Research on the Local Wind Pressure Coefficient for Natural Ventilation
Yingying Feng1*, Xiaofeng Li2, Jie Ma2
1
2
*
Beijing Tsinghua Urban Planning &Design Institute, Beijing 100083, China
Tsinghua university, Beijing 100084, China
Corresponding email: [email protected]
SUMMARY
The natural ventilation potential of buildings is greatly affected by the wind pressure
distribution on the out wall surfaces. For a fixed building shape and surroundings, estimating
the wind pressure coefficient（Cp）on building surfaces is an efficient way to get the wind
pressure under different inflow wind velocities. And this paper aims to develop a Cp database
which applies to different building shapes and arrangements. In this paper, several kinds of
representative building shapes and arrangements in China are chosen and modeled in CFD
models. By analyzing the results, some regular patterns of Cp distribution are concluded. In
the latter part of this paper, a rapid calculation method for getting Cp is discussed, and the
basic framework of the database is being developed due to this new method.
KEYWORDS
Natural ventilation, Building planning and construction, Residential
1 INTRODUCTION
Natural ventilation is good for people’s comfort and health, and is well accepted as an
energy-saving way (BuschJ F,1992). A bad design leads to a higher cooling/heating load in
summer/winter because of a higher infiltration while a good design will save energy. The
energy-saving potential of natural ventilation is greatly affected by the weather condition and
local building arrangement, which result in different wind pressure distribution.
To analyze the annual energy consumption, the hourly wind pressure distribution on building
surfaces is essential. Usually CFD method is adopted to get the hourly wind pressure
distribution. However, it’s impossible to run CFD simulation every hour, so it is important to
build up a wind pressure coefficient database before the energy consumption simulation. A
lot of research on Cp simulation has been done in the previous literatures(Mario Grosso,1992;
Yunqiu Jia and B.L. Sill,1998; B.E. Lee ,B.F. Soliman,1977; J.E. Cermak,1975; P.J. Richards,
R.P. Hoxey, L.J. Short,2001), however, most attention was put on one fixed building. In
present paper, several kinds of representative building shapes and arrangements are studied.
The Cp distribution and the tendency on each surface are analyzed. A regular law of Cp
distribution is developed to build a Cp database.
2 METHODOLOGY
The building surface wind pressure distribution relies greatly on the appearance of the
building. Due to literature (Lianchang Zhu, 1999), there are a lot of kinds of residential
buildings in China, and they have different characteristics and appearance to adapt to
different natural environments and landscapes. For the mostly seen residential buildings in
China, three conclusions could be drawn:
(a) For the height of buildings, the Multi-storey and High-rise buildings account for a large
proportion.
(b) For appearance, slab-type apartment buildings and tower-type high-rise buildings takes a
large proportion. And in addition, cross and trapezoid shape are also very common.
(c) Slab-type shape apartment building is the most common residential building type in
China. Therefore, slab-type apartment building which occupies the highest percentage is
studied. Various rows and columns are studied to see if there exists any common tendency of
wind pressure distribution.
Several typical building arrangements are simulated to get the surface wind pressure
coefficient. The distance between buildings is decided by the Beijing local standard “Code of
urban residential areas – planning & design”. Figure 1 shows the appearance and dimensions
of the building and Fig.2 shows several simulation schemes and case names for each case.
Table.1 concludes the dimension parameters of each case, different building sizes and
distance are analysed.
Figure1. The appearance of the building
Figure2. Simulation schemes and case names
Table.1 Dimension parameters of each case
Case type
A
W/m
A/m
H/m
Distance between rows /m
Distance between columns /m
80
12.6
17
28.9
10
B
C
80
12.6
51
86.7
10
100
12.6
17
28.9
10
The case name is composed by a letter and two numbers. The letter indicates the different
building sizes and arrangements; the first number indicates the total number of rows, and the
second number represents the total number of columns. For example, CaseA63 means that the
dimension type is type_A in Table.1, and there are 6 rows and 3 columns of buildings in total.
Cp distribution is simulated for each case and the results are analysed. The simulation is
performed by the commercial CFD software PHOENICS(Version 3.5). A MMK turbulence
model is chosen for the turbulence equation, and the QUICK differential scheme is adopted.
3 RESULT
In total 90 cases are simulated. The simulation results of CASEA, CASEB and CASEC show
a same tendency. Therefore, in present paper, only some typical results of CASEA are shown.
Single column – CASEA_1 serials
Fig.3-Fig.5 shows the distribution of the wind pressure coefficients on the windward side. For
one specific building at the same row of each case, we can see that the windward side retains
a similar wind pressure coefficient distribution, the difference is small. And from the 3rd to
the last row, the wind pressure coefficient on windward side doesn’t change much. So we can
approximately conclude that for single column building arrangements, the wind pressure
coefficient on the windward side is only related to how many buildings are in front, and keeps
same from the 3rd to the last row.
The wind pressure coefficient distribution is quite uniform on the leeward side of the
building, so an average value would be enough. The conclusion is:
(a) For the first row, the leeward side wind pressure coefficient is relative to the total number
of rows, and the value is about -0.6 for single row, -0.5 for two rows, -0.4 for three or more
rows.
(b) For the last row, the leeward side wind pressure coefficient is always about -0.4.
(c) For every building in the middle, the leeward side wind pressure coefficient keeps about
-0.2.
a)
b) CASE A11
c) CASE A21
Figure3. Cp distribution on the windward side, the 1st row of CASE_2 serials
a)
b) CASE A21
c) CASE A31
Figure4. Cp distribution on the windward side, the 2nd rows of CASE_2 serials
a)
b) CASE A31
c) CASE A41 3rd row
d) CASE A41 4th row
Figure5. Cp distribution on the windward side, last few rows of CASE_2 serials
Double column – CASEA_2 serials
The simulation results show that the trend for the windward side wind pressure coefficient
still holds. We can also conclude that the wind pressure coefficient on the windward side is
only related to how many buildings are in front, no matter how many buildings are in its
leeward side. And from the 3rd to the penultimate row, the windward side wind pressure
coefficient keeps the same. Only for the last row, it’s different from the front rows, but no
matter how many rows in total, the windward side distribution of the last row keeps the same
（Fig.6-Fig.7）.
a)
b) CASE A42
c) CASE A62 3rd row
d) CASE A62 4th row
Figure6. Cp distribution on the windward side, middle rows of CASE_2 serials
a)
b) CASE A32
c) CASE A42
Figure7. Cp distribution on the windward side, last row of CASE_2 serials
Triple column – CASEA_3 serials
The simulation results show similar trends of single column and double column cases. For the
middle column, the trends of single-column still hold with only a slight change on values. For
the top and bottom columns, the trends of double-column also hold.
Other factors
There are a lot of other factors that will affect the wind pressure distribution, e.g. the aspect
ratio of the building, the distance between rows and columns. To clarify the effect of these
factors, more than one hundred cases are simulated and studied. And the simulation results
show a same trend as above mentioned. And the topology of the wind pressure coefficient
contour keeps constant when the building varies in aspect ratio, the exact wind pressure
coefficient could be calculated by interpolation.
4 DISCUSSION
From the conclusion of above discussed cases, we can simplify the calculation of this kind of
building arrays, which is most common in Chinese residential building and find a way to
construct the wind pressure coefficient database. For a determined building size, the distance
between rows and columns are also determined by the local law, which makes it possible to
construct a database for this kind of building. The calculation could be simplified into two
basic cases (fig.8) to build up the database.
(a) For single column buildings, the wind pressure coefficient distribution could be extended
from the results of basic case_1 by applying the conclusion of CASEA serials.
(b) For double column buildings, the wind pressure could be extended from the top & bottom
column of basic case_2.
(c) For multi column buildings, the wind pressure coefficient of the top & bottom column
could be extended from the top & bottom column of basic case_2 and all the columns in the
middle could apply the middle column of basic case_2.
By applying this basic case method, a lot of CPU time could be saved.
Figure8. Two basic cases
5 Conclusion
The wind pressure coefficient distribution of several kinds of representative building shapes
in China is studied. The Cp distribution and the tendency on each surface are analyzed. A
regular law of Cp distribution is developed and a set of rapid calculation method for
constructing wind pressure coefficient database is concluded. For the most common slab-type
buildings, the Cp distribution can be simply calculated by simulating only two cases. In
further work, different incident wind directions will be studied.
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