A CFD MODEL BASED RESEARCH ON WET DEPOSITION OF
LARGE SCALE NATURAL DRAFT COOLING TOWER
Wang Xuan1,2 Bao Wenjie1
Huang Xiaodong1 Wang Xue1
Shanghai Nuclear Engineering Research & Design Institute1, Shanghai JiaoTong University2
No.29 Hong Cao Road, Shanghai, 200233, China
ABSTRACT
Traditional research on environmental impact of Natural Draft Cooling Tower in Nuclear Power
Plant is based on Gaussian diffusion modeling or wind tunnel experiment. As modern mainframe
computers and turbulence model develops, it is becoming possible to use CFD model to simulate plume
drift. CFD model could simulate and display the plume drift more accurately, relying on its powerful
computing ability. In this paper, the validity of CFD simulation is verified using 1977 Chalk Point Dye
Tracer Experiment data, and case studies of two inland nuclear power plants in China are provided.
The research shows that CFD simulation fit well with the data from Chalk Point experiment. Compared
to SACTI model, wet deposition results of CFD model in nearby region are larger. As the down-wind
distance increases, SACTI results decrease faster than CFD results. Based on this research, we can
conclude that when the field data is not available, CFD model could be a powerful tool for
environment impact assessment.
KEYWORDS: Computational Fluid Dynamics, Cooling Tower, Wet Deposition, Nuclear Power Plant1
1
Project: Severe accident analysis and emergency decision support technology research grants(2013ZX06004-008)
About the author: Wang Xuan, Male, born in 1983, Dr.job in Shanghai Jiaotong university, focus on nuclear emergency research,
E-mail：[email protected]
1. Background
With the rapid development of Chinese economy and its nuclear power industry, construction of
nuclear power plant (NPP) is becoming a very important planning target. Due to various limitations,
inland NPP sites often turn out to be located in mountainous area, using large scale natural drift cooling
towers as the circulation cooling system. Wet drifts from cooling towers will cause shadowing, salt
deposition, fog and ice phenomena, which could impact the regional environment, transportation safety
and residential environment. How to give quantitative analysis of these effects is become a important
issue. In China, National Nuclear Safety Administration (NNSA) issued <Technical guidelines for
environmental impact assessment format and content of environmental impact reports for nuclear
power plants> (HJ808-2016). According to the technical guideline, the impact of cooling tower to the
environment must be included in the EIA report.
Due to the rough terrain around inland NPP, wind field near the ground is irregular, and the
pattern of plume drifts from cooling towers shows great uncertainty[1-2]. Plume diffusion is mainly
decided by wind direction, wind speed, turbulence, temperature, air stability and terrain conditions. At
present, simple dispersion models are used to calculate plume concentration [3-6] (e.g. Gaussian model),
as well as the wind tunnel experiments [7-11], SF6 field experiments. While in mountainous area, airflow
in the horizontal and vertical directions are not regular, flow filed distribution is also uneven. When the
concentration does not fully comply with the normal distribution, the traditional Gaussian model will
be unable to solve this kind of problem. With the development of computing technology, the research
on diffusion numerical solution is increasing [12-14]. Wind tunnel experiments could provide a better
simulation of wind filed and concentration filed, but the cost of money and time is considerable. For
wind tunnel experiment, simulation of wet plume deposition is not available. Therefore, it is now
reasonable to use the CFD (Computational Fluid Dynamics) model to solve this problem.
In 1970s, foreign scholars proposed the use of CFD software to simulate the cooling tower drift
diffusion. England [15] used CFD to calculate cooling tower plume of Keystone power plant in the west
of Pennsylvania. Bergstorm[16] used two-dimensional CFD to simulate ideal drift of cooling tower at
different wind speed and in different wind directions. Later Derksen and Bender[17] compared the CFD
result with wind tunnel experiments, and the conclusion showed that CFD can provide a better result of
cooling tower drift diffusion.
2. CFD Software introduction
Popular commercial CFD software includes CFD2000, CFX-4,CFX-5,CFD Taskflow, Flow-3D,
Fluent, PHOENICS，STAR-CD、STAR-CCM+, etc.
STAR-CCM+, which is developed by CD-adapco Company, is used for modeling in this paper. It
uses computational continuum mechanics algorithms, with modern software engineering technology,
thus the performance and reliability is enhanced. STAR-CCM+ has a powerful mesh generator, which
could generate Hexa, Tetra as well as Poly mesh grid. Compared with Tetra mesh, using Poly mesh can
provide a better result in a much shorter time. At present, STAR-CCM+ has been widely used in
various industries such as aerospace industry, shipping business, automobile manufacturing and
environmental assessment.
In this paper, standard k-ε turbulence model is used, for it converges faster and has a relatively
smaller amount of calculation. ε is fluid pulsation kinetic energy dissipation rate.
1) Mass conservation equation
u v w


0
x y z
2
2) Momentum conservation equation
du u
u
u
u
p

u
 v  w  fx 
dt t
x
y
z
x
dv v
v
v
v
p

 u  v  w  fy 
dt t
x
y
z
y
dw w
w
w
w
p

u
v
w
 fa 
dt
t
x
y
z
z
3) Standard k-ε equation
k   kui  


t
xi
x j
  ui  


t
xi
x j

ui
 u 
  k

ui
 u 


 k 

  Gk  Gb    YM
 x j 
  

2

  C1 Gk  C3 Gb   C2 
k
k
 x j 
Wherein:
Gk—Turbulent kinetic energy generated by layer flow velocity;
Gb—Turbulent kinetic energy caused by buoyant effect;
YM—In compressible turbulence, fluctuation of excessive proliferation;
C1ε, C2ε, C3ε—Constant;
σk, σε—Prandtl number.
3. Model Validation
Since there is no cooling tower wet drift experiment in China, thus Chalk Point power plant
experiment data is used to verify the CFD simulation results in this paper.
3.1 Chalk Point Power Plant
The gross generator power of Check Point plant is 2640MW. There are two natural drift cooling
towers lay out in line with a distance of 152m between each other. Each cooling tower has a height of
124m, a base diameter of 114m, and an outlet diameter of 54.8m. Chalk Point power plant is shown in
Fig. 3-1.
3
Fig. 3-1 Chalk Point power plant
3.2 Experiments
Although a number of sodium deposition experiments were performed, affected by simultaneous
releases from other nearby stacks, most of the results were not ideal enough. Among all experiments,
the one performed in the evening of 16 June 1977 was most successful. In this experiment, 30 gallons
of 20% Rhodamine WT (fluorescent dye) were added to the cooling tower basin water, and no
additional water was added to or drained from the basin. Consequently the only loss of dye was drift
loss so that the concentration of dye in the water remained constant in the duration of the experiment.
Plant operating load also remained constant during the experiment. Source measurements reported that
drift loss = 0.002%, plume temperature = 315.3K, ambient temperature =295.3 K, and exhaust velocity
= 4.5m/s. Rhodamine WT (fluorescent dye) tagged sodium source strength equaled to 1.86 g/sec.
Measurements were made at night in 93% humidity, so that the droplet evaporation was negligible.
Predominant wind direction was south, therefore building and tower wakes in the near field did not
intersect. The wind speed profile could be described in two layers: above 100m, the wind speed was
nearly constant, about 8m/s; below 100m, the wind speed was nearly linear with height with a mean
value of about 5m/s.
Instruments to measure drift deposition were placed with intervals of 5° along 35° arcs at the
distance range from 0.5 to 1.0 km north of the cooling towers. The average deposition of the dye
tagged sodium droplets on the 0.5 and 1.0 km arcs was 1080 and 360 kg/km2·month, respectively. Drift
droplet sizes at the measurement stations had a mass median diameter of 340 and 260μm on the 0.5 and
1.0 km arcs, respectively. Most of the drop sizes were range from 250 to 450μm on the 0.5 km arc and
200 to 400μm on the 1.0 km arc. In addition, plume centerline heights were observed at the downwind
distance ranging from 50 to 200m [18].
4
Fig. 3-2 Chalk Point Experiment sampling points distribution [18]
3.3 3D models
In STAR-CCM+, basic parameters of the model are constructed as follows:
Domain length is 2000m, width is 1000m, height is 500m, cooling tower height is 124m, base
radius is 40m, and outset radius is 27.4m. Cooling tower locates in the downwind centerline at a
distance of 500m, as shown in Fig. 3-3.
Fig. 3-3 Chalk Point 3D model
Polyhedral mesh is used. The size of cooling tower volume mesh grid is set to 4m, while for the
other part, the size is set to 50~100m, as shown in Fig. 3-4. The total number of grids is up to 600,000.
5
Fig. 3-4 Chalk Point 3D grid model
3.4 Boundary conditions
Boundary conditions in STAR-CCM+ is set as in Fig. 3-5.
Symmetry
plane
Velocity
Inlet
Pressure
Outlet
Velocity
Inlet
Wall
Fig. 3-5 Boundary conditions
Inlet temperature is set to 295.3K. Below the height of 100m, inlet velocity follows exponential
distribution described by the formula below; above the height of 100m, velocity speed is set to the
constant of 8m/s.
v  0.3523  z 0.6781
6
Fig. 3-6 Inlet wind profile
Surface roughness is set to 0.5m (Z0=0.02m) .
Both sides of the domain are set as Wall, while the top is set as Symmetry Plane.
Standard K-ε model and Lagrangian multiphase flow model is used.
Cooling tower drift outlet velocity is set to 4.5m/s, turbulence intensity is set to 10%, turbulence
length is set to 25m, outlet temperature is set to 315.3K.
As the humidity is very high during the experiment period, therefore, evaporation of the droplets
is negligible in the calculation.
In the Lagrangian multiphase flow model, the particle material is set as water droplets.
Particle mass velocity is set to 0.328kg/s.
Plume drift initial velocity is set to 4.5m/s.
Particle diameter distribution follows Rosin-Rammle distribution, as shown in Fig. 3-7.
Fig. 3-7 Chalk Point Particle diameter distribution [19]
3.5 Analysis
1) Cooling tower plume lift height validation
7
In this paper, CFD simulation result of plume lift height in different distance downwind is
compared with Chalk Point experiment data and Briggs formula calculation result by Hanna, as shown
in Fig. 3-8.
Fig. 3-8 Cooling tower plume lift height comparison
From the above figure, it is clear shown that CFD simulation results fit very well with Chalk
Point experiment data within 50-200m. Compared with Briggs plume lift formula (H=1.6F1/3x2/3/U,
F=2100m4/s3, U=8m/s), Briggs' result fits better in 200m. For the rest distances, Briggs' results are
generally higher than CFD and Chalk Point experiment data.
Cooling tower plume diffusion locus is shown in Fig. 3-9:
Fig. 3-9 Cooling tower plume diffusion locus
2) Wet deposition validation
In Chalk Point experiment, wet deposition was monitored at downwind distance of 0.5km and
8
1.0km. Comparison of CFD result and Chalk Point experiment data is shown in Fig. 3-10.
Fig. 3-10 Wet deposition data comparison
In Chalk Point experiment, data at only two distances was successfully monitored as in Fig. 3-10.
CFD result is about two times larger than Chalk Point experiment at 500m, while in the far region, the
results become relatively closer. This is mainly because of the difference in particle size distribution
which could affect wet deposition distribution. And from the CFD result curve, we could find that the
maximum value appears at 620m, where the wet deposition amount is 6.9E-07kg/m2.s.
The wet deposition distribution of CFD simulation is shown in Fig. 3-11.
Fig. 3-11 Cooling tower wet deposition distribution in CFD
4. Case study
Two inland sites in China are selected for case study, which are T nuclear power plant and P
nuclear power plant.
9
4.1 3D models of two sites
3D models are built up by Solidworks, based on site layout, with terrain data integrated. 3D
models of two sites are shown in Fig.4-1 and Fig.4-2.
Fig.4-1 3D model of T nuclear power plant
Fig.4-2 3D model of P nuclear power plant
Horizontal domain size is set to 20km×20km. In the vertical direction, 4000m height is divided
into 50 layers. For each layer, increase rate is set to 1.2, and boundary layer height is set to 2m. Nest
grid is used with grid size from 3m to 200m, and building surface size is set to 4m. The total grids
number ranges from 2,500,000 to 3,000,000. The 3D grid model is shown in Fig.4-3 and Fig.4-4.
10
#1
#2
#3
#4
Fig.4-3 3D grid model of T nuclear power plant
2#
#1
3#
4#
Fig.4-4 3D grid model of P nuclear power plant
4.2 Boundary conditions
Inlet wind profile is exponential distribution.
u  u 70
 Z 
 

 70.0 
p
u is wind velocity, m/s; u70 is wind velocity at the height of 70m, for T nuclear power plant, u70 is
2.3m/s, for P nuclear power plant, u70 is 5.2m/s; Z is hegiht, m; p is exponent number, in D stability( T
site: 0.25, P site: 0.179).
Surface roughness is set to 0.3m.
Turbulence intensity: T site (below 400m, turbulence intensity is 0.2, above 400m, turbulence
intensity is 0.1), P site (Below 100m, turbulence intensity is 0.1, above 100m, turbulence intensity is
0.05).
Cooling tower drift velocity is 4.35m/s.
Cooling tower drift temperature is 30.33℃.
Air temperature: T site is 16.9℃, P site is 16.5℃.
Mass rate is 0.455kg/s.
Particle diameter distribution follows Rosin-Rammler distribution.
11
Mass fraction is 5.552%.
4.3 Cooling tower design parameters
1) T nuclear power plant
T nuclear power plant cooling tower design parameters are shown as below.
Tab. 4-1 T cooling tower design parameters
Parameters
Value
Base diameter
158.8m
Height
200m
Outlet diameter
95.74m
Throat diameter
92.1m
Two towers distance
110m
Eliminator efficiency
95%
2) P nuclear power plant
P nuclear power plant cooling tower design parameters are shown as below.
Tab. 4-2 P cooling tower design parameters
Parameters
Value
Base diameter
168.66m
Height
215m
Outlet diameter
102.70m
Throat diameter
99.0m
Two towers distance
110m
Eliminator efficiency
95%
4.4 Calculation results
1) T nuclear power plant
Fig.4-5 shows the centerline wet deposition result of CFD and SACTI model. Generally, CFD
results are two times larger than that of SACTI model. This is mainly because in this paper, SACTI
uses hourly meteorological data, while CFD uses fixed wind direction and wind speed. Maximum CFD
wet deposition value is 4.91E-06kg/m2.s, appears at downwind distance 159m of NO.1 cooling tower.
Maximum SACTI wet deposition value is 1.23E-06 kg/m2.s, appears at downwind distance 200m of
NO.1 cooling tower. CFD result is slightly larger than that of SACTI.
12
Fig.4-5 T nuclear power plant downwind centerline wet deposition distribution
Fig. 4-6 shows the cooling tower diffusion locus. Droplets of large particle size fall rapidly to the
ground in the near region, and that of small size particle can drift in a longer distance.
Fig. 4-6 T nuclear power plant cooling tower diffusion locus
2) P nuclear power plant
Fig.4-7 shows the centerline wet deposition result of CFD and SACTI model. In the near region,
CFD results are close to SACTI, while in the region beyond 2km, SACTI results decrease faster.
Within 1km, CFD results are slightly higher than that of SACTI. This is mainly caused by the impact of
cooling tower structure considered in CFD.
Maximum CFD wet deposition value is up to 2.48E-08kg/m2.s, appears at downwind distance
300m of NO.1 cooling tower. Maximum SACTI wet deposition value is 9.65E-09kg/m2.s, appears at
downwind distance 900m of NO.1 cooling tower. CFD simulation results are larger than SACTI in the
near region because of the cooling tower down wash effects.
13
Fig.4-7 P nuclear power plant downwind centerline wet deposition distribution
Fig.4-8 shows the cooling tower diffusion locus of P nuclear power plant. It is clear that in the
near region, due to the blocking effect of cooling towers, part of particle droplets fall rapidly to the
ground.
Fig.4-8 P nuclear power plant cooling tower diffusion locus
5. Conclusion
1) Chalk Point experiment data is used to validate simulation of CFD model. Calculations show
that CFD can simulate plume lifting process better, CFD result of wet deposition is two times larger
than Chalk Point experiment at 500m, while in distant region, results of two models become relatively
closer. This is mainly because of the difference in particle size distribution as it can effect wet
deposition distribution. Generally speaking, CFD could well simulate the process of cooling tower
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plum dispersion and deposition. When field data is not available, CFD could be a powerful tool in
environment impact assessment.
2) P and T nuclear power plants are selected for case study. Calculations show that CFD results
of wet deposition fit well with SACTI in the near region. Compared with SACTI, the calculations of
CFD model provide a maximum wet deposition value at a shorter distance, due to the blocking effect of
cooling towers. In the distant region, SACTI results decrease faster.
6. Outlook
1) Although CFD model can provide a better simulation of cooling tower plume dispersion and
deposition, but due to the limitation of calculation ability, CFD model couldn’t use hourly
meteorological data of a entire year as input. Therefore, in further studies, it is necessary to work on
boundary conditions optimization and reasonable meteorological classification, in order that CFD
model could run under multiple meteorological conditions.
2) This study could also be extended to the calculation of salt deposition, which is of great
significance in the environment impact assessment of seawater cooling tower.
3) Plume shadowing of cooling tower drift is decided by season, sun angel and location, which
are not included in CFD model. These could be important research directions in the future.
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