State and Prospects of Outdoor Environment Research in TPU
Ryuichiro Yoshie
Tokyo Polytechnic University, 1583 Iiyama, Atsugi city, Kanagawa, Japan
ABSTRACT: The Global COE Program “New Frontier of Education and Research in Wind Engineering” of Tokyo Polytechnic University (TPU) will be completed at the end of March, 2013.
In the five year period of the program, the team Project 3 has been carrying out researches on
outdoor wind/thermal environment and air pollution. This paper outlines the framework of the entire research plan of the Project 3, and introduces some of the research results that we achieved
remarkable progress. These study areas are taking important role in the framework of the entire
research of the Project 3. This paper also shows future perspective of the Project3, and discusses
the strategy and responsibility of our generation to maintain the activities of Wind Engineering
Research Center of TPU.
KEYWORDS: Global COE program, Heat island phenomena, Air pollution, Urban ventilation,
Urban planning, Atmospheric stability
1 INTRODUCTION
Winds have a great influence on the outdoor environment in urban areas. Problems they causeroughly be classified into strong wind and weak wind issues. Strong winds around high-rise buildings bring about unpleasant and in some cases dangerous situations for people in outdoor environment. Weak wind conditions can also cause problems. Air pollution and the heat island phenomena become more serious in weak-wind regions in urban street canyons. The importance ofurban ventilation is now broadly recognized as a countermeasure to the urban heat island phenomena and air pollution problem, so it is becoming extremely important to ensure adequate air
ventilation paths in urban spaces. The winds enhance natural ventilation in buildings which can reduce the energy consumption of mechanical ventilation fans and air conditioners for cooling. The
moderate winds improve human thermal comfort for both indoor and outdoor environment insummer. The environmental wind engineering associated with the wind tunnel experiment, fieldmeasurement, and numerical analysis can contributes to the solutions of those issues.
In the Global COE program of Tokyo Polytechnic University, Project 3 covers research andeducation in the fields of outdoor wind environment and air pollution. The ultimate purpose of the
project is proposal of guidelines for urban development. Fig.1 shows framework of the entire research of Project 3. Many research items direct toward this ultimate goal with some of themclosely related to each other. In this article, achievement in some of the important study areas,
namely: Reproduction of occurrence frequencies and vertical profiles of wind velocity and temperature by meso-scale simulation, Generalization of convective heat transfer from urban canopy
surfaces, Effects of building arrangement on urban ventilation and their generalization, Effect of
atmospheric stability on urban pollutant concentration and its generalization, Large Eddy Simulation of gas/thermal dispersion in non-isothermal boundary layer, are outlined and presented.

Proposal of guidelines
for urban development

Revolution of environmental impact
assessment system for air pollution

Environmental impact
assessment system forpedestrian windenvironment

Generalization:effect of
atmosphericstability on
Urban pollutant
concentration

Preparation ofstandard wind data

Preparation of standard
atmospheric stability data

Reproduction of occurrence
frequencies and vertical profiles ofwind and temperature

Generalization: effect ofbuilding arrangement on
urban ventilation performance

Development ofexperimental techniquefor pollutant/thermal
dispersion

Analyses of heat island
Phenomena by WRF

Coupling Urban Canopy
Model with WRF

Modification of
Urban Canopy Model

Validation
Development of
numerical technique forpollutant/thermal dispersion

Generalization:
convective heat Transferfrom urban canopy

Preparation of representative
Inflow profile ofwind and temperature

CFD analyses of Urban
heat island phenomena

Figure 1 framework of the entire research of Project 3
2 RESEARCH RESULTS OF OUTDOOR ENVIRONMENT IN TPU
2.1 Reproduction of occurrence frequencies and vertical profiles of wind velocity andtemperature by meso-scale simulation
To assess the pedestrian wind environment around tall buildings based on occurrence frequenciesof wind velocities, we need reliable statistical wind observation data from near their construction
sites. However, wind observatories are not always located near construction sites. Even if they
do, the observation height is sometimes not high enough and the wind data are affected by surrounding buildings. Meso-scale simulation can be an alternative to direct observation. We aimed
to use the WRF (The Weather Research and Forecasting Model), a meso-scale simulation model,
in order to prepare standard wind data at high-altitude for the assessment of the pedestrian windenvironment. We also aim to use WRF for research on urban heat island phenomena, which is becoming serious in large cities in Japan. One of the effective countermeasures against heat island
phenomena is to lead cool air of sea breeze into urban canopies. This strategy strongly depends onthe vertical profile of wind velocity and the temperature of the sea breeze. Before doing these investigations using WRF, it is necessary to confirm how WRF can correctly reproduce the occurrence frequencies and vertical profiles of wind velocities. For this validation, observation datameasured by Doppler Sodar in the Minami Senju district in Tokyo (Miyashita et al. 2002) wereused. In order to well regenerate the vertical profile of wind velocity by WRF, it is considered thatan appropriate surface roughness should be given to the WRF calculation. However, the default
setting of WRF based on USGS (United States Geological Survey) expresses urban areas as only
by one category and gives a uniform roughness length regardless of building densities and heights.

Thus, we used GIS (Geographic Information System) data to appropriately classify urban landuse categories and to give roughness lengths to the WRF calculation. We conducted two cases of
WRF calculations using a default setting and using GIS data, and the results were compared withthe observation data.
Calculations were carried out for a period of six month from January 1 to June 30, 2000. To
set initial and boundary conditions we used FNL (Final) Operational Global Analysis data fromthe NCEP (National Center for Environmental Prediction). The calculations were conducted using
three-stage, two-way nesting grids. The calculation domains is shown in Fig. 2. The Minami Senju
district, used for the comparison, is included in Domain 3.
Domain1
Domain 3
MINAMISENJU

Domain 2

Figure 2 Computational Domain
The default setting of WRF employs the USGS‟s 24 categories of land use and their corresponding ground-surface parameters such as roughness length, albedo, and so forth. However, in the
USGS, urban area is expressed by only one category and its roughness length is uniformly 80cm
regardless of building density and building height. Moreover, some areas that would be considered “urban” in reality are classified as pasture, grassland, and other categories in the USGS.
Thus, the USGS does not sufficiently express the actual circumstances of Tokyo. For these reasons, we used GIS data published by the Ministry of Land, Infrastructure, Transport, and Tourism
to appropriately classify land-use categories. Furthermore, we classified the urban area into three
categories, “Low”, “High”, and “Commercial” (Table 1). We used roughness lengths of 70cm,
100cm, and 150cm for Low urban area, High urban area, and Commercial urban area, respectively. We adopted these values by referring to Grimmond and Oke (1999). Fig. 3 shows land-use
categories based on the USGS and the GIS, which shows large differences between them.
Table 1
No.

1
2
3
7
15
16
19

Land-use Categories and Surface Parameters

USGS/SIB
GIS
Land-Use Category Land-Use Category
Urban Land
Dryland Croplandand Pasture
Irrigated Croplandand Pasture
Grassland
Mixed Forest
Water
Barren or Sparsely
Vegetated

House and
Pablic Building
Farm
Paddy Field
Park and Grass
ForestLake and Sea
Soil Surface

Urban
Category

Surface Z0[cm]

Albedo[%]Soil
Moisture[%]

Surface
Emmisivity
[%]

Low
High
Commercial

70
100
150

15

10

88

15

17

30

98.5

10

18

50

98.5

12
50
0.01

19
13
8

15
30
100

96
97
98

1

25

2

90

-

2a) USGS

Urban
Water

Low-rise Urban
High-rise Urban

Grass, Forest, etc

Commercial Urban
Water
Grass, Forest, etc2b)(b)GIS
GIS

2a) USGS(a) USGS

Figure3 Land-use
Low-rise
Urban Categories in Domain 3
High-rise Urban

N
NNW

20

NNE

15

NW

NE

10

WNW

ENE

5
W

0

E

WSW

ESE
SW

SE
SSW

SSE
S

OBS
Case1(USGS)
Case2(GIS)of exceedance
probability
超過確率

We carried out two kinds of calculations: one using the default setting of WRF with USGS (CaseCommercial
Urban1), and the other using the land-use
categories
and surface parameters based on GIS data. Fig. 4
and Fig. 5 compare wind rose andprobabilities
of exceedance of mean wind velocity (10 minutes
Water
averaging 300m high) between the
results of WRF and observation data, respectively. Calculated
Grass, Forest, etc
wind roses by Case1 and Case2 agreed well with the observation data. The probability of exceedance of wind velocity calculated by Case 2 agreed very well with the observation data. The probability of exceedance calculated by Case 1 was higher than that of Case 2. However, the difference was not so large2b)
at GIS
this high altitude.
1
OBS
0.8

Case1 (USGS)
Case2 (GIS)

0.6
0.4
0.2
0
0

5

10

15

20

25

風速 (m/s)(m/s)
Wind velocity

Figure 4 Wind rose

Figure 5 Probability of Exceedance
for Mean Wind Speed

For the comparison of vertical profiles for mean wind velocity, we firstly extracted southerly wind
(wind direction=180°±33°at 200m high) for both observation and calculation. Then the vertical profiles of wind velocity of the observation data were classified into eight clusters by cluster
analysis. Corresponding clusters of calculated results were made so that each cluster at the sametime of the observation data became the same cluster. Fig. 6 shows vertical profiles of wind velocities in representative four clusters. Averaged values ±1σ(standard deviation) at each height ineach cluster are plotted in the figures. The percentage shown in the caption for each cluster is theoccurrence frequency for that cluster. There is a pretty good match between observation and calculated results. In particular, Case 2 using GIS is remarkably close to the observation values. Case
1, using the default setting of USGS, generally show higher values than the observation data.

350

350

300

350

200
150

250
200
150

250
200
150

250
200
150

100

100

100

50

50

50

50

0

0

0

0
5 10 15 20Wind Speed (m/s)

0
5 10 15 20Wind Speed (m/s)

0
5 10 15 20Wind Speed (m/s)

Case1 (USGS)

Case2 (GIS)

Case1 (USGS)a) Cluster 1 (Occurrence Frequency 17.4%)

350

350

350

200
150

300

Height (m)Height (m)

250

OBS
WRF

400

300

300

Case2 (GIS)b) Cluster 2 (Occurrence Frequency 19.1%)

OBS
WRF

400

250
200
150

0
5 10 15 20Wind Speed (m/s)

250
200
150

350
300
250
200
150

100

100

100

100

50

50

50

50

0

0

0

0

0
5 10 15 20Wind Speed (m/s)

0
5 10 15 20Wind Speed (m/s)

0
5 10 15 20Wind Speed (m/s)

Case1 (USGS)

Case2 (GIS)

Case1 (USGS)c) Cluster 3 (Occurrence Frequency 13.5%)

OBS
WRF

400

Height (m)

OBS
WRF

400

Height (m)

350

100
0

OBS
WRF

400
300

300

Height (m)

250

OBS
WRF

400

300
Height (m)Height (m)

OBS
WRF

400

Height (m)

OBS
WRF

400

0
5 10 15 20Wind Speed (m/s)

Case2 (GIS)d) Cluster 4 (Occurrence Frequency 11.4%)

Figure 6 Vertical Profile of Mean Wind Speed
As mentioned above, it becomes clear that the WRF can well reproduce the wind rose and occurrence frequencies and vertical profiles of wind velocity if the roughness length is appropriately
given. Although description about temperature was omitted in this manuscript, occurrence frequencies and vertical profiles of the temperature were also investigated. It was found that occurrence frequencies and vertical profiles of temperature calculated by the WRF agreed very wellwith the observation data except at lower altitude. Prediction accuracy of the temperature at lower altitude was improved by coupling an Urban Canopy Model with the WRF. Thus, we obtained
a prospect that the WRF can be used for our purpose, i.e. preparation of standard high-altitudes
wind data, extraction of representative vertical profiles of wind velocity and temperature, andanalysis of urban heat island phenomena.
2.2 Generalization of convective heat transfer from urban canopy surfaces
The WRF coupled with an Urban Canopy Model is considered as an effective tool for the prediction of urban heat island phenomena. The Urban Canopy Model is responsible for predicting theheat transfer from the urban canopy to the overlaying atmosphere. In the Urban Canopy Model(Kusaka et al., 2001) adopted by the WRF, the local convective heat transfer from the urban canopy surfaces and its dependence on urban parameters such as building coverage ratio and buildingheight variations are not explicitly modeled. In the model, the Convective Heat Transfer Coefficient (CHTC) from canopy surfaces is evaluated from Jurge's formula (1924). It is based on the
CHTC of a heated copper square plate, which was oriented perpendicular to a uniform air flow ina wind tunnel. In this formula, the local convective heat transfer coefficient from building walls
and ground depends only on the velocity inside the canopy. However, this cannot be justified
since other urban parameters also contribute to the heat transfer coefficient. Moreover, this modelcannot distinguish the difference between convective heat transfer coefficients on different wall
surfaces, i.e., windward, leeward, side wall of the building and the ground, instead it expresses theCHTC generally as wall. Thus, we need to generalize the CHTC of the building canopy surfaces(ground, windward wall, leeward wall and side wall) more explicitly with respect to the urban parameters that affecting the CHTC of urban canopy surfaces.
For this purpose, wind tunnel experiments were firstly carried out to roughly grasp the dependence of urban parameters on bulk heat transfer from an urban canopy in a thermally stratified
wind tunnel as shown in Fig. 7 and 8. Six experimental cases were carried out for different configurations of urban canopy (three cases with uniform-height blocks of 0.05m and the other threecases with 0.05m and 0.1m-height blocks arranged in a staggered manner). The mixed arrangement of 0.05m- and 0.1m-height blocks are referred to as non-uniform-height cases. The building
coverage ratios (hereafter referred to as BCR) were varied as 6%, 11% and 25% for both the uniform- and the non-uniform-height cases.

Figure 7

Experimental setup

Figure 8 Experimental setup and List of experimental cases

0.05ｍ

Y
X

0.05ｍ

0.025ｍ 0.025ｍ

Y

0.025ｍ 0.025ｍ

However, it is not an easy task in wind tunnel experiments to evaluate local CHTC, which vary on
individual surfaces of building roof, wall and ground. Hence CFD simulation with a lowReynolds-number k-ε model was conducted to predict the convective heat transfer on these surfaces. The two-equation heat-transfer low-Reynolds-number model proposed by Abe et al. (1994,
1995) was chosen for the present work. Abe et al. reported that this model quite successfully
predicted turbulent heat transfer in a separating and reattaching flow. The computational domain
was an exact replica of the wind tunnel in windward length and vertical height. Minimum width
was selected by considering symmetry in the Y direction. Fig. 9 shows a top view of the computational domain and grid arrangement (for example: BCR-25% case). Structured mesh arrangementwith very fine mesh near the wall surfaces (first mesh 0.2mm from wall surfaces). As a result, nondimensional distances from the wall Y+ were below 1.0 for most of the fluid cells close to the wall
surfaces. No- slip boundary conditions were applied for wall shear stress. For thermal boundary
conditions, surface temperatures obtained from the experiments were prescribed and a heat con0.05ｍ
0.05ｍ
duction boundary condition (Fourier‟s
Law)
was applied for heat flux on the wall surfaces.

Figure 9 Top view ofX part of computational domain(For example: BCR-25% case)

3000

Bulk heat transferred (W)Bulk heat transferred (W)Figure 10(a) and (b) compares the bulk heat transferred from the urban canopy by the experiments
and the CFD simulations for uniform- and non-uniform-height cases, respectively. The CFD results showed good agreement with the experimental results. The velocity and temperature profiles of the CFD results also corresponded well with the experimental results for all cases (Pillai etal., 2010). Thus, we judged that the CFD simulations were valid.
Experiment
CFD
2000

1000

0
BCR 6%BCR - 11%

BCR - 25%

Experiment
CFD
2000

1000

0
BCR 6%(a) Uniform-height case

Figure 10

3000

BCR - 11%

BCR - 25%(b) Non-uniform-height caseComparison between experiment and CFD simulations:
Bulk heat transferred from urban canopy for various BCR

After this validation, the local CHTC for a particular wall surface was investigated from the CFD
results. The heat flux q (W/m2) on the floor and block surfaces were obtained from CFD results
and surface integrated for each surface area. The local CHTCs for individual canyon surfaces
were obtained from equation (1)
m

q A

j j

CHTC j 1

S  (Ts  TR )(1)

where CHTC - convective heat transfer coefficient (W/m2°C ), qj = surface heat flux on individual
cells on floor and block surfaces (W/m2), Aj =area of individual cells on surfaces, m = number ofcells on floor and block surfaces, S = area of individual canyon surfaces like windward wall, leeward wall, ground wall and side wall (m2), Ts= area-weighted average temperature of ground andblock surfaces (°C), TR = reference temperature (°C).
Fig. 11 and Fig. 12 (for example) shows the local CHTC of canyon surfaces with horizontal
distance in the flow direction for roof, windward wall, leeward wall and ground The CHTC decreases with the horizontal distance in the windward direction due to higher air temperature in thedownstream direction. Moreover, the CHTC increases with decrease in building coverage ratio.
The CHTC is higher for roof and windward wall than the other surfaces like leeward wall andground. Thus, the effect of building coverage ratio and building height variation of an urban canopy should be considered in evaluating the CHTC of canopy surfaces. Also, the CHTC should bemodeled for individual building surfaces and not in general as CHTC for walls.
40

CHTC (W/m C)

30

30

2o

2o

CHTC (W/m C)

40

20

10
Roof
Leeward wall
Sidewall

20
10

Roof
Leeward wall
SidewallWindward wall
GroundWindward wall
Ground

0

0
0

2

4

6

0

2
4
Horizontal Distance (m)Horizontal Distance (m)(a) Uniform height case6(b) Non-uniform height case

30

30

25

25

CHTC (W/m C)

20

2o

2o

CHTC (W/m C)

Figure 11 CHTC profile in horizontal distance in the flow direction for
individual canopy surfaces for 6% BCR.

15
10
Roof
Leeward wall
Sidewall5

Windward wall
Ground

20
15
10
Roof
Leeward wall
Sidewall

5

0

0
0

2

4

6

0

2

Windward wall
Ground4

Horizontal Distance (m)Horizontal Distance (m)(a) Uniform height case(b) Non-uniform height case

6

Figure 12 CHTC profile in horizontal distance in the flow direction for
individual canopy surfaces for 25% BCR.
Although these results were obtained from a CFD simulation at wind-tunnel-model scale, it would
be applicable to real-scale heat transfer phenomena if there were a valid similarity between thescales. We checked the similarity by performing CFD simulation for 10 times and 100 times thewind tunnel model scale. It was confirmed by the relationship between Rex (local Reynolds number) and Nux (local Nusselt number). As shown in Fig. 13, the relationships between Rex and
Nux obtained from different scale simulations could be expressed by one function regardless ofscale and wind velocity. Thus the local CHTCs from model scale can be converted to those of real
scale.
1000000

1000000

WT model scale

WT model scale 10 times
WT model scale

10 times
100 times
WT model scale WT model scale

100000100000y = 1.3228x0.6115

0.7688y = 0.0752x

2

R = 0.995

2

Nux

R = 0.9964

Nux

100 times
WT model scale

10000

10000

1000

1000

100

100
1000001000000

10000000

Re x

100000100000000

10000000

100000000

Re x(a) Roof

Figure 13

1000000(b) Windward wall

Relationship between local Reynolds number and local Nusselt number.

the model equation
Nuc predictedfrom thebyproposed
Nux obtained

After this confirmation, parametric CFD simulations more than 30 cases changing urban configuration parameters were conducted in order to generalize the CHTC from the urban canopy surfaces to the atmosphere. Tentative equations for evaluating local CHTC from urban parameters andatmospheric parameters were proposed based on the results of the parametric CFD simulation.
Fig. 14 compares the local Nusselt number (Nux) obtained from the proposed equation and CFD
simulations. The proposed equation well expresses the Nux obtained from the CFD simulations.
But we need more parametric studies to increase generality. The generalized equations will be incorporated into the Urban Canopy Model in WRF which can be used for urban planning for mitigating urban heat island phenomena.
500
y = 0.99x + 0.52
R2 = 0.99400

300

All of the 32 cases areplotted in this figure

200

100

0
0

100

200

300

400

500

NuNu
CFDSimulation
simulationx obtained
obtained fromfrom CFDc

Figure 14 Comparison between local Nusselt number obtained from
CFD simulation and proposed equation

2.3 Effects of building arrangement on urban ventilation and their generalization
With rapid urbanization, urban air pollution and urban heat island effects have become more andmore serious. Urban ventilation efficiency should be taken into account since it affects the dispersion and dilution of pollutant and heat in urban areas. It is worth systematically investigating theeffects of urban building arrangement (such as building coverage ratio, building array, and building height variation) on urban ventilation efficiency in order to practically guide urban ventilation
design in these areas.
To investigate the effect of building arrangement on urban ventilation efficiency, we firstly
chose a dense city in Hong Kong for a representative urban area. Hong Kong, where population isconcentrated on a small land and high-rise buildings are densely packed, is facing serious problems of the heat-island phenomena and the accumulation of pollutants because of the poor ventilation in cities and the rapid increase in energy consumption. In order to tackle these problems, theCity Planning Bureau of the Hong Kong government aims to establish the “Air Ventilation Assessment System (AVAS).” The consultant team for the AVAS has started the investigation on
the city planning guidelines for improving air ventilation and assessment methods for the air ventilation, under the slogan of “The more air ventilation, the better.” The most adopted measure for
improving air ventilation in a city is to lead the horizontal wind to the inside of the city, that is, tosecure “horizontal ventilation paths” just above the ground. Actually, there have been some reports (Kubota et al. 2000, Kubota et al. 2002) that urban air ventilation can be improved by lowering gross building coverage ratio, and so reducing building coverage ratio by enlarging street
width and open space is extremely effective as a measure for mitigating the heat island phenomena. However, we consider that in the urban area densely packed with high-rise buildings, such as
Hong Kong, it is difficult to reduce considerably building coverage ratio. Therefore, it is important to lead the upper breeze to the inside of the urban canopy by modifying the configurationof buildings, that is, to secure “vertical ventilation paths.” With the purpose of providing fundamental information for the AVAS establishment, we investigated the influences of the configuration of buildings in terms of floor area ratio, building coverage ratio, and variation of buildingheights on the wind velocity at the pedestrian level.
With reference to the about-600-m range of the urban area in Mong-Kok, which is one of theurban areas most densely packed with high-rise buildings in Hong Kong, we produced a model
district with a scale ratio of 1/600 as shown in the perspective diagram in Table 2. The reason
why such a simple model was adopted instead of reproducing the buildings in Mong-Kok accurately is that the purpose of this experiment is not a special case study targeted at Mong-Kok but
the acquisition of general knowledge regarding the effects of the configuration of buildings on the
air ventilation characteristics of the district. Table 2 shows the features of the models used in thewind-tunnel experiments. As the reference case, we prepared Case 0, in which model buildings
with the same height (assumed height: 45 m) are placed so that its street width, building coverage
ratio, and floor area ratio correspond to those in Mong-Kok. As for Cases 1 and 2, the horizontal
configuration (gross building coverage ratio) of the district was not changed, while the gross floorarea ratios of Cases 1 and 2 were set at 3/2 and 2/3 times that of Case 0, respectively (that is, thebuilding heights of Cases 1 and 2 were set at 3/2 and 2/3 times that of Case 0). As for Cases 3 and4, their gross building coverage ratios were set at 2/3 and 1/2 times that of Case 0, respectively,by enlarging street width. Meanwhile, their building heights were set at 3/2 and 2 times that ofCase 0, so that their gross floor area ratios become equal to that of Case 0. The building height ofeach of Cases 1 to 4 is homogeneous. On the other hand, as for Cases 5 and 6, there are twoheights (60 m and 30 m) and three heights (60 m, 45 m, and 30 m), respectively. Meanwhile, theirhorizontal configuration, the gross building coverage ratio, and the gross floor area ratio are equal
to those of Case 0. As for Case 7, towers with the same horizontal configuration as Case 4 (grossbuilding coverage ra-tio: 32%; height: 60 m) was placed on the upper layer of the podiums withthe same horizontal configuration as Case 0 (gross building coverage ratio: 63%; height: 15m).
This configuration is often seen at building complexes in Hong Kong. As shown in Fig. 15, measurement points are placed at 1.5 m (in real scale) above the ground within the range of 380 m (inreal scale) of these models, and then scalar wind velocity were measured.
Table 2
Case No.

Configuration of buildings in each experimental case

Case 0Case 1 (Floor area ratio(Standard case)

3/2-fold)

Case 2(Floor area ratio
2/3-fold)

Case 3 (Building coverage ratio 2/3-fold)

Perspective ofmodel arrangements
Wind

Wind

Floor area
ratio
Building
coverage ratio
Real buildingheightCase No.

Wind

Wind

954 %

1272 %

636 %

954 %

63 %

63 %

63 %

42 %

45m (homogeneous)

60m (homogeneous)

30m (homogeneous)72m (homogeneous)Case 4 (Building cov-

Case 5

Case 6

Case 7erage ratio 1/2-fold)(2 level of building height)(3 level of building height)(with Podiums)

Wind

Wind

Perspective ofmodel arrangements
Wind

954 %

954 %

954 %

32 %

63 %

63 %

90m (homogeneous)

30m, 60m (2 levels ofbuilding heights)

30m, 45m, 60m (3 levels of building heights)

Measuring point(88 points): Points in the street parallelwith the wind direction: Points in the street perpendicular
to the wind direction
76

76

Wind

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

0.40
0.30
U/Uδ

Floor area
ratio
Building
coverage ratio
Real buildingheight

Wind

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

76

0.20
76

76

76

76

0.00
upwind
side
-200

76

76

76

76

76

76

Measuring area

0.10

Figure 15

76

76

-100

0
Xr (m)

100

downwind side
200 Real X: Xr(m)Layout of buildings and measurement points

954%
Podium:63%, Tower:32%
75m (Podium:15m +Tower:60m)

The findings from this experiment can be summarized as follows:
1) It was found that the air ventilation in the cases reflecting the current situation of the urban area densely packed with high-rise buildings in Hong Kong is much poorer than that in Japanesedistricts with medium to high-rise buildings.
2) When the building height is uniform, the spatial average of the wind velocity ratios at the pedestrian level does not depend on floor area ratio, but depends on building coverage ratio.
3) When building heights are not uniform, the air ventilation especially behind buildings improved. It is necessary to consider not only the gross building coverage ratio at the pedestrian level but also the configuration of buildings at higher level. Thus we proposed “vertically averaged
gross building coverage ratio” defined by Eq. (2) to express the urban ventilation efficiency.
1 hmax(2)
λac 
λz dzhmax 0where λac: vertically averaged gross building coverage ratio,
hmax: highest building level inthe district,
λ(z): ratio of the horizontal cross-section area of buildings at an altitude ofz to the total area of the district
Fig. 16 shows the relation between the “vertically averaged gross building coverage ratio” and thespatial average of the wind velocity ratios for each case. The spatial average of the wind velocity
ratios of all cases can be almost plotted on a single line. Even if the configuration of buildings significantly varies with height, it may be possible to universally evaluate the average air ventilation
at the pedestrian level by using the “vertically averaged gross building coverage ratio”, which isuseful for designing basic schemes for urban developments.
Building coverage
ratio 1/2

Building coverage
ratio 2/3

Case4

Case3

Tower with Podium

Case7
Wind

3 building heights

Wind

Wind

Spatial Average ofWind Velocity Ratio <U/Uδ>
Spatial Average ofWind Velocity Ratio <U/Uδ>

0.25 0.25

0.2

Case6
0.2

Wind

2 building heights

0.15 0.15
Case0Case0Case1Case1
Case2Case2Case3Case3
0.1 0.1Case4Case4Case5Case5
Case6Case6Case7Case7
Japanese
high-risehigh-riseJapanese
medium-toK
0.05 0.05KJapanese
high-rise
residentialarea
residential
arearesidential
area
et al.)(Kubota(Kubotaet (Kubotaal.) et al.)
0
0
0
20
40
0
20
40

Case5
Wind

Case1
60

60

80

80

VerticallyAveraged
VerticallyAveraged
Floor area ratio 2/3
Building
Coverage
GrossGross
Building
Coverage
RatioRatio(%) (%)

Case0

Case2
Wind

Figure 16

Wind

ReferenceWind

Relation between vertically averaged gross building coverage ratio andspatial average of wind velocity ratios

4) When building heights are not uniform, the air temperature is considerably improved. This isbecause cool air above buildings is transported to the ground level and hot air in the street canyons is exhausted to the upper atmosphere effectively. This result indicates that it is important totake into account the “vertical ventilation paths” to cope with the heat island phenomena.

Secondary, a typical residential area in Shanghai (Fig. 17) was chosen to investigate the effectof building arrangement on urban ventilation efficiency. It was chosen because similar urban arrangements can be seen in many regions in China. The ventilation efficiency was investigated by
CFD simulation with a standard k-εmodel. For the CFD simulation, a simplified reference urban
model (Fig. 18) was designed according to a typical residential area. Fig. 19c shows an enlarged
view of a center block in Fig. 18. This block comprised a total of 72 residential buildings (48m (L)×12m (W) ×18m (H)) with 6 stories. The building coverage ratio (BCR), which represents the ratio of ground floor area to lot area, was 40% and the floor area ratio (FAR), which represents theratio of total floor area to lot area, was 240%. To compare the ventilation efficiency of different
urban patterns, other BCRs and arrays (Layouts 2-18) were considered, as shown in Fig. 19. The
BCR was changed by increasing the building height while keeping the FAR constant. The lengths(L) and widths (W) of buildings were not changed. The passage widths D1 and D2 between adjacent buildings changed with BCR and building array. The main road width in these models was
20m for all cases, as shown in Fig. 18.
Wind conditions at pedestrian level in an urban area play an important role in the dispersion ofvehicle pollutants, diffusion of heat, and ventilation of buildings, as well as the comfort and safetyof pedestrians. Therefore, it is necessary to understand the pedestrian wind environment aroundbuildings 0-2m (z) and to take them into account in urban design. A uniform pollutant source assumed to be a passive scalar was generated throughout the 0-2m (z) volume of the center block,as shown in Fig. 20, to simulate pollution from residence areas and vehicles. The pollutant source
volume (PSV) was 326m (LB) × 326m (LB) × 2m (h) (except the building part).

Figure 17

Typical residential area in Shanghai

Figure 18

Reference urban block model

(c) Layout 1 (BCR = 40%)(d) Layout 2 (BCR = 20%)(e) Layout 3 (BCR = 13%)(f) Layout 4 (BCR = 10%)(g) Layout 5 (BCR = 20%)(h) Layout 6 (BCR = 13%)(i) Layout 7 (BCR = 10%)(j) Layout 8 (BCR = 40%)(k) Layout 9 (BCR = 20%)(l) Layout 10 (BCR = 13%)(m) Layout 11 (BCR = 10%)(n) Layout 12 (BCR = 40%)(o) Layout 13 (BCR = 20%)(p) Layout 14 (BCR = 13%)(q) Layout 15 (BCR = 10%)(t) Layout 18 (BCR = 30%)

H (m)Layout8&12 12 m
24 m
9&13 18m
54m
10&14 36 m
72m
11&15 54m
90 m
H of non-uniform layout(r) Layout 16 (BCR = 20%)(s) Layout 17 (BCR = 30%)

Figure 19

Building arrangements
Take layout 4 (BCR=10%, Aligned array) as an example:
Pollutant Source Volume (PSV)
Location:0-2m (z) volume of center block
Size:326m (LB)  326m (LB) 2m (h) (Note: except
building part)
Five surfaces of PSV
a. Front (Windward surface)b. Back (Leeward surface)c. Left, Right (side surfaces)d. Top (upper surface)

Figure 20

Pollutant source volume (PSV)

Fig. 20 shows grid arrangement of the CFD simulation for the reference case. The computational
domain contained 8 blocks surrounding the central one. All 9 blocks had the same arrangement.
For the reference case, the domain size was 1378m (x) × 1378m (y) × 198m (z) and a structured
grid of 2,337,984 cells was made.

Enlarged view of thecenter block grid

Figure 21

Computational domain and grid arrangement

From the CFD simulation results, ventilation efficiency indices (spatially-averaged wind speed ratio, VRw (Ng, 2009) and normalized pollutant concentration, C*, and visitation frequency, VF(Bady et al, 2008) was obtained, and their relationships were investigated. The results are shownin Fig. 22. In general, Both “VRw - C*” and “VRw - VF” are negatively correlated (C* and VF
decreases as VRw increases), and “VF - C*” are positively correlated (C* increases as VF increases).
0.20

0.10

LH

40%

1.6

90

0.10

BCR=
40%
30%
20%
13%
10%

0.05

1.2

0.2

90
0

1.4

0.05
0.00
0.0

0.15
*

*

VF

A

1.8

wind direction
0
45
90

C

0.15

C

0.20

2.0
wind direction
0
45
90

0.4
VRw

0.6

1.0
0.0

0.8

(a) VRw - C*

0.2

0.4
VRw

0.6

0.8

0.00
1.0

1.2

1.6

1.8

2.0

VF

(c) VF - C*(b) VRw - VF

Figure 22

1.4

Relationships among ventilation efficiency indices

One of the most important purposes of this study was to propose a practical design parameter forevaluating urban ventilation efficiency. We proposed a practical design parameter, Passage Ratio,
PR (Hu and Yoshie, 2013) for evaluating urban ventilation efficiency. As shown in Fig. 23,
stronger relationships between PR and ventilation efficiency indices were found compared to other existing practical parameters. With increase in PR, VRw increased while C* and VF decreased.
The PR can be obtained from urban configurations, and it is possible to roughly estimate the ventilation efficiency indices from the PR. It is useful for designing basic schemes of urban developments.

0.4

2.0

0
45
90

-0.14xy = 0.111e
R² = 0.686

VF

0.15
C

VRw

0.6

0.20

0
45
90
*

0.8

0.10

0
45
90

-0.08

1.8

y = 1.275x

1.6

R² = 0.679

1.4
0.2

y = 0.073x + 0.124

0.05

1.2

R² = 0.833

0.0
0

2

4
PR

(a) PR-VRw

Figure 23

6

8

0.00
0

2

4
PR

(b) PR-C*

6

8

1.0
0

2

4
PR

6

(c) PR-VF

Relationships between passage ratio (PR) and ventilation efficiency indices8

2.4 Effect of atmospheric stability on urban pollutant concentration and its generalization
For environmental impact assessment of air pollution, a Gaussian plume model is usually used inJapan. This model is applicable for pollutant dispersion from a high chimney (Fig. 24a), but it isobvious not appropriate for pollutant dispersion within an urban area (Fig. 24b). Even so, it isused for this situation. One of the reasons why wind tunnel experiments (WT) or CFD simulations
are not commonly used for environmental impact assessment for air pollution is that too manycases of wind tunnel experiments or CFD simulations would be required because various classesof atmospheric stability would have to be considered. (Eg. 16 wind directions ×10 stability classes= 160 cases). Another reason is that few institutes have a thermally stratified wind tunnel. If ageneral function independent of wind direction, urban shape, and location expressing the effect of
atmospheric stability on pollutant concentration could be proposed, it would become possible toconduct WT/CFD for only neutral conditions and convert the results (pollutant concentrations
under neutral condition) into those in other atmospheric stability conditions by using the proposed
general function.

(a) from high chimney

Figure 24(b) within urban area

Dispersion of pollutant

We firstly attempted to find the general function that expresses the effect of atmospheric stability
on NOx concentration from observation data at air pollution monitoring stations of the Tokyo
Metropolitan Government (TMG). Observation data in this study consisted of two parts. One washourly averaged NOx concentration data observed at 25 general air pollution monitoring stations
in Tokyo 23 ward area. The other part was meteorological data including wind speed and direction, solar radiation and cloud cover of Japan Meteorological Agency (JMA). Procedure of data
analysis is as follows.
Step 1. Define hourly atmospheric stability classes from 3:00 to 21:00 based on the modified Pasquill–Gifford stability table (Table 3) using above meteorological data. The data from22:00 to 2:00 were omitted because there are no cloud cover data to evaluate atmosphericstability classes during that time zone.
Step 2. For each station normalize hourly NOx concentration value by the formulation below.
C* =CUH2/Q(2)
where C* is non-dimensional normalized NOx concentration; C is observed NOx concentration (ppb); U is wind speed (m/s); H is the representative length (m) and it is always
constant; Q is the emission rate (m3/s). But in the actual procedure, only CU (ppb･m/s)
was calculated for C* because H and Q can be eliminated after the following assumption
and procedure.
Step 3. For each station, classify the C* by 133 groups based on day and hour (7 days and 19
hours from 3:00 to 21:00). We assume that the NOx pollutant emission rate Q is the sameif the day and hour is the same.
Step 4. For each station, divide the above 133 groups into 2128 groups (133*16) based on 16
wind directions

Step 5. For each station, divide the above 2128 groups into 10640 groups (2128*5) based on 5
wind speed (U) ranges (0.5<U<2m/s; 2≤U<3m/s; 3m/s≤U<4m/s; 4m/s≤U<6m/s; 6m/s≤U). The first range 0.5<U<2m/s is different from the first range in table 3 (U<2m/s).
We omit wind speed data less than 0.5m/s because the wind direction under very weakwind condition is not stable and always changing.
Step 6. For each station, select C* under neutral conditions “C*n” on each day, hour, wind direction and wind speed range.
Step 7. For each station, average the selected C*n to obtain the 5 years averaged C*n,ave values foreach day, hour, wind direction and wind speed range.
Step 8. For each station, divide all C* data by C*n,ave to get the ratio that expresses the stability effect for each day, hour, wind direction and wind speed range. (Q and H are eliminated inthis step because the same Q and H exist in both denominator and numerator. As described in step 3, we assumed that Q is the same if the day and hour is the same.)Thus, Stability Effect Ratio = C*/ C*n,ave(3)
Table 3Modified Pasquill-Gifford stability classes

Daytime Solar radiation (T) (kW/m2)
Nighttime cloud coverSurface wind speed at
10m above groundT ≥ 0.6
0.6 > T ≥ 0.3
0.3> T ≥ 0.15
T <0.15
8~10
5~7
0~4
(U) (m/s)
U<2
A
A-B
B
D
D
G
G
2≤ U <3
A-B
B
C
D
D
E
F
3≤ U <4
B
B-C
C
D
D
D
E
4≤ U <6
C
C-D
D
D
D
D
D
6≤U
C
D
D
D
D
D
D
Note: The nighttime refers to the period when there is no solar radiation
Solar radiation data are used during daytime and cloud cover data are used during nighttime.
The first and last hour of nighttime should be defined as neutral condition “D” regardless of cloud cover.

Table 4
Stability class
Definition
Corresponding
numberA
Veryunstable

A-B

1

1.5

B

Atmospheric stability classes
B-C

Unstable2

2.5

C
Slightly
unstable

C-D

3

3.5

D
NeutralE
Slightlystable45FStable6G
Verystable7

Fig. 25 shows the example of C*n at station No.104 for NE wind direction at each hours. 5
years averaged value of C*n,ave and ± 1ζ (standard deviation) are plotted in the figure. The verticaldotted lines correspond to 00:00 (midnight). The daily variation of C*n,ave in hour is regular from
Mondays to Sundays. C*n,ave increases from the minimum value at 3am to the maximum value at
9:00, because 9:00 is the rush hour.
80

C*n(ppbm/s)

60
40
20
0
-20
0 (Sun) 24 (Mon) 48 (Tue) 72 (Wed) 96 (Thu) 120 (Fri) 144 (Sat) 168

Time (Hour) (Station No.104. H = 4m)

Figure 25

Cn in NE wind direction (station No.104)

10268695279611945366462

-1

1

8098

2
3
4
5
6
Atmospheric stability

7

6

1

61

297

2
1
0
122

-1

1

327

54

2089

185

2
3
4
5
6
Atmospheric stability(d) No. 138 station ( N )

7

1213

2
1
0
1022 2112 483 14765

-1

1

329

2
3
4
5
6
Atmospheric stability

7

n,m

208

3

2301 105

73

200

1138

2
1
0
1094 2059 473 14453

-1

1

314

2
3
4
5
6
Atmospheric stability

7(c) No.138 station (all wind directions)

Stability effect ratio C*/C*n,mStability effect ratio C*/C*

359

73

(b) No. 104 station (all wind directions)(a) all data
32253 108

Stability effect ratio C*/C*

2

3

n,m

29318

3

194

1671

2
1
0
80

-1

1

228

92

2
3
4
5
6
Atmospheric stability7(e) No. 138 station ( NE )Figure 26

Stability effect ratio C*/C*

5206

n,m

58116 2665 1843

Stability effect ratio C*/C*

3n,mStability effect ratio C*/C*n,m

We attempted to integrate all the monitoring stations data to find a general relationship between
atmospheric stability and NOx concentration. The index “Stability Effect Ratio” was proposed forthis purpose. It is the ratio between C* and C*n,ave at each hour, each day and each wind direction
for each station. All averaged values of the “Stability Effect Ratio ± 1ζ” (all stations, days, hours,
wind directions, and wind speed ranges) are plotted against stability classes in Fig. 26a. The numbers in horizontal axis express the atmospheric stability classes shown in table 4. Data numbers ofeach class are written in the figure. The stability effect ratio is less than 1 under unstable atmospheric conditions, nearly 1 under neutral atmospheric conditions and larger than 1 under stable
atmospheric conditions. However the standard deviations are very large probably due to the variability (uncertainty) of atmospheric condition and pollutant emission rate. In order to check
whether the “Stability Effect Ratio” in Fig. 26a is independent of urban shape and location, two
additional figures are added: values of the “Stability Effect Ratio ± 1ζ” (all days, hours, wind directions, and wind speed ranges) at Station No. 104 (in Shinjuku-ku) and station No. 138 (inEdogawa-ku) were displayed in Fig. 26b and c. The tendencies of Fig. 26b-c are similar to Fig.
26a. Not only these two stations but also all the stations have the similar tendency (figures areomitted). Thus, there is a possibility that the “Stability Effect Ratio” is independent of urban shapeand location. In addition, in order to check whether the “Stability Effect Ratio” in Fig. 26a is independent of wind direction, three additional figures are added (Fig. 26d-f). Values of the “stability effect ratio ± 1ζ” at Station No. 138 (in Edogawa-ku) under N, NE and S wind directions
were shown in figures 26d-f respectively. Some differences were found. However data number issmall. So we cannot judge whether the atmospheric stability ratio is independent of wind direction
from these data.

3

166

47

12

19

60

2
1
0
-1

48

251

138 1705

20

1

2
3
4
5
6
Atmospheric stability

7(f) No. 138 station ( S )Stability effect ratio

As mentioned above, it was found there is a possibility that the Stability Effect Ratio was independent of wind speed, urban shape and location, and wind direction. But the deviation of theratio was large probably due to variability (uncertainty) of atmospheric condition and pollutant
emission rate. Thus in order to reduce the variability (uncertainty) of atmospheric condition andpollutant emission rate, wind tunnel experiments and CFD simulations were conducted under nonneutral (weakly unstable, unstable, weakly stable and stable) and neural conditions.
The experiments were conducted in a thermally stratified wind tunnel at Tokyo Polytechnic
University (TPU). Fig. 27 shows the experimental setup. The unstable and stable turbulent boundary layers were generated by 26 very thin roughness elements which were made of aluminum angles in the upstream. They create a long, rough upwind fetch to generate a turbulent boundary
layer.
1200

500 5

00

alum

inum

angle

4400

23× @
200

cooli
ng pa
nels
1000

x

Heati
ng an
d

street1840

Figure 27

canyo
n mo
del
460
1670

00

z

9370

Temp
e
profi raturele car
t
y

12

Wind

Experimental setup (units: mm)

Total 9×14=126 cubic blocks were put in an unstable and a stable turbulent boundary layer ina downstream (Fig. 28) to represent building blocks. Each building block has the same configuration: L (60mm) × W (60mm) × D (60mm). The city blocks were spaced 60mm apart in the x direction and 60mm apart in the Y direction. The overview of the building block arrangement was
shown in Fig. 28. Tracer gas ethylene (C2H4) was released from a plane of the floor. Fig. 29
shows measuring points. The locations of the measuring points were selected so that various flow
patterns (reverse flow, upward flow, downward flow in the street canyons and flow on the roads)
were included.
37mm

Gas emission plane

W
W

W
W

W
W=60mm
300mm

1620mmFigure 28

980mm

Arrangement of building blocks

Y=120
60

z=50
30
10
4

0
ガス線源 X=90

-60

30

90

210

-120
X=-90

-30 3090210：測定領域 (a) ：測定点（高さ方向４点）
top view(b) side view

Figure 29：ガス線源

Measuring points

Table 5 summarizes the atmospheric stability conditions. Totally 5 cases were conducted. The
reference height was 0.32m, and the velocity at this height of inflow boundary was set as reference velocity (UR). The atmospheric stability was characterized by Bulk Richardson‟s number(Rib). Bulk Richardson number can be expressed as the following.
gH R  (TR  Ts)Rib (T0  273)  U R 2
Where g is the acceleration due to gravity (m/s2); HR is the reference height (m); TR is the temperature at reference height (oC); TS is the ground surface temperature (oC); T0 is the average inflow temperature (oC); UR is the velocity at reference height (m/s). Rib for 5 inflow profiles weresummarized in the last row of Table 5. Values of Rib ranged from -0.23 (unstable) to 0.29 (stable).
Table 5

Atmospheric stability conditions (HR =0.32m)

Unstable

Weaklyunstable

Neutral

WeaklystableStable

UR

(m/s)

1.4

1.8

1.8

1.5

1.2

TR

(°C)

9

10

11

48

55

TS

(°C)

49

41

11

16

14

40

31

0

32

41

13

14

11

48

55

-0.23

-0.1

0

0.13

0.29

ΔT
T0
Rib

(°C)
(°C)

CFD simulations with the standard k-ε model were also conducted. Fig. 30 shows the grid arrangement.

Figure 30

Grid arrangement for CFD simulation

All the measured concentration data (measurement positions see Fig. 29) in the wind tunnel tests
were included for investigating the effect of atmospheric stability on gas concentration. There
were totally 92 measuring points for each case. As shown in Figure 29, those measuring positions
located in different places. Some were in the street while some were in the canyon between
blocks. Moreover, four heights in vertical direction were included. Concentration values at themeasuring points calculated by CFD simulations were also investigated.
The correlations for normalized non-dimensional concentration C* between neutral conditionand non-neutral conditions were investigated and shown in Fig. 31and 32 As shown in the figures,
data were plotted almost on a single straight line for both experiment and CFD simulation. Thus
the ratio between C* under non-neutral conditions and neutral condition were independent of themeasurement locations (the flow filed around the measuring positions). C* under unstable condition were smaller than C* under neutral condition, and C* under stable condition were larger thanC* under neutral condition.
40

Unstable (C ) (Rib=-0.1)

Unstable (C ) (Rib=-0.1)

40

*

30
y=0.7042
R =0.896

*

20
10
0

30
20
10
0

0

10

20

30

40

y=0.841x2
R =0.996

0

10Neutral (C )(b) CFD (Rib= -0.1)
Unstable (C ) (Rib=-0.23)30
y=0.611x2
R =0.848

20

*

*

40

40

40

Unstable (C ) (Rib=-0.23)30*(a) Experiment (Rib= -0.1)

10
0

20
Neutral (C )

*

0

10

20
30
*
Neutral (C )

30
20
10
0

40

(c) Experiment (Rib= -0.23)

y=0.676x2
R =0.943

0

10

20
30
*
Neutral (C )(d) CFD (Rib= -0.23)*

Figure 31 Correlations of C (Neutral-Unstable)

40

Stable (C ) (Rib=0.13)

40

30

30
20

*

20

*

Stable (C ) (Rib=0.13)40

y=1.315x2
R =0.955

10
0

0

0

10

20

30

y=1.177x2
R =0.978

10

40

0

10

Neutral (C )40

Stable (C ) (Rib=0.29)

30
20

*

*

40

(b) CFD (Rib=0.13)40

Stable (C ) (Rib=0.29)30*(a) Experiment (Rib=0.13)

10
0

20
Neutral (C )*y=1.378x2
R =0.883
0

10

20

30
20
10
0

30

40

y=1.19x2
R =0.931
0

10

20

30

40

*

*

Neutral (C )Neutral (C )(c) Experiment (Rib=0.29)(d) CFD (Rib=0.29)*

Figure 32 Correlations of C (Neutral-Stable)
Fig. 33 shows the ratio between C* under un-neutral condition and C*n under neutral condition
obtained from experiment and CFD results. This is the Stability Effect Ratio (SER) on pollutant
concentration (SER= C*/ C*n). Averaged SER ± σ (the standard deviation) of all the measuring
points are plotted in the figures. As shown in the figures, with the increase of Rib, the SER increases. Since the standard deviation is relatively small, the SER is almost independent of the locations or flow fields. But we need further case studies to confirm the generality of the SER. If
the function of the SER is universal one, we can predict pollutant concentration under un-neutral
condition from experimental or CFD results under neutral condition by using the function. It willbring about a drastic change in the environmental impact assessment of air pollution in Japan.
2.0

2.0

1.6

1.6

C* 1.2C*n 0.8

C* 1.2C*n 0.8

0.4

0.4

0.0
-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.0
-0.3

-0.2

-0.1

0.0

0.1

0.2

Rib

Rib
(a) From experimental results(b) From CFD results*

Figure 33 Stability Effect Ratio (SER) = Ratio between C under un-neutral condition
and C*n under neutral condition

0.3

0.4

2.5 Large Eddy Simulation of gas/thermal dispersion in non-isothermal boundary layerUΘd wind tunnel

thermally stratifie

Aluminum plate

Inlet

1m

Urban heat island phenomena and air pollution become serious problems in weak wind regions
such as behind buildings and within street canyons, where buoyancy effect cannot be neglected. Inorder to apply CFD techniques to estimation of ventilation and thermal/pollutant dispersion in urban areas, it is important to assess the performance of turbulence models adopted to simulate these phenomena. When simulating the turbulent atmospheric boundary layers using Large Eddy
Simulation, a crucial issue is how to impose physically correct fluctuating inflow data. In a nonisothermal field, not only inflow velocity fluctuation but also temperature fluctuation is necessary.
In this research, two inflow turbulence generation techniques (precursor method and recycling
method) were investigated first. Then the generated turbulent inflow data were used for large eddy simulation of gas/thermal dispersion behind a high-rise building and within a street canyon in
an unstable turbulent boundary layer
Firstly, we introduced a precursor method. In our wind tunnel experiment, the unstable turbulent boundary layer was generated by 26 very thin aluminum plates. In this precursor simulation, the whole wind tunnel (6.5 meters long) and all the aluminum plates (shown in Fig. 34) werereproduced by LES using a buoyant solver. The plates were treated as having zero thickness inthe simulation. The wind velocity and temperature distribution at the inlet of the wind tunnel were
spatially uniform and turbulent intensity was very small (less than 1%), so a uniform velocity
U=1.43 m/s and a uniform temperature Θ=9.4℃ without turbulence were given to the inflow
boundary of the pre-simulation. A zero gradient condition was used for the outlet boundary condition. A no-slip boundary condition was applied to the wall shear stress on the floor. As thermalboundary conditions, the surface temperature was 45.3℃ and a heat conduction boundary condition (Fourier‟s Law) was applied for the heat flux on the floor surface. The total mesh number
used in the pre-simulation was 1042×120×79 = 9,878,160. A unique time step ⊿t=0.001sec wasused to make sure that the maximum courant number was less than 1 in all positions of the domain. The sampling plane to obtain fluctuating velocity and temperature data was set at 0.1m (11times the aluminum plate height) downstream of the last aluminum plate.

Sampling Plane

Outlet

1.2 m

X2
X3

0.5m 0.2m

Figure 34

6.5m

Top View

X1

0.1m 0.9m

Inflow turbulence generation method (1) : precursor simulation

Another method for generating inflow turbulence in a non-isothermal boundary layer using a recycling procedure was also investigated here (Fig.35). Tamura et al. (2003) proposed a method for
dealing with the thermally stratified effect. In the driver region, velocity fluctuation was generatedusing Lund‟s method (Lund et al., 1998) for a rough wall, while temperature was treated as a passive scalar, and a mean temperature profile was given to the inflow condition of the driver region.
The same concept was adopted here, but the velocity fluctuation was generated using Kataoka‟smethod (Kataoka and Mizuno, 2002) with the roughness ground arrangement described by
Nozawa and Tamura (2002). The roughness elements were exactly the same as those used in thewind tunnel experiment, but a short domain was adopted here. A mean velocity profile that camefrom the experimental measurement was prescribed for the inflow condition, and only the fluctuating part was recycled between outlet station and inlet station. The following damping function(Kataoka, 2008) was used to restrain development of the velocity fluctuation:1
8.0(  1.0)
 ( )  {1  tanh[] / tanh(8.0)}(1)2
 0.4  0.82
where, η = z/δ. δ is the boundary layer thickness (0.25m). Only a neutral boundary layer (NBL)
was simulated in the driver region, the temperature was treated as a passive scalar, a mean temperature profile of the experiment was prescribed at the inflow boundary of the driver section, andwe tried to use the fluctuating velocity field to generate a fluctuating temperature field. The computational arrangement for the recycling method is shown in Fig. 1(b).
Recycling fluctuating part of velocity

Inlet

<u1>

Buoyancy effectOutletSampling Planeuθ

Driver region

Figure 35

Main region

Inflow turbulence generation method (2) : Recycling method

Fig.34 shows the inflow characteristics generated by both the precursor method and the recycling
method in the sampling position. The mean wind velocity, mean temperature, turbulent kinetic energy, and the r.m.s. value of temperature fluctuation agreed well with those of the experiment.
Both methods can be used to generate turbulent inflow data for LES in a non-isothermal boundary layer.
2.0Exp
Precursor
Recycling

2.0
Exp
Precursor
Recycling

1.5

X3/H

1.5

X3/H

X3/H

1.5

2.0
Exp
Precursor
Recycling

1.0

1.0

1.0

1.0

0.5

0.5

0.5

0.5

0.0
0.0

0.5

1.0

1.5 0.0-1.5<u1>/UHa. Mean stream-wise velocity

Fig. 36-1.0-0.5(<θ >-Θf)/ΔΘ

b. Mean temperature

0.0

0.0
0.00

0.01

0.02

Exp
Precursor
Recycling

1.5

X3/H

2.0

0.03 0.0
0.00

0.05

2

k/UH

c. Turbulent kinetic energy

0.10

0.15

ζ θ /ΔΘ

d. The r.m.s. value oftemperature fluctuation

Inflow characteristics generated by precursor and recycling methods

The generated inflow turbulence data were saved in a Hard disk and used for an inflow boundary
condition of Large Eddy Simulation for gas/thermal dispersion behind a single building (Fig. 37)
and within street canyons (Fig. 38). Wind tunnel experiments were also conducted in the thermallystratified wind tunnel of TPU. The floor surface temperature and the flow temperature were about
45℃ and 10℃ respectively to generate unstable turbulent boundary layer for both cases.
Wind velocity at theheight of building, UH
建物高さ風速：UH

Inflow turbulence
Data base

H=0.16m

ガス排出口(φ＝5mm)Gas dischargehole(φ ＝5mm）

X3 X

2

C 2 H4
5％

X1

Figure 37

Gas/thermal dispersion
behind a single building.

Figure 38

Gas/thermal dispersion
within street canyons

Firstly, for single building case, mean stream wise velocities <u1> of the experiment and the calculations areshown in Fig. 39. The results from the RANS model (two-equation heat-transfer model (Nagano and Kim 1988))
show overestimation of the recirculation size behind the building, and the calculated downward flow in the region around X1/H= 0.7-1.5 is weaker than the experimental one. The calculated reverse flow near the ground andthe rising flow along the rear surface of the building are stronger than those of the experiment. On the other
hand, the recirculation size by the LES is narrower and the reverse flow near the ground and the rising flow
along the rear surface of the building are weaker than those of the RANS model, which is closer to the experimental results.

-0.5

0

0.5
X1/H

1.5

1.0

(a) Experiment
Frame 001  09 Feb 2009  Non-isothermal Square

0.8
0.6
0.4
0.2

0.5
0

-1.0

1.25

1.0

Figure 39

1.0

1.0
X3/H

1.0

0.5

-0.1

-0.1

0

0

0.2

0.5

1.25

1.0
0.8
0.6
0.4

-0. 0
-0.2 1

-1.0

-0.5

0

0.5
X1/H

1.5

1.0

0

-1.0

-0.5

0

0

0.8
0.6
4
0. 2
0.

X3/H

1.0

X3/H

1.25

0.5
X1/H

1.0

1.5

(b) RANS (TEHT model)(c) LES
Vertical distribution of mean velocity <u1>/UH

Fig. 40 illustrates distributions of mean temperature. Although the vertical distribution of mean
temperature far behind the building by the RANS model is similar to that of the experiment, thecalculated temperature along the rear surface of the building is higher and the contour lines are
predominantly vertical. This is because the strong rising flow along the rear surface of the building
0.3
transported the hot air near the ground up along the rear surface. The
vertical distribution behindthe building in LES is similar to the experimental one.
0.25
1.5

0.5

-1

0
-0.5
-0.5

(a) Experiment

Figure 40
T:

1 1.0

0.50.5
Z

0
-0.5
-0.1

0

Y

01
-1
1.5
1.5 X

00
X

0.50.1
1.0 0.2 1.5
X1/H

1.25
0.2
1.00.15-0.975
-0.95
-0.9
25
-0.9
-0.87
-0.85 5-0.825-0.625Z-1-0.975-0.925-0.9-0.6-0.875-0.8
5-0.825
-0.8

X3/H

1.25

X3/H
z/H

X3/H

1.0

-0
-0.95 .975
-0.9
-0. 25
9
-0.8
75
-0.85
-0.825-0.625
00
0.5
1.0
0.5
1
X1/H
x/H

y/H

1.25

0.1

0.5
0.05

00
-0.5

0

0.5
X1/H

(b) RANS (TEHT model)(c) LESND_TEM:
Vertical distribution of mean temperature (<θ>－Θf )/ΔΘ-1

-1 -0.975 -0.95 -0.925 -0.9 -0.875 -0.85 -0.825 -0.8 -0.775 -0.75 -0.725 -0.7 -0.675 -0.65 -0.625 -0.6

-0.975 -0.95 -0.925-0.9-0.875 -0.85 -0.825-0.8-0.775 -0.75 -0.725-0.7-0.675 -0.65 -0.625

-0.6

1.0

1.5

Fig. 41 and Fig. 42 show the distributions of mean gas concentration. In the RANS calculation result, the high concentration area near the ground does not spread downwind of the gas emission
point (marked as a „black triangle‟). Like the temperature distributions shown previously, calculated gas concentration along the rear surface of the building was higher due to the rising flowfrom the ground. The calculation does not reproduce the periodic fluctuations due to vortex
shedding, and as a result, dispersion in the X2 direction (lateral direction) is inhibited (Fig. 42).
0.3
For LES, the distribution pattern is much closer to that of the experimentthan of the RANS model, especially regarding the lateral width of gas dispersion.
0.25

Frame 001  02 Feb 2009  Non-isothermal Square

1.5

1.25

20 10 540
80 320160 640

00

0.5
0.5
x/H

0.5 0.5

80
160

Z0.4

0

X1/H

1.0
1

01
-1
1.5
1.5

Y

X

2.5

5

0
-0.5

y/H

0
-0.5
-0.5

Frame 001  04 Feb 2009  Non-isothermal Square

1.0

z/H
X3/H

2.5

0.5

-1

1

1.0 0.15

1

10
20
40

X3/H

1

X3/H

1.0

1.250.2

0.3

1
0.1

0.5

320
640
0.5 0.1 1.0
X1/H

00

-0.1

Z

1.25

0.05

0.2

1.5

00
-0.5

X

(a) Experiment

10
20
40
80
160
0

2.5
5

320
640
0.5

1.5

1.0
X1/H

(b) RANS (TEHT model)

2.5

5

10

20

40

80

160

320 0.2 640

1.0

X2/H

0.5
2
160 40 0

0
-0.5

0

1

1.0

0.1
0.5

0.5

1.0

20 10

X2/H

1
2.5
5

10
640
1.5

0.5 1.0
X1/H

00

5

2.5

1

640

0

-0.5
-0.1

00
X

0.50.1 1.0
X1/H

1
2.5

0.5
X2/H

1

y/H

C:

Y

(c) LES
1
2.5
5
10
20
80
160
320
640
FigureND_C01:
41 Vertical
distribution
of40meanconcentration<c>/C00-0.5

0.2 1.5

10 5
40 20
640
160
0
0.5
1.0
X1/H

1.5

-0.5

(a) Experiment

5
10
20
40
80
160
320
(b) RANS(TEHT
model)(c) LES
Figure 42 -1 Horizontal distribution of mean concentration <c>/C0
01

-1

-0.5

0

0.5

1

1.5

1

2.5

640

z/H

ND_C01:

x/H

C:

1

2.5

5

10

20

40

80

160

320

640

Secondary, street canyon model: Measuring points are illustrated in Fig. 43. Fig. 44 shows the
distributions of mean stream-wise velocity, mean temperature, and mean concentration near thefloor (z=H/6, H is building height). The LES results agreed well with the experimental data especially for the mean concentration. RANS model (with standard k-εmodel) overestimate the concentration by about 200%. This is because the intermittent air exchanging between street canyon
and upper atmosphere cannot be reproduced in RANS, while it is well captured by LES. Fig. 45
shows the correlations of mean concentration between experiment and LES results. Around 100
points shown in Fig. 43) were used to obtain these correlations. The LES results were very close
to the experimental data.

Z Flow

180

150
110
100
90
70
40 5030
0 410

120

0

X

30

60

-30
-60

-120
-180

0
150
270
390
-150 -60210
330-210 -90-30 30 90-120(a) Horizontal plain(b) Vertical plain
Figure 43 Measuring points

RANS0-1 -1500-60 30
1 90 150210270330390 -1-210-90-30<u>/UH
3
-120

3

0

LES
Exp
X/H
1(<θ>-Θ )/ΔΘ
3
2

1

1

1

0

-1

-1

-1

-2

-2

-2

Y/H

0

Y/H

2

Y/H

2

-3

-3

ガス線源

-2.5

-0.5

X/H

1.5

3.5

(a) Mean stream-wise velocity

Figure 44

-0.5

X/H

1.5

3.5

0.5
<c>/c0

0

-3

ガス線源

-2.5

0

f

ガス線源

-2.5

-0.5

X/H

1.5

3.5

(b) Mean temperature(c) Mean concentration
Comparison between experimental result and CFD results (z=H/6)

LES: <c>/C0

10

0.1

0.001
0.001

0.1
EXP: <c>/C0

10

Figure 45 Correlation between measured concentration and calculated concentration by LES

3 CONCLUDING REMARKS AND FUTURE PROSPECTS
In this manuscript, some of the research results obtained by the Project 3 : wind environment andair pollution group were introduced. Five of the most important study areas, namely: ①
Reproduction of occurrence frequencies and vertical profiles of wind velocity and temperature by
meso-scale simulation, ②Generalization of Heat transfer from urban canopy surfaces, ③Effectsof building arrangement on urban ventilation and their generalization, ④Effect of atmosphericstability on urban pollutant concentration and its generalization, ⑤Large Eddy Simulation ofgas/thermal dispersion in non-isothermal boundary layer, were outlined and presented. These
study areas are taking very important role in the framework of the entire research of Project 3 asshown in the Fig 46. Unfortunately, we have not achieved the final goal yet. But fortunately thegoal and the strategy are quite clear, and the research progresses that move toward the final goal
have been steadily built. The research items that we made remarkable progress are shaded in theFig. 46. We will continue our efforts to achieve the final goal in several years.
Proposal of guidelines
for urban development

Revolution of environmental impact
assessment system for air pollution
Environmental impact
assessment system forpedestrian windenvironment

Generalization:effect of
atmosphericstability on
Urban pollutant
concentration

Preparation ofstandard wind data

Preparation of standard
atmospheric stability data

Reproduction of occurrence
frequencies and vertical profiles ofwind and temperature

Generalization: effect ofbuilding arrangement on
urban ventilation performance

Development ofexperimental techniquefor pollutant/thermal
dispersion

Analyses of heat island
Phenomena by WRF

Coupling Urban Canopy
Model with WRF

Modification of
Urban Canopy Model

Validation
Development of
numerical technique forpollutant/thermal dispersion

Generalization:
convective heat Transferfrom urban canopy

Preparation of representative
Inflow profile ofwind and temperature

CFD analyses of Urban
heat island phenomena

Figure 46 framework of the entire research of Project 3
If I am asked what the best thing was in the Global COE program, I would say that it is thehuman resources. I am very happy to work with various excellent postdoctoral researchers, PhD
students and master students not only from Japan but also from abroad. I could never haveachieved the results only by myself. All I did were just to give them clear and aggressive targets,and to advise them, give some ideas sometimes. It is my great pleasure that they left TPU to become company/university researchers after gaining remarkable knowledge and skill during theirstay in TPU though they were unconfident when they started as a member of the project.
We have to secure excellent human resources and maintain our research activities at the Wind
Engineering Research Center of TPU. In order to execute this, the following factors are very important;・Securing research funds

・Improving the quality of undergraduate students for graduate school by reforming education
・Taking excellent graduate students from locally and internationally.
・Continuing to publish first-class research results in world-class journals.
The above factors are mutually related and we cannot say which is the most important (whichcomes first the chicken or the egg?). Thus we have to carry them out comprehensively. This is theprimary responsibility of our generation who follow the great leader, Professor Yukio Tamura.
Finally, I would like to quote few words from Prof. Hajime Okamura, a Professor Emeritus at
the University of Tokyo (The former president of Japan Society of Civil Engineers. When he wasan under graduate student, as a pitcher of the baseball team of the University of Tokyo, he won
17 games in his career in the TOKYO BIG6 BASEBALL LEAGUE. It is the best record in the
100-year history of the baseball team of the University of Tokyo.)“I found something from this experiment.”→ Even junior high-school students can do that.
・Make hypothesis from a preliminary experiment and create a model or a theory, then carry out
experiments to confirm.
・Generalize or universalize research results. Otherwise, they cannot be used for engineering.
These quotes are what he said to us 15 years ago. I was strongly impressed by the words and I
have been keeping them in my mind to carry out researches. I also have been saying the samewords to my students. These quotes are truly strong foundation of my life as a researcher and for
my students as well.
ACKNOWLEDGEMENT
I would like to express my sincere gratitude to post-doctoral researchers, PhD students, and master students in Project 3, for their great efforts to achieve our research objectives. Without theireffort, such excellent achievements have never been obtained.
The members in project 3 were as follows.
Hideyuki Tanaka (Takenaka Corporation, former 21 Century COE researcher)
Taich Shirasawa (Otsuma Women‟s University, former researcher of Academic Frontier)Jaeyong Chung (TE Solution, former Global COE researcher)
Jianying Jiao (Wyoming University, former Global COE researcher)
Sivaraja Subramania Pillai (Sri venkateswara College of Engineering, former PhD student)
Guoyi Jiang (former PhD student)
Tingting Hu (PhD student)
Fukurato Yamaguch (PhD student)
Tsuyoshi Kobayashi (Azbil, former master student)
Koudai Katada (Maeda Corporation, former master student)
Masanori Mochizuki (Away architectural evaluation net, former master student)
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