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Contents lists available at ScienceDirect
Chemical Engineering Research and Design
journal homepage: www.elsevier.com/locate/cherd
Effects of air temperature and humidity on particle
deposition
Yunlong Han ∗ , Yongmei Hu, Fuping Qian
School of Civil Engineering and Architecture, Anhui University of Technology, No. 59, Hudong Road, Ma’anshan, Anhui 243002, PR China
a b s t r a c t
Particle deposition in a fully developed turbulent duct flow was studied. The random walk model of Lagrangian
approach was used to predict the trajectories of 3000 particles with a density of 900 kg/m3 . The effects of thermophoretic force and air humidity were also considered. The results were compared with the previous studies with
a particle size range of 0.01–50 ␮m and air flow velocity of 5 m/s. The profile of dimensionless deposition velocity
with relaxation time presents a V-shaped curve and the results are in good agreement with the previous studies.
The effects of air temperature and humidity on particle deposition with a particle size of 1 ␮m were also investigated. The results show that thermophoretic force accelerates particle deposition onto the duct walls with increasing
temperature difference between air flow and the duct wall surface. Meanwhile, it was found that particle deposition
velocity increases with air humidity.
© 2011 Published by Elsevier B.V. on behalf of The Institution of Chemical Engineers.
Keywords: Indoor air quality (IAQ); Particle deposition; Thermophoretic force; Lagrangian approach
1.
Introduction
Many concerns about indoor air quality (IAQ) have been
raised by those people who spend their most time in work
or life within a close indoor environment. Some problems
concerning IAQ, i.e., CO2 concentration, emissions of volatile
organic compounds (VOCs) and bacterium, are usually paid
more attention. However, aerosols or inhalable particulate
matters from ventilation ducts are sometimes ignored. If
low and medium grade commercial filters are utilized in a
mechanical ventilation system, the indoor particle concentration may be influenced by particle deposition in ducts when
outdoor air mixing with indoor exhaust air is supplied into
air-conditioning equipments and recirculated through a ventilation system. Respiratory diseases such as bronchia asthma
and respiratory inflammation may be caused when human are
exposed to particulate matters for a long time in a building.
Therefore, particle deposition in ducts has been studied by
some researchers (Sippola and Nazaroff, 2003; Breuer et al.,
2006; Winkler et al., 2006; Wu and Zhao, 2007). Their results
show that particle deposition rate is accelerated with an
increase in the air flow rate (Cheong, 1997), particle size (Zhao
and Wu, 2006) or wall roughness (Lai et al., 2000). For a horizon-
tal duct, gravity can enhance particles deposition on the floor
wall when air flow containing particles is introduced through
a ventilation duct. Inertia and turbulent diffusion can also
improve the deposition rate of particles. Whereas, Brownian
diffusion is usually shown only to be important for particles
smaller than 0.1 ␮m (Zhang and Ahmadi, 2000).
As we know, in a conditioning process heat and moisture
are absorbed by the supply air from ventilation ducts and then
removed from the conditioned space at the state of space air.
Heat or moisture is supplied to the space to compensate for
the transmission and infiltration losses through the building
envelope. Therefore, the temperature difference between supply air and the duct wall can lead to nano- or micro-particles
deposition by thermophoretic force. Some investigators (Bae
et al., 1995; Tsai and Liang, 2001) reported that the minimum
temperature difference between the wall surface and air flow
could keep a small deposition rate. Yang et al. (2008) investigated the influences of the parameters such as the ratio of the
bulk air temperature to the cold wall temperature, the air flow
rate on the kinematical characteristics and the deposition efficiency of PM2.5. They found that deposition efficiency of PM2.5
mainly depends on the temperature difference between the air
flow and the cold wall, while the air flow rate and the particle
∗
Corresponding author. Tel.: +86 555 2311862; fax: +86 555 2311862.
E-mail address: [email protected] (Y. Han).
Received 28 February 2010; Received in revised form 22 January 2011; Accepted 1 February 2011
0263-8762/$ – see front matter © 2011 Published by Elsevier B.V. on behalf of The Institution of Chemical Engineers.
doi:10.1016/j.cherd.2011.02.001
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Nomenclature
aij
Reynolds-stress anisotropy tensor, m2 s−2
C1 ,C2 ,C1 ,C2 ,C Reynolds stress transport model constants for pressure strain term
Cij
convection term, kg m−1 s−2
Cc
Cunningham coefficient
integral time constant
CL
CL1
empirical constant
time-averaged particle concentration, kg m−3
Cm
Cε1 ,Cε2 empirical constant
d
normal distance to the wall, m
particle diameter, m
dp
D
duct hydraulic diameter, m
Dij
turbulent diffusion of Reynolds stress,
kg m−1 s−2
DT
thermophoretic coefficient
f
Fanning friction factor
FD
drag force, N
additional forces, m s−2
Fx
Fxi
thermophoretic force in the temperature gradient direction (xi ), N
gx
gravitational acceleration, m s−2
h
mean roughness height of the rough wall, m
J
particle mass flux, kg m−2 s−1
k
turbulence kinetic energy, m2 s−2
Kn
Knudsen number
mp
particle mass, kg
nk
the component of the unit normal to the wall
Reynolds stress production, kg m−1 s−2
Pij
Rij
pressure–strain term, kg m−1 s−2
Rij,1
slow pressure–strain term, kg m−1 s−2
Rij,2
rapid pressure–strain term, kg m−1 s−2
Rij,w
wall reflection pressure strain term, kg m−1 s−2
Re
Reynolds number for the duct flow
turbulent Reynolds number for enhanced wall
Ret
treatment
t
time, s
T
local fluid temperature, K
TL
Lagrangian integral time, s
ug
gas velocity, m s−1
up
particle velocity, m s−1
*
u
friction velocity, m s−1
ui
fluctuation velocity of the fluid, m s−1
Um
mean air velocity, m s−1
ui uj
Reynolds stress tensor, m2 s−2
V+
dimensionless deposition velocity
xi
coordinate direction
Greek symbols
ıij
Kronecker delta
ε
turbulence dissipation rate, m2 s−3
εij
dissipation term, kg m−1 s−2
k
Von kármán constant (0.4187)
mean free path of gas molecules, m
molecular dynamic viscosity, Pa s
t
turbulent dynamic viscosity, Pa s
v
kinematic viscosity of air, m2 s−1
air density, kg m−3
p
particle density, kg m−3
k
Prandtle number of turbulent kinetic energy
+
dimensionless particle relaxation time
concentration almost affect hardly the deposition efficiency.
He and Ahmadi (1998) reported that Brownian motion is the
dominant mechanism of diffusion for particles only which are
very close to the wall (deep inside the viscous sublayer). Thermophoresis has a strong effect on deposition and transport of
particles smaller than a few micrometers (e.g. 5 ␮m). The deposition velocity is significantly affected by a large temperature
gradient field in turbulent duct flows. As the temperature gradient increases, the range of particle sizes that are affected by
the thermophoresis extends. Other investigators (Wirzberger
et al., 1997; Romay et al., 1998; Lin et al., 2004) also agreed
that the temperature gradient leading to thermophoresis phenomenon can accelerate particle deposition rate onto the duct
wall.
The effect of thermophoretic force on particle deposition by
the previous studies (Chang et al., 1995; Luo and Yu, 2006; Yang
et al., 2008; Dehbi, 2009) has been extensively investigated.
However, most works are not concerned about building ventilation ducts and further study is required. The purpose of this
paper was to investigate particle deposition in a fully developed turbulent flow of a ventilation duct with a cross-section
of 0.3 m × 0.2 m. The Reynolds stress transport model (RSM)
was used to simulate the turbulent duct flow. The random
walk model of Lagrangian approach was adopted to track the
trajectories of 3000 particles. The effects of thermophoretic
force and air humidity on particle deposition were also considered.
2.
Model
2.1.
Air flow equation
The transport of air and particles in a ventilation duct belongs
to a typical two-phase flow of gas–solid. Now, the methods
to describe the two-phase flow include commonly Eulerian
and Lagrangian approaches. The Lagrangian approach can
track the trajectory of individual particle by solving the particle equation of motion, but computational expense needs
to be considered. The concentration of particle in a ventilation duct is general low due to the filtration of filters. Air flow
and particles can be treated as the continuous phase and discrete phase, respectively. Generally, the continuous phase has
a significant influence on the discrete phase. However, the
effect of particles on the continuous phase can be neglected
due to the small particle size and low particle concentration.
Lagrangian method is typically appropriate for flows with low
volume fractions of the dispersed phase. Therefore, in this
study Lagrangian approach with one-way coupling method
was employed.
In this study, particle deposition in a fully developed turbulent flow was simulated using the commercial software
package (FLUENT 6.1). The Reynolds–averaged Navier–Stokes
(RANS) equations with the standard k–ε turbulence model
were employed widely to predict the incompressible turbulent
airflow (Zhang and Chen, 2006; Lai and Chen, 2006; Zhang and
Li, 2008). However, Tian and Ahmadi (2007) conducted some
simulations of nano- and micro-particles deposition in turbulent duct flows. Two RANS models, namely, the two-equation
k–ε model and the Reynolds stress transport model (RSM)
were adopted. They reported that FLUENTTM code can predict
reasonably the deposition rates of nano- and micro-particles
when the RSM turbulence model and the “two-layer zonal”
boundary condition were used. The simulation results showed
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that the deposition velocities followed a “V-shaped” curve for
the particles in the size range of 0.01–50 ␮m. Thus, in this study
RSM and the enhanced wall treatment were employed.
In the RSM approach, the Reynolds stress transport equation can be written in a short form as:
When the RSM is applied to near-wall flows using the
enhanced wall treatment, the pressure–strain model needs to
be modified. The modification specifies the values of C1 , C2 ,
C1 and C2 as functions of the Reynolds stress invariants as
follows:
Dui uj
C1 = 1 + 2.58A
Dt
= Pij + Dij + Rij − εij
(1)
The stress production term, Pij , is defined as:
Pij = −
∂u
j
ui uk
∂xk
∂u
+ uj uk i
∂xk
(2)
Dij represents diffusion transport of turbulent stress components, which can be simplified by the expression:
∂
Dij =
∂xk
t ∂ui uj
k ∂xk
(3)
k2
t = C
ε
(4)
2
(11)
√
C2 = 0.75 A
(12)
2
C1 = − C1 + 1.67
3
(13)
C2
(2/3)C2 − 1/6
= max
,0
C2
(14)
where the turbulent Reynolds number is defined as
Ret = (k2 /ε). The parameter A and tensor invariants, A2 and
A3 , are defined as:
where the value of k is 0.82. The turbulent viscosity, t is
computed as:
A2 1 − exp[−(0.0067Ret ) ]
A= 1−
9
(A2 − A3 )
8
(15)
A2 = aik aki
(16)
A3 = aik aki aji
(17)
where k is the turbulent kinetic energy given by:
aij is the Reynolds-stress anisotropy tensor, defined as:
1
k = ui ui
2
The pressure–strain term, Rij , in Eq. (1) is modeled by the following decomposition:
ε
k
ui uj −
2
ı k
3 ij
(7)
Rij,2 = −C2 (Pij − Cij ) −
+ C2
3
3
uk um nk nm ıij − ui uk nj nk − uj uk ni nk
2
2
(19)
where ε is the scalar rate of dissipation of turbulent kinetic
energy and is obtained from a modeled transport equation
given by:
+
t
ε
∂ε ∂xj
+
1
ε
ε
Cε1 Pii − Cε2 2
k
k
(20)
where ε = 1.0, Cε1 = 1.44, and Cε2 = 1.92.
(9)
The wall-reflection term redistributes normal stresses near
the wall and damps the normal stress perpendicular to the
wall, while enhancing the stresses parallel to the wall. The
wall-reflection term is modeled as:
Rij,w
2
εıij
3
(8)
∂
(uk ui uj )
∂xk
εij =
2
ı (P − C)
3 ij
where P = (1/2)Pkk and C = (1/2)Ckk .The convective transport
production, Cij is given by:
ε
= C1
k
(18)
k
∂
∂
∂
(εui ) =
(ε) +
∂t
∂xi
∂xj
The rapid pressure–strain term, Rij,2 , is modeled as:
(6)
where Rij,1 is the slow pressure–strain term, Rij,2 is the rapid
pressure–strain term, and Rij,w is the wall-reflection term.The
slow pressure–strain term, Rij,1 , is modeled as:
Rij,1 = −C1 aij = −
−ui uj + (2/3)kıij
The dissipation term εij is modeled as:
Rij = Rij,1 + Rij,2 + Rij,w
Cij =
(5)
k3/2
3
3
Rkm,2 nk nm ıij − Rik,2 nj nk − Rjk,2 ni nk
2
2
Cl εd
k3/2
Cl εd
2.2.
Particle motion
The trajectory of a discrete phase particle is predicted by integrating the force balance on the particle, which is written in a
Lagrangian reference frame. These forces include drag force,
gravity, and other forces. The force balance equation can be
written (for the x direction in Cartesian coordinates) as:
gx (p − )
dup
= FD (ug − up ) +
+ Fx
dt
p
(21)
where FD (ug − up ) is the drag force per unit particle mass and,
(10)
where C1 = 0.5, C2 = 0.3, nk is the component of the unit normal to the wall, d is the normal distance to the wall, and
3/4
Cl = C /, where C = 0.09 and = 0.4187.
FD =
18
P d2p Cc
(22)
The second term on the right-hand side of Eq. (21) is the gravitational force and the buoyancy. The magnitude and direction
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of the gravity vector need to be defined in this simulation. The
third term on the right-hand side of Eq. (21) is the additional
force term including Brownian force, Saffman’s lift force and
thermophoretic force. The Brownian force and Saffman’s lift
force used is the expression provided by Chen and Ahmadi
(1997).
Small particles dispersed in the air flow experience a force
in the direction opposite to that of the gradient when there
has a temperature gradient. This phenomenon is known as
thermophoresis. The thermophoretic force acting on particles
can be expressed as the following:
Fxi = −DT
1 ∂T
mp T ∂xi
(23)
where Fxi is the thermophoretic force in the temperature gradient direction, xi . DT is the thermophoretic coefficient proposed
by Talbot et al. (1980). mp is particle mass. T is the local fluid
temperature.
2.3.
The concept of the integral time scale is used to predict dispersion of particle and describe the time spent in turbulent
motion along the particle path (Tian and Ahmadi, 2007). The
integral time is proportional to the particle dispersion rate and
high values indicate intense turbulent motion in a flow. Moreover, the integral time is the local Lagrangian integral time, TL
for small particles moving with the fluid. The estimation of
Lagrangian time scale is approximate as the following:
k
ε
(24)
where CL is the integral time constant. k and ε are turbulence
kinetic energy and turbulence dissipation rate, respectively.
However, the constant value has not been determined yet.
Tian and Ahmadi (2007) reported that a value of CL in the
range of 0.2–0.96 gave satisfactory results comparing with the
experimental data. They considered Eq. (24) originated from
isotropic turbulent flow fields and the value of TL could be
estimated with the modified version of the equation using
the lateral turbulence mean square fluctuations for inhomogeneous and anisotropic flows as the following:
TL = CL1
ui u i
ε
(25)
where CL1 is the constant suggested value of 1. ui is fluctuation velocity of the fluid. In this study, the constant, i.e., CL1 = 1
was used to determine the turbulent Lagrangian time-scale
for simulation with the RSM model.
2.4.
Particle deposition velocity
Particle deposition velocity is commonly presented with an
expression of deposition flux divided by particle concentration. Furthermore, the dimensionless deposition velocity, V+
can be defined as the following for comparison with the previous studies and experimental data.
V+ =
J
Cm u∗
u ∗ = Um
(26)
where Cm is the time-averaged particle concentration in a
duct. J is particle mass flux. u* is the friction velocity as shown
f
2
(27)
where Um is the mean air velocity. f is Fanning friction factor.
The correlation obtained by White (1986) is used to estimate
the value of f as shown by the following:
1
= −3.6 log
f
6.9
+
Re
h 1.1 3.7D
(28)
where h is the mean roughness height of the rough wall and
it is zero for smooth walls in this study. Re is the Reynolds
number for the duct flow. D is the duct hydraulic diameter.
The dimensionless particle relaxation time, + , can be
defined as the following:
+ =
Lagrangian time scale
TL = CL
below:
Cc p d2p u∗2
(29)
18v
where and v are the molecular dynamic viscosity and kinematic viscosity of air, respectively. p and dp are particle
density and diameter, respectively, Cc is Cunningham coefficient which can be calculated as:
1.1 Cc = 1 + Kn 1.257 + 0.4 exp −
Kn
(30)
where Kn is the Knudsen number:
Kn =
2
dp
(31)
where is the mean free path of gas molecules, which is
0.065 ␮m at atmospheric pressure and the temperature of
25 ◦ C.
3.
Computational domain and boundary
conditions
A fully developed turbulent flow was simulated in a ventilation
duct with a rectangular cross-section of 0.3 m × 0.2 m and 3 m
in length as shown in Fig. 1. Hexahedral mesh elements with
a side of 1 mm and 5 mm in length were generated in the core
region of the duct. The generation of high resolution meshes
near the wall is significant in particle deposition. According
to the suggestion of Tian and Ahmadi (2007), the first grid
was generated at 0.05 mm away from the wall surface. Subsequent grids were grown up to 1.2 wall unit inward the core
region with a growing factor of 1.2 in the normal direction.
150,000 hexahedral mesh elements using GAMBIT mesh tool
were generated in the computational domain.
A stochastic method of random walk model was used to
predict the trajectories of 3000 discrete phase particles with
a density of 900 kg/m3 . A particle size range of 0.01–50 ␮m
was used in this study. Effects of temperature, gravity, Brownian force and thermophoretic force on particles deposition
were also considered. The inlet air velocity was 5 m/s and the
boundary condition of the duct exit was “outflow”. The duct
wall was assumed hydraulic smooth and “trap” was used as
wall boundary condition. Particles are regarded as deposition
on the wall when particles touch the wall neglecting bouncing
against the wall. Spherical particles are assumed in this study,
collision and coalescence between particles are ignored.
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Fig. 1 – Schematic diagram of ventilation duct and cross-section grids.
4.
Results and discussion
4.2.
4.1.
Flow fields
In order to compare with the previous studies, the dimensionless deposition velocity varying with the relaxation time
is presented as shown in Fig. 3. As we know, the dominant
deposition mechanisms, i.e., Brownian force and inertial force
acting on particles are different for ultra fine particles and
large particles. Thus three different regimes, i.e., diffusional
deposition regime, diffusion–impaction regime and inertiamoderated regime have been identified. Small particles, e.g.,
sub-micrometer or nano-particles are mainly affected by
Brownian diffusion, meanwhile inertial force can be neglected.
The inertia-moderated regime is only relevant for large particles ( + > 20), i.e., a particle size above dp > 30 ␮m for the fluid
flow (Horn and Schmid, 2008). As can be seen from Fig. 3 the
profile of the dimensionless deposition velocity presents a Vshaped curve. The Brownian diffusion region for ultra fine
particles lies on the left side of the V-shaped curve and the
inertial region lies on the right side. The dimensionless deposition velocities fall mostly in the range of three deposition
regimes and the results are in good agreement with the previous studies.
The above results about deposition velocity were obtained
on the basis of three-dimensional simulation with a ventilation duct cross-section of 0.3 m × 0.2 m. The Reynolds number
is 82,135 higher than that used by the previous researchers, i.e.,
the Reynolds number is 6667 in the study of Tian and Ahmadi
(2007). In this study, the high Reynolds number is caused by
an increase of the cross-section size other than the air flow
velocity which generally enhances particles deposition. The
large cross-section size resulting in a high Reynolds number
has not evident influence on particles deposition.
Generally speaking, particle deposition rate can be accelerated with an increase in the air flow rate which results in
a high Reynolds number. However, the effect of Reynolds
number resulting from the cross-section size of a ventilation duct has not been uncovered. In this study, flow fields
of two cross-section sizes were investigated to find the difference of flow fields. The inlet air velocity is 5 m/s in the
ventilation ducts with the cross-section sizes of 0.3 m × 0.2 m
and 0.075 m × 0.05 m. Reynolds numbers are 82,135 and 5136,
respectively. It was found that the flow velocity distribution
of the large cross-section is more uniform than that of the
small cross-section as shown in Fig. 2. The small velocity gradient of the large cross-section is observed evidently. Both the
velocity values of the core zone of the duct flow and velocity gradient near the wall for the small cross-section are large.
Therefore, there are some discrepancies of flow fields between
the large cross-section size (high Reynolds number) and the
small cross-section size (low Reynolds number).
Fig. 2 – Flow fields of two cross-section sizes: (a)
0.3 m × 0.2 m and (b) 0.075 m × 0.05 m.
Deposition velocity and Reynolds number
Fig. 3 – Comparisons of predictions of this study with the
previous studies.
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Fig. 4 – Effect of thermophoretic force on particle
deposition.
4.3.
Effect of temperature
The air temperature and humidity in a ventilation duct sometimes vary with the change of seasons for an actual center
air-conditioning system. When there is a temperature gradient between air flow and the duct wall surface, the effect of
thermophoretic force is not neglected for small particles. In
order to investigate the thermophoretic effect resulting from
temperature gradient between air flow and the duct wall on
particle deposition in a full-size ventilation duct flow, 3000
particles with a particle size of 1 ␮m were tracked in a 3 m
long duct. Fig. 4 shows the effect of the wall temperature
on particle deposition as the inlet air temperature is fixed at
24 ◦ C. A decreasing trend of particle deposition velocity with
increasing wall temperature (for floor, ceiling and vertical wall)
was observed as shown in Fig. 4. This also shows that thermophoretic force increasing with the temperature gradient
between air flow and wall surface enhances particle deposition. Meanwhile, it was found that the particle deposition
velocity onto the vertical wall is smaller than that onto floor
and ceiling walls.
However, it is not clear that whether particle deposition is
affect by the temperature of air flow or not if duct walls are
insulated. In this study an attempt was made to investigate
the effect of the air flow temperature on particle deposition
in a duct with insulated walls. It was found that the particle
deposition velocity onto the floor and vertical walls decreases
slightly as the inlet air temperature is increased, but for the
ceiling, particle deposition velocity increases as shown in
Fig. 5. A possible cause is a decrease in the Reynolds number. This reason is that the kinematical viscosity of air in the
duct increases with the air temperature. Few investigations
on Reynolds number in particles deposition were performed.
Romay et al. (1998) investigated particle deposition with particle sizes between 0.1 and 0.7 ␮m at Reynolds numbers between
4000 and 10,000. They found that the theory and experiments were in reasonable agreement for smaller particle sizes
(dp ≈ 0.1 ␮m) and lower Reynolds numbers (Re ≈ 5000). However, as particle sizes and Reynolds number were increased,
the experiments showed up to 2.0 times greater than the theoretical predictions. Therefore, a decreasing trend of particle
deposition can be attributed to a decrease in Reynolds number. This leads to a low probability of particles moving into the
viscosity sublayer by turbulent diffusion. Another cause may
be ascribed to the effect of buoyancy by the temperature dis-
Fig. 5 – Particle deposition on insulated duct walls.
crepancy between the floor and the ceiling wall. It was found
that the temperature at the top is slight higher than that at
the floor wall in despite of the insulated walls. Therefore, particles move partly toward the ceiling by buoyancy as the air
flow temperature increases. This leads to an increase in particle deposition onto the ceiling wall, and a decreasing tendency
of particle deposition onto the floor and vertical walls.
4.4.
Effect of air humidity
In general a humidifying/dehumidifying process for the supply air is always needed in order to maintain a level of air
humidity in a conditioned space. This process is performed
in an air handling system and the conditioned air is supplied
to a conditioned space by the ductwork. Particles suspended
in moist air depositing onto the ventilation duct walls have
been reported little. In this study, an investigation into particle
deposition with air relative humidity of 40–80% and the inlet
air temperature of 16 ◦ C was made as shown in Fig. 6. As can be
seen from the figure an increasing tendency of particle deposition onto the duct walls with air relative humidity was found.
This can be attributed to an increase in viscosity of moist air
as the air humidity rises. Therefore, the increasing viscosity
boundary layer leads to particles moving into this low velocity
zone and the deposition rate increases. However, the high air
humidity would lead to condensative water on the wall surface
if there is no good heat insulation of the duct wall in practice.
Fig. 6 – Effect of air relative humidity on particle deposition.
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This can increase the roughness of the duct wall. The roughened surface can result in a higher particle deposition velocity
than that in the case of the smooth surface. Furthermore, the
moist environment and suitable temperature in a ventilation
duct could lead to contaminants of biological origin such as
bacteria, viruses, and fungi. This can result in infectious diseases by airborne transmission. Therefore, indoor air quality
or indoor environment contamination by air humidity and
temperature in ventilation and air-conditioning system must
increase our concerns.
5.
Conclusions
The deposition of particle in a fully developed turbulent duct
flow was studied using random walk model of Lagrangian
approach. Effects of air temperature and humidity on particle
deposition with a particle size of 1 ␮m and the inlet air velocity of 5 m/s were investigated on the basis of comparison with
the previous studies. Some results were obtained as follows:
(1) The flow velocity distribution of the large cross-section is
more uniform than that of the small cross-section. However, the cross-section size resulting in a high Reynolds
number has not evident influence on particle deposition.
(2) In this study thermophoretic force can enhance particle
deposition velocity with a particle size of 1 ␮m. It was
found that particle deposition velocity increases with the
temperature difference between air flow and the duct wall
surface. Deposition velocity of particle onto the vertical
wall is smaller than that onto the ceiling and the floor
walls.
(3) Particle deposition onto the ceiling wall is higher than
the floor and vertical walls as the air flow temperature is
increased in an insulated duct.
(4) Particle deposition velocity increases with air relative
humidity. It can be attributed to an increase in viscosity
of moist air. Meanwhile, the rising roughness of duct wall
resulting from condensative water on the wall surface in
practice may enhance deposition velocity of particle onto
the duct walls.
Particles deposition in ventilation duct has an important
influence on human exposure to air borne particles. More
experiments and numerical calculative work in indoor particulate matters are needed. For this purpose, effects of supply
air on indoor air quality and particles distribution in airconditioning rooms are the next research work.
Acknowledgments
The authors would like to thank school of energy and environment of Southeast University for computation support based
on FLUENT 6.1.
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Please cite this article in press as: Han, Y., et al., Effects of air temperature and humidity on particle deposition. Chem Eng Res Des (2011),
doi:10.1016/j.cherd.2011.02.001