1. 1
Piping System Dead Legs - Sustainable Control of Biofilm in High Purity Water
B. Meehan, B. Corcoran
School of Mechanical & Manufacturing Engineering, Dublin City University, Dublin 9, Ireland
Abstract
Contamination of high purity water systems is a significant problem in the pharmaceutical industry. A major factor in contamination is
the onset, growth and proliferation of a microbial organism known as biofilm. This organism is prevalent under certain conditions.
Investigating the conditions in which biofilm may occur in dead legs associated with high purity water systems is the focus o f the
paper’s research. Research is focused on different rates of fluid velocity for a set loop temperature coupled with varying lengths of the
dead leg branch on an experimental rig. A three dimensional Computationl Fluid Dynamic (CFD) software modeling of the fluid and
temperature profiles using the Fluent software application and a two dimensional thermograhic analysis to profile the temperature
distribution along the length of the dead leg were applied.
Keywords: Biofilm; Dead Leg; Temperature; Fluid Flow Velocity; Fluent; CFD; Thermography
Introduction
Biofilm was initially discovered more than sixty years ago and
effects a wide industrial spectrum [1]. Biofilm has significant
industry costs associated as a result of the effects to process
performance, decrease of product quality and quantity by
microbial attack [2]. Biofilm dispersion has become widely
recognized as a natural phenomenon associated with the
terminal stage of biofilm development as presented in Figure
1. Understanding the behavioural process characteristics of
bacteria is of great importance in regulating biofilm structure.
Controlling the dispersion of biofilm is also very significant in
inhibiting the spread and persistence of the organism. Biofilms
have the ability to form on living and non-living surfaces. The
microbial cells growing in biofilm are different from
planktonic cells of the same organism. The planktonic cells
differ from a physiological point of view in that they are single
cells that may float or swim in a liquid medium [3]. On the
surface of stainless steel the initiation and propagation of
biofilm into crevices is a function of time [4].
Biofilm micro colonies follow viscous fluid behaviours in that
they deform under fluid shear stress. They oscillate at high
shear forces and subsequently lose their surface attachment
under these forces and both drift and roll over the colonized
surface. The design of this microorganism aids its
proliferation. Mushroom-like micro colonies with water
channels are the optimal situation for biofilm to access
nutrients. The nutrients are imparted to the bacteria via the
water channel at low flow velocity. Each micro colony moves
in the bulk fluid at high fluid velocity [5]. In a nutrient poor
system with high linear velocity and shear forces, organisms
can only remain in the system if they are resident if the
hydrodynamic boundary layer that exists along the walls of the
distribution piping system. The thickness of the boundary
layer has a significant effect on the biofilm morphology. A
thin boundary layer is conducive to the promotion of dense
and compact biofilms [6].
Figure 1: Processes governing biofilmformation [1].
Research has indicated that there is high risk of microbial
contamination at ambient temperature. The research has
shown that when the system had constant flow over a seven
day period the probability of the biofilm growth was reduced
by a factor of forty. It showed less bacteria were detached and
less colonies were formed on the piping surface. This fact,
coupled with the finding that elevated temperature can control
the biofilm growth, are key criteria for high purity fluid
systems [7].
System Dead Leg’s
The design and operation of high purity waters systems
employed in the pharmaceutical industry is significantly

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affected by dead legs. A dead leg is an area of entrapment or
an inactive part in the piping system which may lead to
contamination of the product, in this case high purity water.
‘The circulation temperature of the water systemis dictated by
either the required microbiological specification or the
required temperature for usage. To achieve a microbial limit
of less than 10 CFU/100 ml, a minimum continuous
temperature of at least 80oC is required (80 °C is the accepted
minimum temperature that self sanitization will occur)’ [8].
This technique will inhibit the spread of biofilm but will not
eliminate the bacteria from the water system.
Systems that comprise of stagnant or low fluid velocity flow
are subject to bacterial growth. The benchmark within the
pharmaceutical industry suggests that a minimum velocity of
between 1.0-3.0m/s is needed to control the bacterial limits of
such systems although many users apply the rule of thumb of
0.3m/s [9]. It has been found that there is no single loop
velocity that can ensure high purity recirculating water
systems can be designed and operated to cease biofilm
formation. It has been shown that high flow rates of water
alter the pace of bacteria propagation but do not prevent
attachment to the pipe wall surface [10].
Experimental testing consisted of using a single pipe loop.
This type of configuration is commonly used in
pharmaceutical plants. The system consists of a storage tank,
pump, sampling points and flow regulation meters. The piping
material used was 316L high polished stainless steel. 316L
stainless steel consists of properties that are highly resistant to
corrosion. Sanitisation chemical agents such as chlorine and
hydrochloric acid are extremely corrosive at high temperatures
(600 – 800) [11]. Stainless steel may be polished to a
roughness magnitude that reduces significantly the ability of
bacteria to lodge in microscopic crevices where it can grow,
proliferate and contaminate the system [12].
System Dead Leg’s Test Section
Three different tee sections were applied to the experimental
main loop to obtain temperature profiles for the dead legs. The
dead leg is measured by the term L/D where “L” is the leg
extension from the center line of the main loop normal to the
flow direction. “D” is the outer diameter of the tee section.
The temperature was recorded at the base of the dead leg using
a T type thermocouple. The loop temperature is measured
upstream of the branch entrance and the temperature controller
is set to the storage tank temperature.
The tee sections were fabricated from the same material as the
main loop, 316L stainless steel. Tee sections of 300mm (6D),
200mm (4D) and 100mm (2D) were used. Materials used
conformed to the American Society for Testing and Materials
(ASTM), specification number ASTM A479 T 316L [13].
Figure 2: Schematic of 6D dead leg configuration [13].
Experimental Results
2D Dead leg: The results presented for Figure 3 display the
temperature profile at the bottom of a 2D dead leg. Fluid flow
velocities of 0.28m/s, 0.56m/s and 1.03m/s are presented. The
graph shows a saw tooth type output at steady state which is
representative of temperature controller output toggling from
an on/off state with a one degree range. The loop temperature
is measured upstream of the dead leg before the branch
entrance and Td denotes the temperature readings for the
bottom of the dead leg for the different velocities. The three
velocity outputs represent convective type mixing in the 2D
dead leg with the 1.03m/s velocity reaching steady state
temperature fastest indicating a higher magnitude of
convective mixing in the dead-leg.
Figure 3: 2D dead leg bottomtemperature profile for fluid
flow velocities.
4D Dead Leg: The 4D dead leg configuration results are
presented in Figure 4. The results display the loop temperature
reaching steady state of 810C at approximately 6500s with the
with a saw tooth profile. The 1.03m/s profile hits set point of

3. 3
800C. The 0.56m/s dead leg temperature reaches set point of
800C but is noisy. The 0.56m/s indicates a crossover point is
approaching in the thermal gradient of the dead leg. The
0.28m/s temperature profile reaches a top temperature of
approximately 620C. The 0.28m/s profile is representative of a
crossover of heat transfer form convective to diffusive.
Figure 4: 4D dead leg bottomtemperature profile for fluid
flow velocities.
6D Dead Leg: The 6D configuration results are presented in
Figure 5. The loop temperature reaches a steady state
temperature of 810C. The temperature profile for the loop
velocity of 1.03m/s has a noisy output approaching the set
point of 800C. This profile is representative of turbulent
thermal diffusive type mixing. The 0.56m/s temperature
profile displays a diffusive heat transfer that reaches a
maximum temperature of approximately 330C. The 0.28m/s
temperature profile displays a similar type profile to the
0.56m/s profile except the temperature maximum is
approximately 250C.
Figure 5: 6D dead leg bottomtemperature profile for fluid
flow velocities.
Computational Fluid Dynamics
Computational Fluid Dynamics, CFD, is a collective term
used to describe the analysis of fluid flow and heat transfer
systems and associated phenomena using computer
simulations. The CFD codes are structured around a numerical
algorithm that is suited for fluid flow problems [14].
CFD codes consist of three main elements: the preprocessor,
which defines the geometry of interest, grid generation and
physical problem of interest; the solver, which solves the fluid
equations on the given grid using in this case the finite volume
method; and the post processor, which presents the results
visually in a 2D/3D surface contours, graphs and tables. The
validation of models in CFD is difficult due to the complex
nature of the algorithms. To aid in validation, experimental
data and research reports can be used [14]. The experimental
data is a constituent element of this report and is used to
support the computer modeling results.
The Finite Volume Method (FVM): This is a numerical method
which discretizes a volume. There are three steps in which this
is achieved. The first step is the grid generation where the
domain is divided into control volumes which the governing
equations can be integrated over. The accuracy of these
equations is a function of how fine the grid distribution is. The
second step is discretization. After the structure is divided into
smaller control volumes, the governing equation is applied to
each control volume. The third and final step is the solution of
the discretized equations which is solved by a system of
equations [14].
Governing equation in turbulent pipe flow: For simulating
turbulent fluid models, the Reynolds-Averaged Navier-Stokes
(RANS) simulations were applied. ‘In a turbulent flow the
governing equations are the continuity and Navier-Stokes
equations combined with the transport equation for the two
quantities turbulent kinetic energy, k, and dissipation of
kinetic energy, ε [15]’.
The k- ε model equations: The k- ε model is derived for a high
Reynolds flow and the coefficients are derived empirically.
Experimental evidence from Versteeg and Malalasekera
[2007] supports the Boussinesq’s 1877 proposal that when
subjected to the normal Reynolds stresses the formula
generates the correct result. The k- ε model is not integrated at
the walls; instead, the production and dissipation of the kinetic
energy is specified using a near wall treatment. The wall
treatment applied in this instance was an enhanced wall
treatment.
Model generation and meshing
The dimensions for the 6D, 4D and 2D tee sections were
modeled to specification using solid works. The geometries
were then imported into ANSYS work bench in .IGS file

4. 4
format. The fluid flow (Fluent) application was launched in
the analysis systems tree. The geometry, once imported, was
meshed.
Meshing: The meshing adopted was tetrahedrons using the
patch conforming method. Tetrahedron cells are optimal for
capturing turbulent conditions in the control volumes. The
patch conforming is a bottom up approach as can be seen in
Figure 6. The meshing starts from the edge, face and then
continues to the volume meshing. All faces and their
boundaries are respected and meshed. This is the preferred
choice for clean CAD drawings.
Near wall treatment: The mesh at the wall face must account
for the structure of the flow. This means that a minimum of
five cells is required to resolve the boundary layer and was
applied in the mesh model. In the experimental results, the Y+
values were 35 which placed the resolution within the buffer
layer region, ensuring reasonable resolution accuracy [16].
The boundary layer mesh, which is the outer circumference
layer, is presented in Figure 6. Instead of solving the flow to
the viscous sub layer a wall function was applied to allow the
solver to progress [16]. The wall function applied was the
enhanced wall treatment. The assumption underpinning a wall
function is that the Y+ value is between 30 and 500, which in
this set of experiments is true.
Figure 6: Meshing configuration for the dead leg.
ANSYS Fluent 14.5.0
Solution set up
The ANSYS Fluent 14.5 software CFD package with a 3D
model was used to set up the solution. A pressure based,
absolute and steady state solver was applied. The viscous
model chosen was a standard k-epsilon (2-eqn) and the near
wall treatment applied was an enhanced wall treatment. The
materials consisted of water for the fluid and stainless steel for
the solid. The cell zone condition ‘part_1’ was set to fluid.
The boundary conditions consisted of four variables: interior
part_1; the velocity inlet, where the input velocity and fluid
temperature were given values relative to each iteration of test
being run; the velocity output, which was set to a pressure
outlet; and the wall momentum, where the momentum tab was
set to ‘no slip’ and thermal tab was set to convection with the
heat transfer coefficient set at 50W/m2K and a free stream
temperature of 210C. The reference values were set relative to
the input variables.
Solution
Scheme: The solution method used for the pressure-velocity
coupling was a SIMPLEC scheme as the computing time is
more efficient than that of the SIMPLE method. ‘The SIMPLE
scheme is a robust method that produces rapid stabilization of
the veolocity and pressure as seen by their respective
convergence histories after five iterations. The SIMPLEC
(SIMPLE-Consistent) algorithm by Van Doormal and Rathby
(1984) follows the same iterative steps as in the SIMPLE
algorithm. The main difference between the SIMPLEC and
SIMPLE algothtims is the discretized momentum equations
are manipulated so that the SIMPLEC velocity correction
formula omits terms that are less significant than those omited
in the SIMPLE algorithm [17].’ The SIMPLEC solution
method is less computationaly expensive, and therefore faster,
compared to the SIMPLE sloution method.
Spatial discretisation: The spatial discretisation had six
variables to set: gradient, pressure, momentum, turbulent
kinetic energy, turbulent dissapation rate, and energy. The
gradient was set at Green Gauss Node based. This method is
more accurate and unlike the Green Gauss cell based method
it provides second order accuracy with a tetrahedral mesh. The
pressure was set to the ‘PRESTO’ scheme which is suited for
pressure gradients involving rotating flows as it provides
improved pressure interpolation in swirling flow situations
[18]. The remaining four variables, momentum, turbulent
kinetic energy, turbulent dissapation rate and energy, were set
to second order upwind as higher-order accuracy is achieved
at cell faces through a Taylor series expansion of the cell-
centered solution about the cell centroid [18]. The second
order upwind accuracy is a minimum requirement as per the
Journal of Fluids Engineering. First order accuracy will
converge faster but lacks accuracy.
For the solution controls the default option was selected and
the monitors variable residuals were set to 10-6 order of
accuracy, to ensure the accuracy of the the conveged solution.
The standard initilization option was chosen for the solution
initilization. The calculation was set to 5000 iterations.
Computational time required for solutions to converge
depended on the number of elements in the domain. For the
6D mesh which contained the largest the number of elements
(1.4e106) the computational time for convergence was
approximately fifteen hours.

5. 5
Fluent Results
Results for velocity, temperature and wall shear stress are
presented in Figures 7, 8 and 9 respectively for the 0.28m/s
loop velocity 6D dead leg. The thermal gradient for the main
loop velocity of 0.28m/s presented in Figure 8 displays a
temperature of 290C at the bottom of the dead leg. The loop
velocity profile in Figure 7 is consistent with the input
variable of 0.28m/s and displays the velocity drop off above
the inlet to the tee section. The velocity profile at the tee inlet
shows turbulent eddies which are consistent with convective
type mixing. The wall shear stress presented in Figure 9 shows
shear stresses along the main loop of approximately one
pascal.
Figure 7: Fluent velocity profile for 6D dead leg at loop
velocity of 0.28m/s.
Figure 8: Fluent temperature profile for 6D dead leg at loop
velocity of 0.28m/s.
Figure 9: Fluent wall shear stress for 6D dead leg at loop
velocity of 0.28m/s.
Figure 10: Fluent velocity profile for 6D dead leg at loop
velocity of 1.03m/s.
Figure 11: Fluent temperature profile for 6D dead leg at loop
velocity of 1.03m/s.

6. 6
Figure 12: Fluent wall shear stress for 6D dead leg at loop
velocity of 1.03m/s.
The Figures 10, 11 and 12 display the profiles for velocity,
temperature and wall shear stress respectively for the 6D dead
leg with a loop velocity of 1.03m/s. The contours for the
velocity in Figure 10 are similar to that of Figure 7 at the inlet
entrance and exhibit a convective type mixing occuring at the
inlet entrance. The loop velocity is consistant at approximately
1m/s in the main body of the loop. Figure 11 displays a slight
thermal gradient and temperature reading at the bottom of the
dead leg of 75oC. The wall shear stress in Figure 12 is a
approximately 10 pascals upstream and 7 pascals downstream.
The wall shear stress can be seen at the inlet of the
downstream section of the dead leg and tapers off at 1D into
the deadleg.
Figures 13, 14 and 15 display velocity, temperature and wall
shear stress profiles respectively for the 2D dead leg with a
loop velocity of 0.28m/s. The velocity contours are consistent
again with a convective type mixing profile at the inlet of the
dead leg. The loop velocity is well matched to the model set
up value of 0.28m/s. The temperature at the bottom of the
dead leg is 73oC with a slight thermal gradient in Figure 14.
Figure 13: Fluent velocity profile for 2D dead leg for loop
velocity of 0.28m/s.
Figure 14: Fluent temperature profile for 2D dead leg for
loop velocity of 0.28m/s.
The wall shear stress in Figure 15 displays a value of between
one and two pascals similar to Figure 9 with no significant
wall shear stress in the dead leg.
Figure 15: Fluent wall shear stress for 6D dead leg at loop
velocity of 1.03m/s.
Thermography
Thermography or thermal imaging is a method of determining
the spatial distribution of heat in objects by transforming an
infrared image into a visible image.
‘Emisstivity (ε) is a function of wavelength, temperature, body
material and surface conditons of the radiating body. Spectral
emissivity of a material is a function of both the temperature
and the operating wavelength and is generally expressed by a
complex function ε(λ,T) [19]’. The emissitivity of polished
steel is low at 0.075. Infra red (I.R.) scanning requires
emisstivity levels close to one to accurately determine the
temperature. The dead legs were wrapped in insulating tape
and the emisstivity setting altered to 0.9 to give the surface of
the polished stainless steel the effect of a black body in order
to enable the I.R. camera to accurately measure the heat
signature. Surfaces with an emissivity of <0.60 make reliable

7. 7
and consistent determination of actual temperatures
problematic. The lower the emissivity, the more potential error
is associated with the Imager's temperature measurement
calculations. This is also true even when adjustments to the
emissivity and reflected background adjustments are
performed properly.
Results
Figure 16 presents the I.R. scanning of the 6D dead leg in a
two dimensional profile for the 0.28m/s, 800C loop velocity
and loop temperature respectively. The variation of the
temperature at the deadleg inlet is due to the poor adhesion of
the insulation tape at the curved tee junction affecting the
emmistivity value. The profile displays a thermal gradient in
the dead leg. Figure 17 presents 6D dead leg I.R. scan for the
1.03m/s, 800C loop velocity loop temperature respectively.
The thermal gradient has been eliminated with the increase in
loop velocity to 1.03m/s.
Figure 16: Infra-red thermography profile of 6D dead leg at
fluid velocity flow of 0.28m/s.
Figure 17: Infra-red thermography profile of 6D dead leg at
fluid velocity flow of 1.03m/s.
Disscussion
Three different approaches were adapted to investigate the
thermal characteristics of dead legs with different fluid flow
velocities and dead leg depths: experimental rig, CFD and
infra red (I.R.) scanning. The experimental results for the three
approaches are presented in Tables 1, 2 and 3.
Table 1: 6D Dead leg bottomtemperature for CFD, IR
Scanning and experimental rig.
Velocity (m/s) CFD (o
C) IR Scanning (o
C) Rig (o
C)
0.28 29 29 26
0.56 35 35 34
1.03 75 77 78
6D Dead Leg
Table 1: Results presented in the 6D Dead Leg Table show
that the three methods concurred for the temperatue readings
at the bottom of the dead leg. The I.R. scan profile for the
velocities 0.56m/s and 1.03m/s matched with the CFD models
for the respective velocities indicating that the computer
model and I.R. scanning were successfully applied to the
problem as the thermocouple readings from the rig were from
a calibrated instrument. The thermal profile of the CFD Fluent
model showed a higher degree of accuracy than the I.R. scan.
This shows that the resolution from the CFD is more sensitive
than the readings taken using the I.R. scanning camera.
Table 2: 4D Dead leg bottomtemperature for CFD, IR
Scanning and experimental rig.
Velocity (m/s) CFD (o
C) IR Scanning (o
C) Rig (o
C)
0.28 43 65 63
0.56 65 78 78
1.03 74 80 80
4D Dead Leg
Table 2: Results presented in the 4D Dead Leg Table show
variation between the CFD model and the other two methods,
experimental rig and I.R. scanning. The I.R. scanning and the
rig were closely matched for the various loop velocities for the
temperature readings at the bottom of the dead leg. However,
the CFD models reported a lower temperature at the dead leg
bottom indicating the CFD model needs further mesh
refinement to accurately capture the heat transfer in the 4D
dead legs.

8. 8
Table 3: 2D Dead leg bottom temperature for CFD, IR
Scanning and experimental rig.
Velocity (m/s) CFD (o
C) IR Scanning (o
C) Rig (o
C)
0.28 72 78 79
0.56 78 79 79
1.03 79 79 80
2D Dead Leg
Table 3: Results presented in the 2D Dead Leg Table were
similar for the three methods investigated. The CFD model for
0.28m/s displayed a 7% error delta. This suggests that a lower
residual convergence coupled with a mesh refinement may
allow a more accurate solution for thermal propagation at
lower temperatures for the CFD model.
Conclusion
The thermal properties of the dead leg configurations for the
6D, 4D and 2D for the velocities 0.28m/s, 0.56m/s and
1.03m/s were investigated. It was found the dead leg depth
and fluid flow velocity are highly significant in terms of
thermal penetration of the respective dead legs. The
experimental rig findings show that for the bottom of the 2D
dead leg the temperature reaches the main loop temperature of
80oC for the three loop velocities examined. The Fluent 2D
dead leg model displayed the nature of the heat transfer was
predominately convective. The thermography analysis
supported the temperature profiles of both Fluent and the
experimental rig. The 4D dead leg analysis conclusion is the
temperature at the bottom of the dead leg reaches the loop
temperature for both the 0.56m/s and 1.03m/s and the heat
transfer is both convective and diffusive. The 6D dead leg
analysis conclusion is the dead leg bottomalmost reaches loop
temperature at 78oC and the heat transfer is again both
convective and diffusive in nature for the loop velocity of
1.03m/s. The findings support that for non-invasive inspection
that I.R. scanning is a quick way to determine thermal
gradients as long as the emissivity correction factor is applied
correctly and the surface material is coated with a material to
mimic a black body. The experimental rig supports the
conclusion that a 6D dead leg is not optimal. This is due to the
necessity of maintaining a high flow rate to ensure thermal
penetration which ultimately drives power consumption for
system pumps. The Fluent application, with further research
into the mesh generation and wall treatments, would provide a
cost efficient method to design a tee junction that would aid in
the heat transfer into dead legs and inhibit the onset of biofilm
which ultimately will contaminate a system if the temperature
integrity and wall shear stress are not maintained to a
sufficient level for the entire system surface area.
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