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BSE Public CPD Lecture – Heat Island Effect on 22 November 2010 Organized by the Department of Building Services Engineering (BSE), a CPD lecture on Heat Island Effect was conducted by Prof. Ryozo Ooka on Monday, 22 November 2010. The lecture was well-received with an attendance of 145 participants.
Powerpoint file of the CPD lecture Professor Ooka received his Bachelor and Master degree from University of Kyoto and his PhD from University of Tokyo, Japan. He is a committee member of various professional institutions, such as Architecture Institute of Japan, The Japan Society of Fluid Mechanics, Meteorological Society of Japan and Japan Society for Wind Engineering. His research interests encompass thermal comfort, air pollution, CFD simulation and environmental engineering.
CPD lecture by Prof. Ooka
Souvenir presentation
“Heat Island Effect” has long been an important topic in modern city design and it is believed to be a main contributor to the elevated temperature in metropolis. Building engineers are increasingly aware of the issue. Current analysis mainly focuses on heat transfer theories coupled with CFD simulations and evaluation of effectiveness on different simulation models.
In the lecture, Prof. Ooka illustrated on mitigating Urban Heat Island by using numerical simulation and the results were applied to an actual street block in central Tokyo (Marunouchi District). Results of the case study demonstrated that covering roadways with water-permeable materials, and planting grass and trees on-site could reduce air temperature and MRT, whereas covering roadways with highly reflective paint would increase MRT and SET and would lead to environmental degradation.
Full house
Presentation by Prof. Ooka
Good discussion on the topic carried on in the question and answer session. Participants showed strong interests in the topic and actively asked questions.
Q&A Session
Asking questions
BSE News CPD 20101122
1
DEVELOPMENT OF ASSESSMENT TOOLS FOR
URBAN CLIMATE AND HEAT ISLAND MITIGATION
Ryozo OokaInstitue of Industrial Science,
The University of Tokyo, Japan
2010 Seminar at Hon Kong Polytechnic University,22 November 2010
2
BackgroundIn recent years, problems concerning the urban climate including the urban heat island effect and urban air pollution – have attracted much attention with the widespread urbanization throughout Japan.
Tem
pera
ture
[℃]
(ave
rage
in a
yea
r)
Year( by Japan Meteorological Agency)
•Human Unpleasantness
•Electric Demand for Cooling Load
•Heat Disorder
•Ecological Change
3
The surface temperature from a high-rise building in Shinjuku Area (12:00~13:00 on July 22, 2004)
East Area
North Area
The surface temperature seems over 50˚C in average.
4
Countermeasures for Urban Heat Island Mitigation
There are various kinds of countermeasures.
Purpose Large Category Small Category
Cool Surface Use of Green Maintenance of Green Land Garden Roof Greening/Garden Street Greening/Trees Use of Structural Water Permeable Material Material Water Contained Material High Albedo Painting Photocatalyst Use of Water Water Park/Waterfront Water Sprinkler Creation of Arcade Shading Area Pergola Promotion of City Block Ventilation Lane Urban Ventilation Configuration Arrangement of Buildings Building Minimization of Aspect Area Configuration Pilloti Reduction of Energy-Saving Energy-Saving Machinery Anthropogenic Heat Transport Manegement Energy-Saving Life Style Heat Release Water Cooling Tower Treatment Heat Sink（River, Sea, Ground）
5
Examples of Countermeasures for Surface Cooling
Large Green Park
Building Greening Green Parking
High Albedo Painting (Prof. Y. Kondo) Water Contained Material
Water Contained Ceramic
Ordinary Ceramic
Water Sprinkler on A Road
Waterfront Space
6
Sea Breeze
Examples of Countermeasures for Shading
Road Tree Pergola Arcade
Ventilation LanePilotiOptimum Arrangement
of Buildings (Narita)
Cool Wind in Summer
BuildingBuilding
Examples of Countermeasures for Ventilations
7
Examples of Countermeasures for Reduction of Anthropogenic Heat
Energy Saving
The Top Runner ProgramEco Life Style
COP=3.0 COP=6.05km/litter 15km/litter
Air Conditioning
Lighting
Entertainment
Kitchen
Bath & Toilet
Cleaning
CarTreatment of Anthropogenic Heat Release
Use of Heat Sink
Water-chilling Cooling Tower
8
Image of various counter measures for heat island
Figure 8: Conceptual diagram of the measures on heat-island alleviation
Emitting heat-waste from high place
Using high albedo material with external walls
Accelerating wind ventilation
Creating open spaces
Creating void spaces
Planting on the ground
Transpiration effect
9
Roles of Assessment Tools
Although many countermeasures for urban heat Island mitigations are proposed, the effects of these measures have not been clarified enough.
It is expected that the assessment tools can evaluate these effects before these measures are applied actually.
10
Structure of Assessment Tools
InputVarious Scenarios and
Countermeasures for
Urban Heat Island
Mitigations
SolverPhysical Model
Social Model(Convection, Radiation, Physiology etc)
(Economic, Psychology etc)
DatabaseGIS data
• topogｒaphy
• land-coverage
• land-use etc
Meteorological data
• Wind
• Pressure
• Temperature etc
Urban Configuration
• Building Conf.
• Floor Area Ratio
• Material etc
Human Activity
• Energy Consumption
• Transportation
• Life pattern etc
Output
Effect on
Climatic Change
Human Sensation
Social activity
(Temperature decrease etc)
(SET*, PET etc)
(Energy consumption etc)
11
SolverPhysical Model
Climate Model
Meteorological Model
Meso-scale Meteorological Model
Urban Canopy model
Micro Climate Model
Human Physiological Model
Building Energy Model
Ecology Model
Global Climate Model
Hydrology Model
・・・etc
Epidemiology ModelEconomical Model Human Lifestyle ModelSocial Model
12
Various Scales of Physical Models
geostrophic wind
suburban area
sea
suburban area urban area
heig
ht (
m)
0
200
300
100
mountain
sea-breeze
⑤ Urban scale (10km～100km)
② Room scale (3m～10m)
outlet
heat island circulation
④ City block scale (～1km)
heat island circulation
artificialheat release
artificial heat release
③ Building scale (30m～50m)
crossventilation
latent heat
convection
artificialheat release artificial
heat release
solar radiation ① Human scale (～1m)
solar radiation
inlet
convection
radiation evaporation
respiration
Meso-scale Meteorological Model
Micro Climate Model
Building Model
Human Sensation Model
13
Meso-scale Meteorological ModelsTable 1 Comparison of various meso-scale meteorolosical models for urban climate and heat island study
NAMEDeveloper Equation Turbulence Model Surface sublayer Main User for Hear Island Study
LSM[3] Tsukuba-Univ Hydro 0 equation Monin Obukhov F. Kimura, H. Kusaka
AIST-MM [4] AIST*1 Hydro 0 equationGambo’s model[19]
Monin ObukhovAIST-CM
H. Kondo, Y. Genchi, Kikegawa
SoftwarePlatform[5]
NEDO*2 Hydro k-l two equationMellor and Yamada[12]
Monin Obukhovurban canopy
S. Murakami, A. Mochida,R. Ooka
UCSS[6,7,8,9]
BRI*3 Non-hydro k-ε two equationVuThanh Ca’s [7,8]
urban canopy Y. Ashie
OASIS[10,11]
Osaka-Univ. Non-hydro optional
k-l two equationMellor and Yamada[12]
urban canopy D. Narumi, A. Kondo
HOTMAC[12] YSA*4 Non-hydro optional
k-l two equationMellor and Yamada[12]
Monin Obukhovforest canopy
S. Murakami, A. Mochida
CSUMM[13] CSU*5 Hydro 0 equationPielke’s model
Monin Obukhov T. Ichinose, I. Uno
RAMS[14] CSU*5 Non-hydro optional
k-l two equation,LES optional
Monin Obukhov M. Kanda, A. Velazques-LozadaC. Sarrat
MM5[15] PSU*6
and NCARNon-hydro k-l two equation Monin Obukhov A. Kondo, H. Fan
MC2[16,17] EnvironmentCanada
Non-hydro 0 equation Monin Obukhov E. Krayenhoff
WRF[18] NCAR Non-hydro k-l two equation Monin Obukhovurban canopy
H. Kusaka
*1 Advanced Institute of Science and Technology *2 New Energy Development Organization *3 Building Research Institute, Japan*4 Yamada Science and Art Co. *5 Colorado State University *6 Pennsylvania State University
14
Meso-scale Meteorological Model
26
28
30
32
34
36
38
40
℃
32
30
28
Examples of Applications
26
28
30
32
34
36
38
40
℃
36
34
32
28
30
34
Comparison of Ground Surface Temperature(1) Edo era (2) The present
Effects of Various Countermeasures on Air Temperature (August, 13:00, 1.5m height)
(1) High Albedo on Roof (2) Roof Greening (3) 0 Anthropogenic heat
Table 1： Analysis size domains and grid resolution
Size (X[km] x Y[km])
Grid number
resolution(km)
D1 450x540 51x61x23 9
D2 216x261 73x88x23 3
D3 99x120 100x121x23 1
MM5 modelThe Fifth-Generation NCAR / Penn
State Mesoscale ModelCalculating
wind, temperature, pressure, etc.
CMAQ modelThe Community Multi-scale Air Quality modeling
EMISSION DATAPrepared by Central Research Institute of
Electric Power Industry, Tokyo, JapanIncluding
CO2, NOX, VOC, SO2, NH3, etc.
advection chemistry
depositions cloud, precipitation
aerosols
Species O3 NO NO2 ALD FORM ETH OLE TOL XYL ISO PARICs 28 2.0 4.0 1.8 2.2 1.4 1.6 14.4 0.6 0.5 74.3
BCs
N 28 2.0 4.0 1.8 2.2 1.4 1.6 14.4 0.6 0.5 74.3E 25 2.0 4.0 1.8 2.2 1.4 1.6 14.4 0.6 0.5 74.3W 28 2.0 4.0 2.0 2.4 3.2 4.2 11.4 0.7 0.5 82.7
S Default (USA)
Initial andboundary condition
Solar radiation
Analysis area and configuration
●Matsudo-shi
●Saitama-shiUrawa
●Shinjuku
●Kawasaki-shiSai
Nerima● Fuchu●
Hour emission data at 14 JST August 6 (mole/s/grid)
Air quality concentration simulation
MM5: Version 3.7Physics process: NCAR default, FDDA CMAQ: Version 4.6 Physics and chemistry process : EPA (USA) defaultSimulation period: August, 2005
Urban Climate-Air Quality Modeling System
NMHC
NOx
NCAR Met. Data: T, U, P, RH, SST, Topography , Land Use, etc.
FDDA
JCAP
16
• Weak point of meso-scale meteorological model
Meso-scale Meteorological Model
• Advantage of meso-scale meteorological model
It can evaluate the whole spatial structure and the detail of urban heat island.
It cannot evaluate the environment at human activity and pedestrian level.
17
Meso-Scale Model incorporating Urban Canopy model
Urban cool spot Urban cool spot
Fresh and cold air
Fresh air from mountainTurbulent heat
transfer from ground
Solar radiation
Heat generated by from the city center
Energyconsumption
Heat sink
Heat sink under ground
wind(1-a) Resistance Force against Wind
(2-a) Shading Sunshineby Building (1-b) Increase Turbulence
(2-b) Shading Long WaveRadiation by Building (2-c) Absorption of Sunshine by Trees
(2-d) Decrease Absorption of Sunshine on Ground by T
(3-a) Sensible and Latent Heat on Roof and Wall at Building
Domain for Meso-scale model analysis
Urban Canopy model as boundary modelIn order to estimate the environment at pedestrian level and human activity, urban canopy model has been incorporated.
See, A. Martilli, H. Kondo, Y. Ashie, A. Kondo, M. Kanda, H. Kusaka, The authors etc.
18
Micro Climate Models• This models are usually based on the
coupled simulation with CFD and radiation.
[3] Calculation of SET* for evaluating the themal environment based on prediction results
[2] Coupled simulation of convection (CFD) and radiation Radiation
calculation･ Surface temperature･ Convective Heat ･ Latent Heat
CFD simulation for convection Feedback
[1] Input Condition
Input data 2 Geometry of boundary condition ･Building coverage ･Floor area ratio ･Floor height, etc.
Input data 1 Period for analysis ･target data for analysis ･solar radiation ･wind velocity ･humidity, etc.
Input data 3 Boundary conditions of ground surface, building wall ･Albedo ･Soil moisture ･Heat conductivity, etc.
①Wind velocity ②Temperature ③Radiation ④Humidity ⑤Clothing ⑥Metabolism
･MRT ･Operative temperature
assumed
SET* Thermal comfort index
Flow of Micro Climate Simulation
Radiation from ground
Solar Radiation
Water Retentive MaterialWater Space
Rooftop Planting
Latent Heat
Convection Solar
RadiationLatentHeat
Latent Heat
Latent Heat
Radiation from building wall
Artificial Heat
Ventilation
19
CFD within city block area has been developed in the wind engineering field.
Simulation Results of Wind Velocity Vectors aided by Unstructured Mesh System
20
Comparison of Various Turbulence ModelFlow around a Cube
XR XF
Wind Tunnel Experiment
0.7 1.2
LES 0.6 1.4
k-ε model No reverse flow 1.8
Definition of reattachment lengths XR and XF( * XR > 1.0 : flow does not reattach on the roof )
H XF
XR
Reverse flow Reverse flowNo reverse flow
Reattachment point Reattachment point Reattachment point
(1) Wind Tunnel Exp (2) k-ε (3) LES
Conducted by Mochida and Murakami
21
Wind Tunnel Experiment Standard k-ε Model LES
Comparison of turbulent energy k profile around a cubic building
The k-ε model overestimates k values around the frontal corner of the cubic building.
Comparison of Various Turbulence ModelFlow around a Cube
Conducted by Mochida and Murakami
22
CFD within city block area has been developed in the wind
engineering field.Accuracy Computational
load
Low Order RANSsuch as the standard k-ε
△ ◎
High Order RANSRevised k-ε, RSM etc
○ ◎
LES ◎ △
23
Radiation Simulation
Method Directional Averaging Approximation Monte-Carlo
Advantages Low Computational Cost Suitable for Complex Geometry
disadvantages Not Suitable for Complex Geometry High Computational Cost
Method Flux Method Energy Balance Method
Advantages Easy to consider the effect of gas absorption and directivity.
Treat as linear matrix equations
disadvantages Accuracy depends on the direction and number of fluxes.
Not Easy to consider the effect of gas absorption and directivity.
Calculation Method for View Factors
Calculation Method for Radiation Transfer
DTM (Discrete Transfer Method) is the combination of Monte-Carlo and Flux method.
24
Radiation Analysis with Monte Carlo Method
η
θ
1
Radiation Ray
Y
X
Z
0i i i i iS R H C LE+ + + + =
(1) View Factor is calculated with Monte Carlo method
Fig.2 Outdoors Coordinate System
),( ahe
yi
nθ∗
z
o x
Sky Solar
Contiguity building Ground
h: Solar altitudea: Solar direction
Mx+1
Minute side element Sw(I,J,K,L)
Sg(I,J,K)
My
Mz
Mx
ij ij itotalF N N=
Fig.1 Radiation calculation coordinate system
Nitotal=Total Number of Radiation Ray from point i
point i(2) Calculation of surface temperature of building/ground
Energy Balance Model
Si ：Solar radiation( short wave radiation)［W］
Ri ：Long wave radiation(Air radiation is included)［W］
Hi ：Heat transfer by convection［W］
L・Eil ：Heat dissipation by evaporation［W］
Ci ：Heat conduction to underground and building［W］
Si
Ci
Ri↓ Ri↑ Hi L⋅Ei
25
(1) Drag force of the planted tree, (2) Shading effects on solar and long-wave radiations,(3) Transpiration of water vapor from the plant canopy.
Sub-model for Micro Climate Model: Modeling of plant canopy
The plant canopy model developed here includes the following effects:
solar radiation
(d)
longwave radiation
(c)
(a)
(b)
(turbulence)
(penetration)
(a) drag effects of the plant (b) latent heat from the plant canopy (c) shading effect on long-wave radiation (d) shading effect on shortwave radiation
Please see the presentation of Prof. Yoshida in the detail.
26
Applications of Micro Climate Model
wind
S
E
W N
wind
S
E
W N
wind
S
E
W N
(1) Case 1（Grass area ratio : 10%,
without plant canopy）
(2) Case 2（Grass area ratio : 90%,
without plant canopy）
(3) Case 3（Grass area ratio : 10%,
with plant canopy）
S
W
E
N
33.032.0
33.0
31.5
34.0
32.5
31.532.0
34.0
S
W
E
N
32.0
31.0
32.0
31.0
31.5
31.0
32.5
S
W
E
N
28.529.0
27.5
29.0
28.0
27.0
28.0
Air temperature (15:00, 1.5m height)
34
838
38
50 5
(54.0)
(36.5)
(39.1) (36.8)
52
(35.4)
40
(53.0)
(35.6)
(37.7)36
40 30
28
(32.5)(34.4)
(49.7)
(32.9)
26
26 2626
26
26
(24～26)
(26～28)
(24～26)
Surface temperature (15:00, 1.5m height)
sunshine
Analysis on Existing City Block : OtemachiHigh Rise Office District (Building Height is 40~170m)
Analysis Domain
1930m(W)×2720m(L)×600m(H)
379706 Unstructured Grids
解析領域
NW
ES
Results：Surface Temperature (Otemachi)
敷地 敷地
(1) Base Case (2) Roof Greening(Moisture Availability:0.3)
(3) Roof High Albedo(Albedo:0.5 )
(4) Road: Water retainedSite: Greening
(5) Road High Albedo (6) Site Greening
Air Temperature Distributions (1.5m high)Otemachi (high rise office district)
NW
ES
(1) Base Case (2) Without AC Heat Release
(4) Water Retain Road/ Site Greening
（5）Road High Albedo
(3) Roof Greening
（6）With Traffic Heat Release
30
Human Thermal Sensation Model
HumidityTemperature
Wind
Metabolism
Radiation
Clothing
There are many thermal indices for the outdoor thermal environment such as WBGT, SET*, PET, PMV etc.
These indices should be based on the exact Human Physiological Model.
Unsteadiness and Non-uniformity in the outdoor thermal environment should be considered into thermal indices. Heat Balance of Human Body
31
Subject Experiment for Human Thermal Physiology
• Measurement of physiological factors in hot environment – Core Temperature– Skin Temperature– Cumulative Sweat
Evaporation
TimeExp. A: From 29 November to 20 December 2004Exp. B: From 1 to 15 August 2005Exp. C: From 9 to 23 January 2006
Place Ultimate Environment Test Chamber, I.I.S., The Univ. of Tokyo
Subject 17 men and 17 women in healthy condition, aged 18 to 24
Content Measurement of physiological factors
Outline of the experiment
Purpose: Estimation of Accuracy of Two Major Human Thermal Physiology Model
2Node Model in SET* and PHS (Predicted Heat Strain) Model
32
Comparison between Experiment and Computation
State ofCorrespondence
Low metabolic cases High metabolic case
2NM PHS 2NM PHS
Core Temperature Tcr Good Good Good Good
Skin Temperature Tsk Good Poor Poor Poor
Sweat Evaporation Good Poor Poor Good
0
100
200
300
0 5 10 15 20 25 30 35 40 45 50 55 60
Time ［min.］
Sw
eat E
vapo
ratio
n ［g/
m2］
Experiment 2NM PHS
0
100
200
300
0 5 10 15 20 25 30 35 40 45 50 55 60
Time ［min.］
Swea
t Eva
pora
tion ［
g/m
2］
Experiment 2NM PHS
0
100
200
300
0 5 10 15 20 25 30 35 40 45 50 55 60
Time ［min.］
Swea
t Eva
pora
tion ［
g/m
2］
Experiment 2NM PHS
0
100
200
300
0 5 10 15 20 25 30 35 40 45 50 55 60Time ［min.］
Swea
t Eva
pora
tion［
g/m
2］
Experiment 2NM PHS
Comparison of Cumulative Sweat Evaporation
Case 0：standard (35deg.C, 50%, 1.0met) Case 1：high metabolism (35deg.C, 50%, 2.0met)
Case 2：high temp. (40deg.C, 50%, 1.0met) Case 3： high humidity (35deg.C, 70%, 1.0met)
33
Improvement of Sweating Model
Σcr, warm = Tcr – 36.8［-］Σsk, warm = Tsk – 33.7［-］
C][)0.1( °×−+×= crskb TTT αα
][585417.0
7451832.00417737.0 −
++=
blV&α
h]/m[ 2003.6 2, ⋅Σ+= l&warmcrblV
[ ]hmgTm warmskbrsw ⋅⎟⎟
⎠
⎞⎜⎜⎝
⎛ Σ×−×= 2, /
7.10exp)49.36(170
Average body temperature：
Mass ratio of core and skin：
Skin blood flow：
• Sweating model in 2Node Model in SET*
New Model
M： Value of Metabolic rate [ - ]
( ) ⎟⎠⎞
⎜⎝⎛ −
⋅−⋅=7.10
7.33exp49.36170 sk
bsw
TTm { }( )2))1(exp(1))1(5.0exp(31 −−−−−⋅+× MM
34
Improvement of Sweating Model• Comparison of core temperarure Tcr
Case 0： Standard 1.0met (sitting) Case 1： 2.0met (treadmill 0.9 m/s)
Case 4： 3.0met (treadmill 1.4 m/s)
36.0
36.5
37.0
37.5
38.0
0 10 20 30 40 50 60
Time [min]
Tem
pera
ture
[deg
C]
Experiment2NMexist2NMnew
36.0
36.5
37.0
37.5
38.0
0 10 20 30 40 50 60
Time [min]
Tem
pera
ture
[deg
C]
Experiment2NMexist=2NMnew
36.0
36.5
37.0
37.5
38.0
0 10 20 30 40 50 60
Time [min]
Tem
pera
ture
[deg
C]
Experiment2NMexist2NMnew
35
Numerical and Experimental Study on Convective Heat Transfer
around a Human Body in Outdoor
36
(1) Conditions
(2) Cases measured
Experimental thermal manikin
Surface temperature[deg. C]
33.7(const.)
Wind tunnel
Wall temperature[deg. C]
28 0.75
Air temperature[deg. C]
30 0.25
* Turn off the lights during measurement
Outline of wind tunnel test (experience)
CaseWind velocity
[m/s]Turbulence
intensity [%]
A 0.5 11.2
B 1.0 11.6
C 2.0 11.9
±
±
37
Model analyzedComputational
thermal manikin（Number of surface meshes: 5,612）（Number of meshes: 334,515）
(2) Cases analyzed (same as wind tunnel test)
(1) Model analyzed
Inflow
Outflow
Outline of CFD analysis
Case Wind velocity [m/s] Turbulence intensity [%]
A 0.5 11.2
B 1.0 11.6
C 2.0 11.9
38
Turbulence model Cubic non-linear Low-Reynolds number k-ε turbulence modelwith the addition of the revised LK (Launder-Kato) model
Numerical schemes Second-order upwind difference scheme (Quick)
Algorithm SIMPLE algorithm
Inflow boundary
Wind Velocity Uin: 0.5, 1.0, 2.0 m/sTemperature Tin: (results of experience)Turbulence Intensity TI: (results of experience)Turbulence Energy kin = 1.5(Uin× TI/100)2
Ratio of viscous dissipation: εin = Cµkin3/2/0.1 (Cµ=0.09)
Outflow boundary Uout , Tout , kout , εout: free-slip
Computational thermal manikin
Surface temperature Tsk: (results of experience) Velocity, Temperature: no-slip
WallsSurface temperature Twall: 33.7[deg. C] (results of experience) Velocity, Temperature: no-slip
Numerical methods and boundary conditions
39
CFD analysis is sufficiently accurate and effectiveto evaluate convective heat transfer coefficient
Accuracy of CFD analysisComparison of convective heat transfer coefficient between wind tunnel test and CFD analysis
0
10
20
30
40
Con
vect
ive
heat
tran
sfer
coe
ffic
ient[W
/m2K]
Hea
d
Che
st
Bac
k
Pelv
is
Upp
er A
rm
Thi
gh
Ave
rage
Fore
arm
/H
and
Low
er L
eg
/F
oot
Con
vect
ive
heat
tran
sfer
coe
ffic
ient[W
/m2K]
0
10
20
30
40
CFD analysis
wind tunnel test
Hea
d
Che
st
Bac
k
Pelv
is
Upp
er A
rm
Thi
gh
Ave
rage
Fore
arm
/H
and
Low
er L
eg
/F
oot
Wind velocity 0.5m/s (Case A) Wind velocity 1.0m/s (Case B)
40
Wind velocity V [m/s]
0.1 0.5 1.0 2.0 4.0
Turbulence intensityTI [%]
10 2.9 6.6 12.9 21.3 32.3
20 3.0 7.8 15.4 24.9 37.5
40 3.1 12.1 21.2 34.5 55.1
Results of parametric study (contd.)
Mean Convective heat transfer coefficient αc [W/m2K]
Develop a new formula for αc base on these results
41(2) This has enabled us to evaluate both influences of velocity
and turbulence intensity of wind
4.3)(00080.035.00.4 2 +⋅−⋅+= TIVTIVVcα
(1) This has enabled us to evaluate influence of turbulence intensity
Not considering turbulence intensity
in formula
Development of formula for αc in outdoor environment
42
Future Subjects for Assessment Tools• Preparation of Supporting DataValidation data for Climate Model
Field Experiment
Scale Experiment
Wind tunnel Experiment Field Experiment by Rotach et al. (ICUC5)
Scale Experiment by Narita et al. Wind Tunnel Experiment by the autors
43
Future Subjects for Assessment Tools• Preparation of Supporting Data
Tokyo Metropolitan Research Institute for Environmental Problem
Observation Network Preparedness
To understand the spatial and temporal structure of Urban Heat Island
To get the validation data for simulation method
Example of Observation Network
METROS:Cooperative Research of Prof.
Mikami and Tokyo Metropolitan
106 points for temperature and humidity
20 points for wind direction/Velocity, temperature, humidity, solar radiation, precipitation etc
44
Future Subjects for Assessment Tools• Preparation of Supporting Data
Preparation of GIS data
Development of New Measurement Instruments
UV Doppler Lidar (Prof. Kobayashi)Rayleigh temperature Lidar (Prof. Kobayashi)
airborne laser system aided by artificial satellite.Kokusai-Kogyo Co. LTD Distribution of the buildings
height in Tokyo area
Example of DSMMeasurement Accuracy:
±30cm: Horizontal, ±15cm: Vertical
45
Future Subjects for Assessment Tools
Japan Sea N
X
Y
Pacific Oceangrid A
grid C
grid B
(1) Computational domain of mesoscale analysis (grid A-C)
(2) Computational domain of microscale analysis (grid D-F)
400 km
480 km 2km
2km
grid D
grid Egrid F
grid Size of domain (X×Y×Z) Grid number (X×Y×Z) Turbulence models
grid A 480km×400km×9.6km 60×50×49 grid B 96km×96km×9.6km 48×48×49 grid C 32km×32km×9.6km 64×64×49
Mesoscale analysis
[Mellor and Yamada level 2.5]
grid D 2km×2km×500m 32×37×14 grid E 700m×620m×300m 58×73×29 grid F 145m×65m×154m 78×41×28
Microscale analysis
[The modified k-ε model proposed by the authors ]
Merging of Meso-scale and Micro Scale
Sixth Stage Nesting (Mesh size 8km to 2m)
Domain CDomain BDomain A
(1side:500km）
(1side: 100km）
Domain E
Domain D
Domain C（1side:0km）
Domain E（1side:1km
）Windmill
(1side:20km）
Similar Research: T. Yamada
There is still a problem relating merging of time scale between Meso-scale and Micro Scale.
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Future Subjects for Assessment Tools• From Assessment Tools to Design ToolsEasy understanding by Visualization
Surface Temperature of Urban Structures at noon Conducted by Prof. Akira Hoyano
The present Planted Case Changing Configuration
47
Future Subjects for Assessment Tools• From Assessment Tools to Design Tools
48
Future Subjects for Assessment Tools• From Assessment Tools to Design Tools
49
Future Subjects for Assessment Tools• From Assessment Tools to Design Tools• Development of Optimum Design Method aided
by Numerical Simulation and Genetic Algorithm.
The Optimum Design System
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Concluding Remarks• The necessity and importance of assessment
tools are described.• The recent research achievement and future
subjects of the assessment tools are explained.
• Preparation of supporting data is important.• Assessment tools should become design tools
in the next stage.
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Thank you for your attention!
謝謝！