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Ventilation effectiveness of building cluster.

INTRODUCTION

The environment has an innate ability to cope with pollutants itself. With natural airflows, the nature could remove the dirt and refresh the area with clean air. Thus, it was able to clean up pollutants in the early stage of urbanization when only small buildings, short streets and pollutants covered the land. Unfortunately, over the past decades, urbanization in many countries has been intensified.

Urbanization is the physical growth of urban areas. It is the outcome of social, economic and political developments that lead to urban concentration and growth of large cities, changes in land use and transformation from rural to metropolitan pattern of organization and government. With reference to the definition from the United Nations, urbanization is the movement of people from rural to urban areas with population growth equating to urban migration.

According to the UN State of the World Urbanization Prospects 2009 report (UN, 2010), in the middle of 2009, the majority of people worldwide will be living in towns or cities, for the first time in history; this may referred to as the arrival of the "Urban Millennium". Refer to Fig 1. The number of people living in urban areas (3.42 billion) had surpassed the number living in rural areas (3.41 billion) (UN, 2010).

[FIGURE 1 OMITTED]

Such a sustained increase of urban population combined with the deceleration of rural population growth will result in continuous urbanization and increasing proportions of the population living in urban areas. The level of urbanization is expected to rise from 50% in 2009 to 69% in 2050 (UN 2010).

This has resulted in a dramatic increase in the number of high-rise residential and office buildings, which forms a densely populated, high-rise urban environment. Consequently, the atmospheric ability to disperse pollutants has been deeply undermined. Pollutants cannot be blown away from the city and the natural ventilation mechanism is broken down. At the same time, continuous growth in population and human activities has resulted in more outdoor pollutants like vehicle emissions. The city becomes a black box being pressurized by pollutants.

Many people concern the indoor air quality in their work and home environment as they spend 88% of their time indoors (Robinson et al., 1995). Some researches found a considerable connection between indoor air quality and human health (Jones, 1999). There have been some other studies showing that indoor air quality has an effect on productivity. Consultant engineers were appointed by building owners to develop effective ventilation strategies for the improvement of indoor air quality.

Rubino et al. (1998) pointed out that "the final quality of air inside the buildings is determined by the effect of three main occurring phenomena: (i) pollution of the outdoor air in the neighborhood of the building with respect to both continuous and intermittent sources, (such as vehicular traffic) (ii) pollution generated by indoor activities and finally (iii) by the smoothing effect which the mechanical ventilation system exerts on both". Jones (1999) also demonstrated that the outdoor pollutant sources could be one of the main contributors to the concentration of indoor contaminants. Traffic is one of the major sources of ultra-fine particles in the urban environment (Hitchins et al., 2000) and other outdoor contaminants like CO, NOx and SO2 also affect the work and living environment.

Accordingly, effective ventilation strategies are vital for minimizing the intake of outdoor pollutants into the buildings. Prior to the formation of any guidelines relating to outdoor air intake, it is necessary to understand the characteristics of outdoor air quality as well as the microenvironment surrounding the buildings and the building cluster.

While urbanization is an integral part of a city's development, it is almost impossible to stop constructing high-rise buildings in most cities, especially in the developing countries. Nevertheless, the study of the airflow pattern, air change rate, local velocity of the building cluster environment and also the microclimatic environment of a city, are essential for the healthy planning of a city's development.

METHODOLOGIES

Critical review on the ventilation of building cluster was carried out. Numerical study of a classical case was gone through and the results were verified with experimental results from existing studies in literature.

Then, the numerical study of a standard building cluster with particular wind-driven force and buoyancy-driven force was carried out and the corresponding ventilation rate was studied. Further numerical study of building clusters with various aspect ratios, wind-driven force and buoyancy-driven force were carried out. The air change rates of each case were analyzed. Pedestrian comfort in each case was also investigated.

The flows around a single building were investigated prior to the study of the ventilation of a building cluster. The flows around a simple building were examined by using numerical simulation of a simple cube. The study provides invaluable insights for the future studying of buildings clusters using numerical simulation and turbulence models.

Building configuration

Fig 2 illustrates the building configurations used for the study. To simplify the parameters throughout the simulation, it is assumed that the ratio between the building's length and width is 1, and the ratio between the building's length and height is 0.2. And all nine buildings in the building cluster carry the same configuration. It is also assumed that the ratio of the street's length (parallel to the prevailing wind direction) to width (perpendicular to the prevailing wind direction) is 1, where a = b. Besides, the ratio of the building height to the width of street is defined as "aspect ratio, m".

[FIGURE 2 OMITTED]

In this CFD simulations, the computational domain was chosen as 20 times of building height (20H) at the upstream of the building cluster and 50 times (50H) of building height at the downstream of the building cluster. Regarding the width of the domain, the distance between the edge of the domain on each side and the outer edges of the outer building was chosen as 20 times of the building height (20H). And the height of the domain was chosen as 20 times of the building height (20H) from the roof of the building.

Before the study, mesh sensitivity tests with various grid sizes were performed. Each test was setup with the same basic setup and parameters but different cell size. Finally, the minimum grid resolution in the study was kept at approximately 0.1 m near to the building wall, the building roof and the ground and the expansion ratio between two consecutive cells was kept below 1.3.

The wind profile in the study exhibited a constant magnitude at the inlet boundary and the plot of the actual approaching wind profile to the building cluster was shown in Fig 3c, which was comparable to the power law with rural exponent. The incoming wind speed shown in the result and discussion session represents the wind speed at a height of z [much greater than] H.

[FIGURE 3 OMITTED]

MATHEMATICAL MODELING

RNG-turbulence model was employed for this study. Prior to the decision, a simple cubic building was used to perform the comparison of computational results by using different turbulence models (k-[epsilon] model and RNG k-[epsilon] model) and the experimental results reported in Castro et al. (1977). The wind pressure coefficients on the cubic building windward wall, leeward wall, sidewall as well as top surface were examined. The RNG k-[epsilon] model showed reasonable good agreement between the computed and the measured data.

[FIGURE 4 OMITTED]

Governing Equations

The CFD simulations are based on the Reynolds-averaged Navier-Stokes equations and the turbulence model used was the RNG k-[epsilon] turbulence model. The governing equations are,

Continuity equation,

[delta][u.sub.i]/ [delta][x.sub.i] = 0 (1)

Momentum equation,

[delta] [u.sub.i]/ [delta]t + [u.sub.j] [delta] [u.sub.i]/ [delta] [x.sub.j] = -1[delta]p/[rho] [delta] [x.sub.j] +[varies]/[rho][[delta].sup.2][u.sub.i]/ [delta] [x.sub.j] [delta] [x.sub.j]-[delta]/ [delta] [x.sub.j]([[bar].[u'.sub.i][u.sub.j]]) + [g.sub.t] (2)

And the Reynolds stress is introduced as,

-[[bar].[u'.sub.i][u.sub.j]] = 1/[rho] [[varies].sub.t]( [delta] [u.sub.i]/ [delta] [x.sub.j] + [delta] [u.sub.j] [delta] [x.sub.i]) - 2/3[[delta].sub.ij]

where

t is the time;

[g.sub.i] is the gravitational body force;

[rho] is the air density;

[epsilon] is the turbulence dissipation rate;

[x.sub.i] and [x.sub.j] are the coordinates;

[u.sub.i] and [u.sub.j] are the i and j component of the velocity;

k is the turbulence kinetic energy;

[varies] is the dynamic viscosity;

[[varies].sub.t] is the turbulent viscosity, ([[varies].sub.t] = [rho] [C.sub. [varies]][k.sup.2]/ [epsilon])

To account for the presence of wall, we have to include low Reynolds number effect into the turbulence model. Without such consideration, the turbulence model may fail to predict the sharp changes in the turbulence kinetic energy close the building surface. The RNG k-[epsilon] turbulence model was derived using a precise statistical technique (called renormalization group theory), (Yakhot and Orszag, 1986).

Air change rate and ventilation efficiencies

The ventilation is generally expressed in terms of air change rate, which is measured as the number of air changes per hour (ACH) in a defined volume space. An air change rate of 1 ACH of a room means that an amount of air equal to the room volume is removed and replaced by outside air once every hour. The air change rate and ventilation efficiencies of a building cluster are vital for the supplies of "outside" air into the building cluster, and the removal of pollutants from it.

Apart from the flow rate through the street openings, the airflow due to the buoyancy effect in close proximity to the building wall may also play another important role to the ventilation rate of the building cluster. We have to consider the airflow flowing into (inflow) and flowing out (outflow) of the building cluster in all directions including the roof.

We may adopt the same volumetric air exchange rate per hour used for the study of ventilation in indoor environment. The only difference is we have to create a virtual cubical to enclose the building cluster Fig 5a with the intention that ventilation rate of the virtual cubical can be determined.

[FIGURE 5 OMITTED]

We may also determine the contaminant removal effectiveness in future study, so as to measure how quickly an air-borne contaminant can be removed from the building cluster.

There are both inflow and outflow air flowing through each surface of virtual cubicle, as shown in Fig 5b, which including the surfaces parallel to the wind direction, surfaces perpendicular to the wind direction and the roof. In determining the inflow and outflow airflow rate, it can be obtained by,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3); [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)

where

[Q.sub.n.sup.in] and [Q.sub.n.sup.out] are the air volume flow in and flow out of the surface "n" of the virtual cubicle;

[[bar].V] is the velocity vector;

[[bar].[n.sub.in] and [[bar].[n.sub.out] are the normal directions of the surface in the direction of flowing into and out of the virtual cubicle correspondingly;

[A.sub.n] is the area of the surface "n";

The total airflow into and out of the virtual cubicle, or the building cluster can be determined by:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

And the magnitude of the air volume flow into the building cluster should be equivalent to the air volume flow out of the building cluster according to the continuity equation. After determining the total air volume flow into (or flow out of) the building cluster, the air change rate of the building cluster can then be defined as,

ACH = [Q.sub.in] / (volume of virtual cubicle - volume of buildings)

In addition, there is an effective flow rate through the open roof due to the turbulence exchange (Liu et al., 2005; Li et al., 2005) and (Hang et al., 2008), which can be determined by

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)

Where [[delta].sub.w] is the fluctuation velocity, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

[k.sub.roof] is the turbulence kinetic energy at the roof surface;

Similar turbulence exchanges will also introduce fluxes through other openings, but will not affect the mean flow rate through the particular openings. Thus, those turbulence exchanges will contribute to the contaminants exchange, but not the ACH.

RESULTS AND DISCUSSION

Throughout the study, the following parameters were used to examine the change in airflow pattern, temperature contour and velocity profile and air change rate under various circumstances.

1. Wind speed (inlet velocity);

2. Heat flux on the building surfaces;

3. Building geometry, the aspect ratio, ratio of building height to the street width.

Air change rate was one of the major features examined in this study. Before the mixed-convection flows of the microclimate environment of the building cluster were investigated, we examined the cases with wind-driven flow and buoyancy driven flow alone.

Wind-driven flow

There is an impression that the ventilation of a building cluster will be diminished as the aspect ratio increases. The study examines the air change rate (ACH) in various aspect ratio and incoming wind speed.

Table 1 is a summary of the ACH of the building cluster in different aspect ratios m and incoming wind speed. The air change rate increased almost 100% when the incoming wind speed was doubled. This can be understood that the increases in incoming wind speed drove much air volume into the street canyon, which has boosted the air change rate.
Table 1. Summary of the air change rate ACH of the building cluster with
different aspect ratio m and incoming wind speed, under wind-driven
flows only.

              Aspect ratio
                (Building
            height to street
                 width)

Incoming          m = 5        m=10    m=20
wind speed

1 m/s                  26.23   30.02   28.73
2 m/s                  54.67   58.70   55.05
4 m/s                 109.03  114.70  112.82


However, when one compared the air change rate with different aspect ratios, as indicated in Fig 6, it is interesting to note that the air change rate was not very much sensitive to the aspect ratio m. The air change rate was only responsive to the incoming wind speed. Minor change from 2% to 14% was found in the air change rate when the aspect ratio was escalated from 5 to 10 and 20. It was concluded that the air change rate of the building cluster was independent of the aspect ratio.

[FIGURE 6 OMITTED]

Buoyancy-driven flows

There are interactions between buildings and the direct as well as reflected solar radiation. The short-wave radiation exposed on the building surfaces will be partially absorbed and partially reflected, while portion of the absorbed component will cause the outside surface temperature elevation and lead to a re-release of energy to the surrounding. Various heat fluxes on the building surfaces (including roof) were used in our study to represent the resultant of energy released from building surfaces, which contributed the buoyancy-driven flows.

Table 2 summarized the air change rate of a building cluster under the influence of buoyancy-driven flows alone (without incoming wind condition). Basically, the thermal convection boundary layer airflows benefited the ventilation of a building cluster. There was 10-20% increase in air change rate when the heat flux of the building surfaces were increased from 0 to 50 W/[m.sup.2] or -50 W/[m.sup.2]. The increase went up almost 30% when the heat flux was increased to 100 W/[m.sup.2]. Furthermore, it was also discovered that the increase in air change rate was much significant when the building surfaces gain positive heat flux. The situation can be found during a sunny day in the winter, where the building surfaces' radiant temperature was higher.
Table 2. Summary of the air change rate of the building cluster in
different aspect ratio m and heat flux on the building surfaces,
under buoyancy-driven flows only.

                      Aspect
                  ratio(Building
                 height to street
                      width)

Heat flux on           m = 5         m=10   m=20
building
surfaces

50 W/[m.sup.2]              31.39  35.75  47.97

75 W/[m.sup.2]              37.02  39.48  56.24

100 W/[m.sup.2]             42.03  43.97  61.64

- 50                        19.71  22.12  31.05
W/[m.sup.2]

- 75                            -  24.69  35.09
W/[m.sup.2]


[FIGURE 7 OMITTED]

While examining the air change rate ACH with various aspect ratios, it is found that the increase in air change rate was much significant in large aspect ratio, which means narrower streets. The air change rate enhanced approximately 15% and 50% when the aspect ratio was changed from 5 to 10, and 5 to 20 respectively.

Although at this moment, one cannot conclude that the thermal convection boundary layer airflows predominates in the ventilation of narrow street configurations, the findings suggested that buoyancy flows play a very crucial role in the air change rate of a building cluster.

Mixed-convection Flows

Finally, different aspect ratios, heat fluxes and incoming wind speeds were used in the study to further understand the ventilation of a building cluster.

Table 3 summarized the air change rate of a building cluster with various aspect ratios (5, 10 and 20) and heat fluxes on the building surfaces, under different incoming wind speeds. The results shown that thermal buoyancy flows enhanced the air change rate of a building cluster regardless of the incoming wind speed, as indicated in Fig 8. And it was more significant with positive heat flux on building surfaces (building with heating in winter) than negative heat flux (building with cooling in summer).
Table 3. Summary of the air change rate of the building cluster in
different aspect ratio m and heat flux on the building surfaces, under
(a) buoyancy forces only (no incoming wind), (b) combine forces and
incoming wind speed at 1 m/s, (c) 2m/s, (d) 4 m/s.

                 Aspect ratio
                   (Building
                   height to
                    street
                    width)

Heal flux on         m = 5       m=10      m=20
building
surfaces

-75 W/[m.sup.2]                24.69       35.09

-50 W/[m.sup.2]         19.71  22.12       31.05

0 W/[m.sup.2]                      -           -

50 W/[m.sup.2]          31.39  35.75       47.97

75 W/[m.sup.2]          37.02  39.48       56.24

100 W/[m.sup.2]         42.03  43.97       61.64

                 Aspect ratio
                   (Building
                   height to
                    street
                    width)

Heal flux on         m= 5      m=10   m=20
building
surfaces

-75 W/[m.sup.2]         15.14  47.55  53.61

-50 W/[m.sup.2]         30,45  36.69  49.99

0 W/[m.sup.2]           26.23  30.02  28.73

50 W/[m.sup.2]          38.97  55.08  55 68

75 W/[m.sup.2]          43.06  59.95  63.69

100 W/[m.sup.2]         47,34  64.28  70.32

                 Aspect ratio
                   (Building
                   height to
                    street
                    width)

Heal flux on         m=5       m=10   m=20
building
surfaces

-75 W/[m.sup.2]         53.39  73.38  67.99

-50 W/[m.sup.2]         54.41  71.62  64.9S

0 W/[m.sup.2]           54.67   58.7  55.05

50 W/[m.sup.2]          56.55   74.6  66.17

75 W/[m.sup.2]          57.43  82.74  74.15

100 W/[m.sup.2]         63.53  83.61  80.01

                 Aspect ratio
                   (Building
                  height (to
                 street width

Heal flux on         m=5        m=10    m=20
building
surfaces

-75 W/[m.sup.2]        113.08  124.54  117.25

-50 W/[m.sup.2]        110.39       -   115.7

0 W/[m.sup.2]          109.03   114.7  112.32

50 W/[m.sup.2]          99.88  122.01  119.36

75 W/[m.sup.2]              -  126.39  122.69

100 W/[m.sup.2]        118.36  127.57  126.23


[FIGURE 8 OMITTED]

However, Fig 8(d) indicated that it was less significant and less sensitive to the aspect ratio when the incoming wind speed increased. The air change rate increased 48% to 90% as aspect ratio increased while incoming wind speed was as low as 1 m/s. The air change rate changed only 10% when incoming wind speed reached at 4 m/s. Also, the rate of increase in the air change rate was higher in those cases with greater aspect ratio of 10 or 20 instead of 5. It demonstrated that buoyancy-driven flows were more important in those building clusters with large aspect ratio (narrow street).

CONCLUSION

Urbanization has induced radical change in the look and also the atmospheric properties of an urban area. Buildings become part of the urban area, and they are engaged in the transformation of thermal characteristics and airflow pattern of the urban environment. Besides, the buildings and the building cluster geometry might trigger the formation of radiation trap and stagnated air. The reflection of short wave radiation is also depended on the individual building's reflective surfaces, and also on the building cluster's geometrical arrangement. The airflows affect the pollution dispersion, and the air pollution in return impinges on the transfer of radiation. All these factors are well knitted and they are interactive to each other. This study attempted to assess the ventilation of a building cluster by the combined forces, wind-driven and buoyancy driven airflows.

Before evaluating and addressing a good system design for better IAQ and building energy efficiency, the understanding of airflows surrounding a single building, as well as a group of buildings; we named it as a building cluster, seems equally important. Both winds and thermal convection boundary flows along the vertical building walls have resulted in a different microenvironment. The airflow pattern, temperature distribution, overall airflow directions, air change rate and ventilation efficiencies should be examined to understand the pollutant removal capability of a building cluster. In order to improve the ventilation of the building cluster, it is essential to enhance the wind flows in the outdoor environment. Our study demonstrated the important role of building cluster geometry in the ventilation performance. The air change rate was enhanced radically with the increase of the street's width. We also demonstrated that the thermal convection boundary flows, which are resulted from the building surface heat flux, have contributed to the improvement of the ventilation efficiencies. The outcomes are invaluable for identifying an engineering approach to improve urban ventilation as well as building performance. At the same time, engineers should work closely with town planners and landscape architects to develop the overall urban and building ventilation strategies.

REFERENCES

ASHRAE. 2009. Indoor air quality guide: Best Practices for design, construction, and commissioning. American Society of Heating Refrigeration and Air Conditioning Engineers, Inc.

Castro, I.P. and Robins, A.G., 1977. The flow around a surface mounted cube in uniform and turbulent streams. Journal of Fluid Mechanics 79, 307-335.

Hang, J., & Li, Y. (2010). Wind Conditions in Idealized Building Clusters: Macroscopic Simulations Using a Porous Turbulence Model. Boundary-Layer Meteorol 136(1), 129-159.

Jones, A.P., 1999. Indoor Air Quality and Health. Atmospheric Environment 33, 4535-4564.

Li, X.X., Liu, C.H., and Leung, D.Y.C., 2005. Development of a k-[epsilon] model for the determination of air exchange rates for street canyons. Atmospheric Environment 39, 7285-7296.

Liu, C.H., Leung, D.Y.C., and Barth, M.C., 2005. On the prediction of air and pollutant exchange rates in street canyons of different aspect ratios using large-eddy simulation. Atmospheric Environment 39, 1567-1574.

Liu, X.Y., Zhag, G.Q., Xiong, Z.M., and Zhang, Q., 2005. Modeling the size distribution of indoor suspended particulate matter with outdoor particulate matter parameters. In proceedings of Indoor Air 2005, Beijing.

Oke, T. (1988). Street design and urban canopy layer climate. Energy and Buildings, 11, 103-113.

Theurer, W. (1999). Typical building arrangements for urban air pollution modelling. Atmospheric Environment, 33, 4057-4066.

Tsui, E. and Li, Y.: Near-building vertical concentration profile of air pollutants in a densely populated and high-rise urban environment. In the Proceedings of the Healthy Buildings 2006 conference, Lisboa, Portugal, 4-8 June 2006.

United Nations. 2010. World Urbanization Prospects: The 2009 Revision.

KC Tsui

ASHRAE member

Yuguo Li, PhD

Fellow ASHRAE

KC Tsui is a research student in the Department of Mechanical Engineering, University of Hong Kong, Hong Kong. professor in the Department of Mechanical Engineering, University of Hong Kong, Hong Kong.
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Author:Tsui, Kc; Li, Yuguo
Publication:ASHRAE Transactions
Article Type:Report
Geographic Code:9HONG
Date:Jan 1, 2012
Words:4080
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