σ2 reflect the amount of

mixing in the disinfection tank. The indicators are classified into “poor”, “compromising”, “acceptable”

and “excellent” in accordance with guidelines of US EPA (AWWA,1991) and Van der Walt (2002).

Overall, numerical model predictions provide a reliable indication of the disinfection performance

demonstrated by the close agreement against measured results. The non-ideal nature of hydrodynamics

and mixing processes in the contact tank is observed inside the tank, where compromising or even poor

performance is encountered at some tracer sampling points. HEIs drawn from compartments 1-2 (marked

grey in the figure) are considered unreliable as the monitor points are inside recirculation zones where

tracer mass reappears multiple times during the sampling duration (due to convection), thus distorting the

accuracy of RTD curves and their corresponding HEIs.

The comparison of experimental and computational results presented herein, indicates the ability of

the current CFD approach to predict flow and solute transport conditions within disinfection tank

facilities. This can be refined to produce design specific parameters (i.e. HEIs) that can either aid in the

performance of a design prior to construction, or the optimization of an existing design through

retrofitting by evaluation of potential improvements computationally.

3.2 Retrofit of Current Geometry

The optimization of the CT-1 design was examined experimentally by Rauen (2005) in an effort to

neutralize the current inlet water jet that results in the dominance of complex flow in compartments 1 and

2. By implementing a cross-baffling configuration (CT-O), the inlet three-dimensionality is constrained to

a lesser extent of the tank volume (Figure 6a). The impact of retrofitting the design was evaluated through

outlet HEIs (Table 1) which suggest superior performance. The available data presented an opportunity to

quantitatively examine and confirm the predictive capabilities of the current CFD approach. A numerical

model of the CT-O design was setup to simulate a tracer experiment similar to the one undertaken by

Rauen. The present simulation HEIs closely matched the measured values as shown by the percentile

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deviations of 3.6, 0.1 and 1.8% for σ2, t10 and Mo respectively.

Figure 6 Velocity magnitude (Vmag) contour plots for (a) optimized baffling configuration (CT-O) and (b) Embsay CT simulation (CT-Embsay) at free surface (z/Ht = 1.00). The flow direction in each case is illustrated through the white arrows

3.3 Simulation of Field-Scale Model

The inability of performing hydrodynamic and solute transport measurements inside field scale

tanks has led to the use of physical models as main source of experimental data for contact tank research.

Teixeira (1993) reported an experimental analysis of 3-D hydrodynamic conditions and mixing processes

in two geometrically scaled models (1:4, 1:8) of the contact tank located in the Embsay Water Treatment

Plant, designed and operated by Yorkshire Water, in England, UK. Khan et al. (2006) supplemented this

study by presenting CFD results, aiming at the reproduction of the measurements in the 1:8 scale model.

For the purposes of this study the field-scale tank (CT-Embsay) was directly simulated (Figure 6b) as a

further example of CFD modelling in disinfection tank design, to (a) compare hydraulic efficiency of the

CT-Embsay with available data (Table 1), and (b) highlight the potential and advantages of field-scale

contact tank simulations.

Table 1 Different CT Design Outlet Hydraulic Efficiency Indicators

CT Design σ2 t10 t90 Mo

CFD Exp CFD Exp CFD Exp CFD Exp

CT-1 0.092 0.097 0.750 0.73 1.539 1.54 2.05 2.11

CT-O 0.053 0.055 0.781 0.78 1.294 1.32 1.66 1.69

CT-Embsay 0.119 0.126 0.649 0.66 1.422 1.51 2.19 2.27

The results from the physical model and the field-scale simulation agreed quite well, as suggested by

the HEI comparison (Table 1). A pattern which arises is that CT-Embsay mixing indicators have

consistently greater values compared to the measured ones in this scaled model. Even though the

deviation is within 4-6%, it could be justified by the effect of scaling, as a greater turbulence level in

CT-Embsay (i.e. higher overall Reynolds No.) could encourage the more efficient mixing conditions

which are reflected by the HEIs. Physical models are characterized by an inability to simultaneously scale

both the turbulence and the decay rates, which means that model predictions will not reflect prototype

conditions, and in certain cases (e.g. recirculation zones) the results can be in serious error. In CFD

models, on the other hand, all parameters are modeled at prototype dimensions, thus the turbulence

mixing processes and decay rates are not distorted by scaling (Falconer, 1990).

Refinement of CFD models enables the incorporation of kinetics (e.g. Zhang et al., Greene et al.) by

applying bacteria inactivation predictive models in the scalar transport module, thus enabling the

simulation of the disinfection process within the computational domain. However, such simulations have

so far been limited to scaled models and have not been tested against field scale results. However, such

CFD approaches have the potential to fully optimize disinfection operation by providing information

about the chemistry of the process, such as optimum disinfectant dosing or bacteria inactivation achieved

throughout the design.

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4 CONCLUSIONS

In this study a RANS based CFD technique was employed to predict flow, transport processes and

hydraulic efficiency in serpentine contact tanks. The CFD model was first validated by comparing

simulated velocity and tracer prediction with experimental data obtained from an Acoustic Doppler

Velocimetry measurement program and conservative tracer experiments in a physical contact tank model.

The analysis was complemented with the evaluation of contact tank performance using disinfection

specific Hydraulic Efficiency Indicators (i.e. t10, σ2 and Mo). These were calculated at various locations

inside the tank rather than only at the outlet, which provided a better understanding of the mixing

processes inside the model and not treat the contact tank operation as a “black box”.

The predictive capabilities of the numerical model were then demonstrated by simulating a

retrofitted design of the laboratory model, reaffirming the implementation of validated CFD approaches

as suitable design tools. The scale of the problem was then expanded by simulating a field-scale tank

highlighting how the computational simulations do not have to be constrained by the uncertainties and

scaling problems leading to the Froude-Reynolds conflict of small-scale physical models. It is shown that

a 3-D CFD model can aide in and/or guide the design of contact tanks by providing reliable predictions of

flows, scalar transport and hydraulic efficiency. There is the need to further refine the model with more

sophisticated kinetic bacteria inactivation models which could more specifically describe disinfection

reactions (i.e. disinfectant decay, pathogen inactivation, by-product formation). Such refinements would

provide even more detailed analyses of newly designed or retrofitted CTs.

ACKNOWLEDGEMENT

The first author acknowledges the financial support of Halcrow, a CH2M Hill company.

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