ISSN 1880-8468
Technical Report ofInternational Development Engineering࿖㓙㐿⊒Ꮏቇႎ๔TRIDE-2010- 03

February 16, 2010

Abstracts of Master Theses
Presented in February 2010

Department of International Development Engineering,
Graduate School of Science and Engineering,Tokyo Institute of Technology
http://www.ide.titech.ac.jp/TR

Preface

Master theses of Department of International Development Engineering, Tokyo Institute ofTechnology were presented successfully on August 3, 2009 and February 16, 2010. This
technical report consists of the abstracts of those theses.

Technical Report of International Development Engineering
TRIDE-2010-03
Table of Contents
Corrosion behaviour of stainless steel in concrete exposed to carbonation................................................................................................ George William C. HONG

1Development of a visible light responsive composite photocatalyst for PCE
treatment....................................................................................................................... Go ISHIDA

5

Influence of temperature on sulfate resistance of cementitious material
including blast furnace slag, anhydrous gypsum and lime stone powder............................................................................................................ Takahisa UCHIDA

9

Effects of usage conditions on the energy efficiency of boost switchingregulator.............................................................................................................. Keisuke OTOME

13

Research of cement hydrate, porosity and chloride penetration into blast
furnace cement concrete exposed to marine environment.............................................................................................................. Takashi KOZAKI

17

Experimental analysis and modeling of dual-polarized MIMO channel................................................................................................................Yohei KONISHI

21

Environmental karte as a tool for regional cooperation on town developmentin watershed.......................................................................................................... Kenji KOBAYASHI

25

Mechanisms of urban effects on localized heavy rain in tokyo using numericalsimulation..................................................................................................................Ryo SHIMOJU

29

Study of propagation channel for wireless sensor network in the vicinity ofhuman body...................................................................................................................Yuuki TERAO

33

Utilization of byproducts in the process of biodiesel production from Jatropha............................................................................................................ Tomoki HAYASHI

37

Assessment of users’ profitability and perception of residential photovoltaic
systems under failure risk.............................................................................................................Toshihiro MUKAI

41

A numerical study on turbulence in the urban canopy layer affected by theatmospheric boundary layer..................................................................................................... Yoshimi YAMASHITA

45

Evaluation on durability of the autoclaved cement-based material using -C2S............................................................................................................ Tatsuya YUMOTO

49

Process of absorption oil separation using solvent extraction and distillation.................................................................................................. Yoshihisa YOSHIMURA

53

Future estimation of the aircraft CO2 emissions for flights between Japan,
China and South Korea........................................................................................................................Ying QIAO

57

Numerical analysis of micro-scale cavity flow............................................................................................................................ Xin XIN

61

Applicability of JOHNSON approximation in area adhesion theory.................................................................................................................... KouJen TIEN

65

A robust image matching method to local affine transform by using scale
invariant features.......................................................................................................................... Jiaping LI

69

Geolocation of emergency network radios in a post disaster area.................................................................................................. Mutsawashe GAHADZA73

CORROSION BEHAVIOUR OF STAINLESS STEELIN CONCRETE EXPOSED TO CARBONATION
Student Number: 07M51418

Name: George William C. HONG

Supervisor: Nobuaki OTSUKI

The study investigated the corrosion behaviour of stainless steel in concrete exposed to carbonation. Threetypes of steels, SUS304 (18% Cr), SUS410L (12% Cr) and ordinary carbon steel SD295A, were cast in several OPCconcrete scenarios. The following were observed: (1) Both stainless steels were unaffected by carbonation in concretewith proper cover even with high water/cement ratio (0.7w/c) and high temperature exposure (40ºC). (2) Galvanic
effect was observed and enhanced by carbonation though was not a great corrosion threat for steels; while early ingressof carbonation (due to thin cover) served as corrosion threat for the economical SUS410L stainless steel. (3)Carbonation enhanced the corrosive effects of chloride on SUS410L and SD295A ordinary steel; while both stainlesssteels remained passive at pH 8 solution. Overall, carbonation was not a great threat for stainless steels; however, somefactors enhanced its effects. The new economical stainless steels (ex. SUS410L) with lowered chromium content merit
caution when used in carbonated environments, especially in thin cover scenarios.

1

Introduction

1.1

Stainless Steels

galvanic effect, (b) crack defect and (c) thin cover. (3)To investigate the effects of (a) chloride in concreteand (b) pH variation in solutions on the corrosion
behaviour of stainless steel. The research may provide
information for material selection and investigative
works of structures, and support the efforts in
producing better performing steels that accommodate
economy and serviceability for society.

Stainless steels have been used as concrete
reinforcement for structures that need to withstand theharshest environments; therein, serving as a solution to
the engineering problem of corrosion. The high cost of
stainless steels, however, has driven industry andacademe to further develop lower costing steels tobetter its economic viability and promote innovative
applications. These cheaper stainless steels produced
have lowered chromium and nickel contents whichmake them more susceptible to corrosion [1].

Table 1: Steel Specifications
Steel Cr Fe Ni C Si Mn P
S Yield13mm (%) (%) (%) (%) (%) (%) (%) (%) N/
mm2
ØSUS 18.34 71.06 8.13 0.05 0.37 1.50 0.31 0.24 295
304
SUS 12.57 86.34 0.20 .005 0.32 0.32 0.22 0.03 295
410L

1.2 Carbonation in Concrete

SD
295A

While corrosion studies on stainless steels havefocused on the harsher environments, the weakercauses of corrosion, such as carbonation, have yet to beextensively explored. Carbonation in concrete relies on
the mechanism of lowering the pH of concrete andtherein destroying the protective passivating film
formed on steels [2]. Stainless steels, at a glimpse, may
seem to be unaffected by a weak carbonation effect.
However, with the new innovative steels with loweredchromium and nickel content, greater considerationmust be made for the safe and appropriate usage incarbonated environments.

2

-

98.27

-

0.18 0.12 0.65 0.32 0.46 295

Experimental Procedures

2.1 Materials
Three types of steels were investigated: (1) SUS304
Stainless steel (18% Cr); (2) SUS410L Stainless Steel(12% Cr); (3) SD295A Ordinary Carbon Steel. The
undivided steel bars were corrugated type, 13mm
diameter and 8cm long. For divided steel bars, 1cm
length bars were used. The steels’ chemicalcompositions are shown in Table 1. OPC cement wasused with natural river sand and coarse aggregates(13mm maximum size) to make concrete specimens.

1.3 Research Objectives
The research goal was to gain better understanding of
the corrosion behaviour of stainless steel in concrete
exposed to carbonation and ultimately contribute to the
pursuit of better designs and wider applications forstainless steel as a sustainable material for society. The
specific objectives were: (1) To investigate the general
corrosion behaviour of stainless steel in concrete
exposed to carbonation; considering (a) water/cement
ratio, (b) carbonation exposure, (c) temperature
variations and (d) types of steel. (2) To investigate themacrocell and microcell corrosion induced by (a)2.2Investigation Scenarios

The specimens were designed to address eachobjective of the study. The steel specimens were casted
in OPC concrete and were exposed to 20ºC with 5% 10% CO2 at 60-70% RH for carbonated exposures andto 20ºC at 50-80% RH with negligible CO2 levels for
non-carbonated exposures. The specimens’ details areshown in Table 2.

1

2.3

Carbonation Depth: The carbonation monitoring was
performed using phenolphthalein solution test; 1g
phenolphthalein in 90ml ethyl alcohol and 10ml H20
solution. This test indicated the area where pH of
concrete had values above pH 9 [6].

Corrosion Monitoring

The general or microcell corrosion of steels wereassessed using three electrochemical techniques;Corrosion Potentials, Corrosion Current throughPolarization Resistance, and Passivity Grades throughAnodic Polarization. Macrocell corrosion wasmonitored using the divided steel bars concept
developed by Otsuki and Miyazato et al. (2001) [3].
Visual confirmation was also performed while
phenolphthalein test was used to monitor carbonation
depth.
Corrosion Potentials: The corrosion potentials
indicate the probability of corrosion of steel. This wasmeasured using the corrosion monitoring instrument
CT-7, manufactured by Riken Denshi Co., Ltd., using
an Ag/AgCl reference electrode (EAg/AgCl = ESCE +
0.006V) [4]. Potentials ranging more than -250 mV areconsidered to have 90% probability of no corrosion.
Potentials between -250 mV to -400 mV are said tohave some probable degree of corrosion, though quite
uncertain. Potentials less than -400 mV are considered
to have 90% probability of active corrosion for
Ag/AgCl reference electrode for the study (ASTM
C876-6).
Corrosion Current: The polarization resistance ofsteels was measured using the corrosion monitoring
instrument CT-7, manufactured by Riken Denshi Co.,
Ltd., using an Ag/AgCl reference electrode [4]. A
voltage of 50 mV with amplitude ranging from 0.05 Hz
to 5000 Hz was applied. Equation 1 shows the functionof corrosion current. For the study, a constant of K =0.0209 V was assumed for both carbon steel andstainless steels.
K
Equation 1
I micro =Rp × S

Table 2: Specimen Specifications and Exposure
Exposure(Carbonated and NonCarbonated)(20ºC for 260 days)(30 & 40ºC for 5
hours after 260 daysin 20ºC)

Specimen

Specifications

W/C ratio& Temp(0.3, 0.5, 0.7 w/c);(15mm cover)

Galvanic(0.5 & 0.7 w/c);(15mm cover)

20ºC for 260 days

Crack
Defect(1mm crack);(0.5 w/c);(15mm cover)(20ºC for 100 days)(40ºC for 5 hours
after 100 days in
20ºC)

Thin
Cover and
Chloride(0.7 w/c mortar);(3mm cover)(0,3,6 kg/m3 Cl-)
(Bare steel)(pH 3: H2SO4);(pH 6: Na2SO4);(pH 8 & 12: CaOH2)

pH
solutions

20ºC for 60 days

20ºC for 14 days

3

Results and Discussions

3.1

General Corrosion

Water/Cement Ratio Effect: The steels were casted
in three water cement ratios, 0.3, 0.5, and 0.7 w/c, andexposed in carbonation for 260 days. Only the 0.7 w/c
specimen carbonation depths reached the steel bars
with an average of 27mm carbonation depth. Results
showed that higher water cement ratio allowed fastercarbonation ingress, therein promoting corrosion.
0.7 w/c2

Corrosion Rate (A/cm2)

where: I micro : microcell corrosion current density (A/cm ),K : Stearn-Geary constant = 0.0209 (V),Rp : polarization resistance (), S : surface area of component (cm2).

Anodic Polarization and Passivity Grades: For the
study, the passivity grading method derived from theanodic curves as described by Otsuki et al. (1992) [5]
was applied. This was done using a potentiostat (HZ3000 by Hokuto Denko) which shifts the steel potentialfrom its natural value to breakdown potential or a valueof -1V with a scan rate of 1mV/s. A reference electrodeof Ag/AgCl and counter electrode of stainless steel wasused. The passivity grading system considers the anodic
currents’ range at the 0.2V to 0.6V potential, where
grade 5 passivity indicates good passivity protection
and grade 0 passivity indicates no passivity.
Macrocell Corrosion Current: The divided steel bars
concept developed by Otsuki and Miyazato et al. (2001)
was used to assess the macrocell corrosion of the steel
bars [3]. The corrosion currents flowing in and out of
the bars were measured using zero-resistance ammeters;
the set-up is shown in Figure 3.

5.00E-08
4.00E-08
3.00E-08
2.00E-08
1.00E-08
0.00E+00
0

50

100

150

200

250

300

Days

NOTE: Carbonation reached steels from 100 days. Full carbonation at 260 days.

SD295ASUS410L

SUS304

Figure 1: Corrosion Currents of CarbonatedSpecimens
Carbonation Effect: The effect of carbonation wasobserved for the SD295A steel bars with a drop in
potential and increase in corrosion currents as seen in
Figure 1. Both stainless steels were unaffected bycarbonation and remained at high potentials and low
corrosion currents. With proper concrete cover, bothstainless steels were resistant to carbonation.
Temperature Effect: The 0.7 w/c specimens were
exposed to higher temperatures (30ºC and 40ºC) for 5
hours at each temperature and monitored accordingly.
The potentials are shown in Figure 2 for varying
temperatures. The higher temperature caused a2although increased slightly in currents, as shown in
Figure 5. Passivity grades of stainless steels remained
high while visual confirmation showed no signs ofcorrosion for both stainless steels.

potential drop for all the steels. However, only
SD295A turned to active corroding state, while bothstainless steels remained at high potentials. The
corrosion currents results also showed an increase in
SD295A corrosion currents (4.5x10-8 A/cm2) whileboth stainless steels remained low in currents (about
1x10-8 A/cm2). Temperature increase was seen toenhance the effects of carbonation, although stainlesssteels remained passive.
Steel Types: Although both stainless steels remained
passive in carbonation exposure, SUS304 wasobserved to have higher potentials and lower corrosion
currents compared to SUS410L. Both stainless steels
were superior in corrosion resistance compared toSD295A carbon steel.

0.7 w/c (Day 260)

STAINLESS STEELSCARBON STEELS

5.00E-09
4.00E-09

Macrocell Current (A/cm2)

3.00E-09

ANODIC
2.00E-09
1.00E-09
0.00E+00
1-1.00E-09

2-2.00E-09

3

4

5

6

7

8

CATHODIC-3.00E-09

Carbonated (0.7 w/c)

-4.00E-09

Potential(mV vs. Ag/AgCl)

Bars-5.00E-09

200
100
0
-100
-200
-300
-400

SUS304 & SD295A

Figure 4: Macrocell Corrosion Currents
for Galvanic Effect (Carbonated)

ct

20

30

Crack 40o C (Day 100)40

Temperature (oC)
SUS410L
SUS304

SD295A

1.00E-05
8.00E-06

15

1

STAINLESS STEEL BARS

mm

Macrocell Current(A/cm2)

Figure 2: Corrosion Potentials of CarbonatedSpecimens in Varying Temperatures
mm

0.5 ~ 1mm

CARBON STEEL BARS

6.00E-06

4.00E-06
2.00E-06

1

2

3

4

5

6

7

8

9

-2.00E-06

Ii

I

I0

Epoxy

macro

Bars

-4.00E-06

Figure 3: Macrocell Galvanic Corrosion
Divided Steel Bars Set-up

3.2~ x10-7 A/cm2

0.00E+00

Concrete
Solderedwire

SUS410L & SD295A

SD295A (1)SD295A (2)SUS410L (1)

SUS410L (2)

SUS304 (1)SUS304 (2)(uncracked regions = ~x10-9 to ~x10-8 A/cm2)

Figure 5: Macrocell Corrosion Currents for Crack(Carbonated)

Macrocell Corrosion

Thin Cover Effect: Mortar concrete was used for thethin cover (3mm) specimens with undivided steel bars.
The thin cover allowed early ingress of carbonation,
reaching a 12mm carbonation depth in 60 days
exposure period. The results showed a drop in potential
for SD295A and SUS410L (-250 to -300 mV range)
showing considerable probability of corrosion.
Corrosion currents increased for both steels, although
SUS410L (2x10-8 A/cm2) remained relatively lower
than SD295A (1x10-7 A/cm2). The passivity grading of
SUS410L dropped to 2 while SD295A dropped to 1 asshown in Figure 6. Visual confirmation showed thatgeneral corrosion occurred for SD295A and a pitting
corrosion tendency was seen for SUS410L. A study
done by Imperial College Of Science, Technology And
Medicine (2002, February 14) indicated that stainlesssteels corrode through pitting due to random lowchromium regions in the metal [7]. Carbonation may
have exploited this phenomenon and affected
SUS410L in thin cover scenario.

Galvanic Effect: The galvanic effect of steels occurs
when dissimilar metals with different potentials areconnected. In this case, the stainless steel bars wereconnected to the carbon steel bars using the divided
steel bar concept shown in Figure 3. Upon carbonation,
the ordinary carbon steel connected to the stainlesssteels turned anodic while the stainless steels turned
cathodic, as shown in Figure 4. This showed thatgalvanic effect occurred and was enhanced bycarbonation. However, the macrocell currents (lowvalues of ~x10-9A/cm2) were smaller than the microcell
currents (high values of ~x10-9 to ~x10-8A/cm2) for thesame steel bars. This indicated that the galvanic effectwas not a great corrosion threat for the steels.
Cracks Effect: The cracked region allowed fastercarbonation ingress compared to the uncracked regions.
The cracked regions showed higher macrocell
corrosion than microcell corrosion. The SD295A
turned into active corrosion state and increased incurrents while the stainless steels remained passive,

3

3.3

Chloride and pH Solutions

Overall, carbonation was not a great threat for stainlesssteels; however, some environmental factors enhanced
its effects. The new economical stainless steels (ex.
SUS410L 12% Cr) with lowered chromium content
merit caution when used in carbonated environments,
especially with thin cover scenarios.

Chloride Effect: Chloride (NaCl) of 0, 3, 6 kg/m3 ofmortar was mixed in concrete (thin cover 3mm) using
undivided steel bars. Carbonation was seen to enhancethe effects of chloride additions such that only the
carbonated specimens showed signs of corrosion in thesteels. SUS410L was affected by chloride andcarbonation, where potentials dropped to active regions,corrosion currents increased and passivity grade
lowered at 3 and 6 kg/m3 Cl-. SUS304 remained
passive in all chloride and carbonation scenarios.
pH Effect: Bare steels were submerged in pH
solutions for 14 days. Different pH level solutions
were achieved by mixing H2O with H2SO4 for pH 3,
Na2SO4 for pH 6, and CaOH2 for pH 8 and 12. It wasobserved that both stainless steels remained passive at
pH 8, typical pH value of carbonated concrete.
SUS410L showed corrosion at pH 3, while SUS304
remained low in corrosion for all pH values. SD295A
corroded at pH 8 and below.

The following are recommendations for the usage ofstainless steels: (1) For carbonated scenarios withproper concrete cover, the economical stainless steel
SUS410L has sufficient corrosion resistance; even withhigh water/cement ratio (0.7w/c) and high temperature(40ºC) exposure. (2) The economical Stainless SteelSUS410L was much better than SD295A for cracked
and thin cover concrete scenarios exposed tocarbonation. However, SUS410L was found not to becompletely immune to thin cover under carbonation.
Hence, usage in these scenarios needs properconsideration. The more expensive stainless steel
SUS304 is more reliable for thin cover scenarios undercarbonation. (3) Use of Stainless Steel SUS410L in
scenarios with chloride and concrete carbonation needs
proper consideration; SUS304 is more reliable forsimultaneous chloride and carbonation scenarios.

(V vs. Ag/AgCl)

Potential (V)Anodic Polarization
Thin Cover (Carbonated)

Grade 5
Grade 2
SUS304

Grade 1

SUS410L
SD295A

1.00E-03

1.00E-02

1.00E-01

1.00E+00

1.00E+01

1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4

References

1.00E+02[1] U. Nurnberger and W. Beul (1999), Corrosion of
Stainless Steel Reinforcement in Cracked Concrete,
OTTO-Graf-Journal, V.10 (1999), 23-37.
[2] C.F. Chang, J.W. Chen (2004), The Experimental
Investigation of Concrete Carbonation Depth, Cement
and Concrete Research, 36 (2006), 1760 – 1767.
[3] S. Miyazato, N. Otsuki, and H. Kumura (2001),
Estimation Method of Macrocell Corrosion Rate ofRebar in Existing Concrete Structures Using NonDestructive Tests, East Asia-Pacific Conference(EASEC 8), 2, pp.531-541.
[ 4 ] M. Bacay (2006), Influence of Construction
Defects on the Corrosion Behaviour of Steel inConcrete Exposed to Carbonation and Chloride Attack,(Doctoral dissertation, IDE, Tokyo Institute ofTechnology, 2006), Japan.
[5] N. Otsuki, S. Nagataki, and K. Nakashita (1992),
Evaluation of AgNO3 Solution Spray Method for
Measurement of Chloride Penetration into Hardened
Cementitious Matrix Materials, ACI material Journal,
89 (1992), 587-592.
[6] A. Bentur, S. Diamond & N.S. Berke (1997), SteelCorrosion in Concrete: Fundamentals and Civil
Engineering Practice, p. 7-19, UK, E & FN Spon.
[ 7 ] Imperial College Of Science, Technology And
Medicine (2002, February 14), Stainless SteelCorrosion Mystery Solved By UK Researchers,
ScienceDaily, Retrieved July 21, 2009, fromhttp://www.sciencedaily.com/releases/2002/02/020214
080414.htm

Current Density I (uA/cm2)

SD295ASUS410L

SUS304

Figure 6: Anodic Curves for Thin Cover Specimens

3.4 Conclusions andRecommendations
The following conclusions were derived from the study
to address the objectives: (1) Carbonation was not a
great threat for both SUS410L and SUS304 stainlesssteels in properly covered concrete; even with highwater/cement ratio (0.7w/c) and increased temperature(40ºC) exposure. (2) Galvanic effect did not pose agreat threat to the steels in contact even as stainlesssteels were promoted as cathode and carbon steel as
anode for carbonated scenarios. The cracks defect inconcrete exposed to carbonation induced greatermacrocell corrosion for the steels, however, stainlesssteels remained relatively low in corrosion compared
to SD295A and had no visible corrosion tendency.
Thin cover (3mm) scenario with carbonation lowered
the corrosion resistance of SUS410L where pitting
corrosion tendencies were visually observed.
Carbonation was then considered to be a threat for
SUS410L in thin cover scenarios. (3) Chloride effectwas significantly enhanced by carbonation, causing
corrosion for SUS410L and SD295A (3kg/m3 Cl- with
3mm cover). It was also observed in pH solutions thatstainless steels remained passive at pH 8, typical pH ofcarbonated concrete.

4

DEVELOPMENT OF A VISIBLE LIGHT RESPONSIVE COMPOSITE
PHOTOCATALYST FOR PCE TREATMENT
Student Number: 08M18016 Name: Go ISHIDA Super visor: Hirofumi HINODE

᪂つྍどගᛂ⟅ᆺ」ྜゐ፹ࢆ⏝࠸ࡓ᭷ᶵሷ⣲໬ྜ≀ࡢศゎ▼⏣ ㇦ ⌧ᅾࢸࢺࣛࢡ࢚ࣟࣟࢳࣞࣥ㸦PCE㸧࡞࡝ࡢ᭷ᶵሷ⣲໬ྜ≀࡟ࡼࡿᆅୗỈởᰁࡀⓎ⏕ࡋ࡚࠸ࡿࡀࠊ
ࡇࡢࡼ࠺࡞໬Ꮫ≀㉁ࡣ⏘ᴗάືࢆ⥅⥆Ⓨᒎࡉࡏࡿࡓࡵ࡟ᚲせ୙ྍḞ࡞≀㉁࡛࠶ࡿࠋࡑࡢࡓࡵࠊࡇࢀ
ࡽࡢ໬Ꮫ≀㉁ࡢ㧗ᗘฎ⌮ࢩࢫࢸ࣒ࡢ㛤Ⓨࡀᛴົ࡛࠶ࡿࠋᮏ◊✲࡛ࡣ᭷ᶵሷ⣲໬ྜ≀࡜ࡋ࡚ᗈࡃ౑⏝ࡉࢀ࡚࠸ࡿ PCE ࢆ㑅ࡧࠊࡑࡢ⡆᫆࠿ࡘ┬࢚ࢿࣝࢠ࣮࡞㝖ཤ᪉ἲ࡜ࡋ࡚ࠊኴ㝧ගࡀ฼⏝࡛ࡁࡿྍどගᛂ⟅ᆺゐ፹࡛࠶ࡿከᒙ࣮࢝࣎ࣥࢼࣀࢳ࣮ࣗࣈ࡜஧㓟໬ࢳࢱࣥࡢ」ྜゐ፹㸦TiO2-MWNT㸧ࢆ㛤Ⓨࡋࠊ
PCE ගศゎᐇ㦂࡟ࡼࡗ࡚ホ౯ࢆ⾜ࡗࡓࠋᐇ㦂ࡢ⤖ᯝࠊTiO2-MWNT ゐ፹࡟ࢩࢼࢪ࣮ຠᯝࡀ⌧ࢀࠊྍどග↷ᑕୗ࡛ࡶ TiO2-MWNT 」ྜゐ፹࡟ࡼࡾ PCE ࢆගศゎࡍࡿࡇ࡜ࡀ࡛ࡁࡓࠋ

1. Introduction
Chlorinatedasbioremediation, the treatment period needed istrichloroethylene (TCE) and tetrachloroethylene

long. There is also treatment by photodegradation(PCE) have been widely used as industrial

using

solvents for degreasing metals and dry cleaning.

relatively

short

However, they are commonly found to contaminate

secondarytreatment.

water,

responsive photocatalyst is used.

soilorganic

andaircompounds

due

to

such

their

low

photocatalyst.

This

methodrequirestreatment

period

and

no

Only

UV

However,

Titanium dioxide (TiO2) was widely used as

biodegradability, toxicity and high persistence inenvironment,

photocatalyst to degrade contaminants. However,contamination usually occurs in groundwater. The

it doesn’t respond to visible light. Therefore,

treatment of groundwater contaminated with PCE

visible light responsive composite photocatalysts

will be very costly and requires long treatment

have been developed. TiO2 supported on carbon

time due to large amount of groundwater that

nanotube showed promising results in adsorptive

must be treated. Although some effective PCE

ability and photoactivity under visible lighttreatment methods have been established forirradiation [1]. Using this photocatalyst, lowindustrial purposes, there is still no practicalenergy cost, short treatment period and simple

method for groundwater treatment. Some methods

treatment process could be achieved. Furthermore,such as adsorption using activated carbon or

many other composite photocatalysts under visible

bioremediation have been researched recently. In

light irradiation such as Fe:Nb-TiO2 were also

adsorption using activated carbon, secondary

developed [2].

theenvironment.

In

water

In this study, the photodegradation of PCE

treatment is needed for the used active carbon. In

5under visible light irradiation using composite

MWNT was collected on a glass fiber filter andphotocatalyst of TiO2 and multi-walled carbon

washed with deionized water until the pH value

nanotube(MWNT)

reachedcomposite

catalystwaswasinvestigated.
named

The

neutral

[3].

The

cut

MWNT

was

characterized by TEM and BET.

TiO2-MWNT.

Figure 1 shows the schematic figure for the

2.2 Preparation of TiO2-MWNT catalyst

contaminated groundwater treatment system.
First, the groundwater which is polluted by PCE isTiO2-MWNT was synthesized by sol-gel method.

drawn by the pump to reaction tank. And then,The precursor, titanium tetraisopropoxide (TTIP)

PCE in the groundwater is degraded by theor tetra-n-butoxy titanium (TNBT) was hydrolyzed

photocatalysts under visible light irradiation.

to form TiO2 and deposited on MWNT with the aid

Finally,of supersonic treatment [4]. The preparation was

cleangroundwaterisreturnedtocarried out as follows: 0.1 mg of MWNT was put

underground.

into 20 ml of ethanol under supersonic treatment
for 0.5 hour and then 3.60 ml of TTIP or 3.99 ml of
TNBT

was

added

followedbysupersonictreatment for 0.5 hour. After that, 80 ml ofdeionized water was added under supersonictreatment for 0.5 hour. Obtained grey sol-gel
liquid was aged at room temperature for 10 hours
and then dried at 100 Υ for 10 hours. The

Figure 1 New concept for treatment of PCE in

obtainedgroundwater by photocatalyst

atmosphere at 400 Υ for 1 hour. Synthesized

solid

was

calcinated

under

N2

catalyst was named X-TiO2-MWNT, where X

2. Experimental

corresponds to the weight percentage of MWNT in

2.1 Cut of MWNT

TiO2-MWNT composite catalyst. In this study, X is

As prepared MWNT contained many impurities

fixed at 10. The catalysts were characterized bysuch as catalysts used in synthesis, it was firstly

SEM, TEM, TG, and XRD.

washed in concentrated HCl under magnetic

2.3 Photodegradation experiments

agitation for 5 hours and filtered to eliminate
these catalysts. After that, cutting procedure of

The evaluation of TiO2-MWNT photocatalytic

MWNT was carried out to enlarge the specificactivity for degradation of PCE was conductedsurfaceofunder UV-vis irradiation. The experiments wereadsorption. The cutting procedure was as follows:

carried out in a glass photochemical reactor. The

250 mg pretreated MWNT was suspended in 300

reactor was equipped with a UV and visible light

area

and

increaseamountmagnetic

lamp (100 W, 300 nm ~ 600 nm) located axiallyagitation for 30 min at room temperature and thenand held in a quartz immersion, and a UV-cut film

180 ml of concentrated HNO3 was added. After

which allows only visible light (400 nm ~ 600 nm)

that, the mixture was heated at 60Υ for 6 h. The

to pass. The UV photodegradation experiments

mlofconcentrated

H2SO4

the

under

6

MWNT’s surface area increased 1.7 times.

were carried out without the UV-cut film and thevisible light photodegradation experiments were

The XRD results of TiO2-MWNT are shown incarried out by applying the UV-cut film on the

Figure 3. Only anatase structured titania waslamp. The experiments were done in a closed

found from the XRD patterns of TiO2 andsystem. Before irradiation, the PCE solution with

TiO2-MWNT. Anatase structure is known tophotocatalyst was stirred in a dark condition for 1

contribute to the photoactivity.

hour

to

establishanadsorption-desorption

equilibrium.

3. Results and discussion
3.1 Characterization
The TEM images of MWNT and cut MWNT are
shown in Figure 2.

Figure 3 XRD results

3.2 Photodegradation of PCE
Figure 4 shows the PCE degradation by(a) MWNT

photocatalysts

under

UV--visirradiation.

10-(B)TiO2-MWNT was prepared using TNBT as
TiO2 precursor. High conversions of PCE wereobserved for 10-TiO2-cutMWNT, 10-TiO2-MWNT,
10-(B)TiO2-MWNT, and TiO2.

(b) cut MWNT
Figure 2 TEM images

Compared to the one before cutting, the cut
MWNT surface became rougher. The BET surface

Figure 4 PCE degradation under UV-vis

area of MWNT and cut MWNT were 23.6m2/g andirradiation

40.1m2/g, respectively, which means that the cut

7Figure 5 shows the PCE degradation byphotocatalysts under visible light irradiation.
TiO2-MWNT

catalyst

showedhigherphotocatalytic activity than TiO2. The improved
photocatalytic activity of TiO2-MWNT catalyst
might due to the synergetic effect between TiO2
and MWNT [5].

Figure 6 Photodegradation mechanism [5]4. Conclusion
TiO2-MWNT

composite

photocatalyst

was

prepared by a modified sol-gel method. The new

Figure 5 PCE degradation under visible lightcatalyst

irradiation

irradiation for PCE treatment by the synergeticwaseffective

under

visiblelighteffect between TiO2 and MWNT.

3.3 photodegradation mechanism
The PCE photodegradation mechanism (Figure6) could be explained as follows: considering theReference

semiconductive properties of carbon nanotubes,[1] Tomonori, OGATA, the dissertation for Master
Degree of Engineering, 2005

MWNT may absorb the irradiation and transfer
the photo-induced electron (e) into the conduction

[2]

Carl

Renan

Estrellan,

Chris

salim

&

band of the TiO2 particles. Simultaneously, aHirofumi Hinode, “Photocatalytic activity ofpositive charged hole (h+) might be formed bysol-gel derived TiO2 co-doped with iron andelectron migrating from TiO2 valence band toniobium”, Reaction Kinetics and Catalysis

MWNT. With this understanding, the role played

Letters (2009) 98: 187-192

by MWNT can be illustrated by injecting electrons[3] J. Li, Y. Zhang / Applied Surface Science 252

into TiO2 conduction band under visible light(2006) 2944–2948

irradiation and triggering the formation of very[4] Zhiwei, Wu, the dissertation for Master Degree

reactive radicals superoxide radical ion O2 andof Engineering, 2008

hydroxyl radical HO, which are responsible for[5] W.Wang et al./ Journal of Molecular Catalysis

the degradation of the organic compound [5].

A: Chemical 235 (2005) 194–199

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      ͠\{|^  ͠\{|^|  #          \{| ]  _`^\   \ ^  \^     \*^   \^^  '*    ?      '*11       *; ' <     
'  |}     #            |      {|                        \ ^ >    
#                  !}' ' "         \{|^     |      {|                
 {|          |      {| ]  _`     *               \ ^  ?           '  {|        #      >     #                      !}'  '     
#                  !}'  ' "#           }         #  
|      {| ]  _`          ?       {| ]  _` "                        
"  |      {| ]  _`                  ? #           ?
    {|        
 '*     ?
    ?                                       `                         ^                    _` 
                                       ‫'‬

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‫^|{  \͠}!^\‬‫^|{  \͠^\‬‫|‬‫> |‬‫ ‬‫   ‬‫  ‬‫ ‬‫ ‬‫ ‬‫ ‬‫ ‬‫‬‫ ‬‫   ‬‫‬‫ ‬‫‬‫   ‬‫  ‬‫' ‬‫' ‬‫‬‫|{^\‬‫|{^\‬‫      |‬‫   ?‬‫^|{  \    ‬‫‬‫‬‫^_ ] {\͠^\ ^_ ] {\͠}!^\‬‫|‬‫> |‬‫ ‬‫   ‬‫  ‬‫ ‬‫ ‬‫ ‬‫ ‬‫ ‬‫‬‫ ‬‫   ‬‫‬‫ ‬‫‬‫   ‬‫  ‬

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‫' ‬‫    ?       ‬‫         "‬‫        ?    ‬‫‬‫    ‬‫      ‬‫    ‬‫  ‬‫' ‬‫‬‫‬‫|{^\‬‫|{^\‬‫      |‬‫   ?‬‫^`_  ]\    ‬‫     ‪$‬‬‫      |  |‬‫ ‬‫          ‬‫   ‬‫    |{       |‬‫  @ '  '}!       ‬‫  ] |{       ‬‫            `_‬‫'  '}!‬‫       |  |‬‫‬‫        ?‬‫       '  '}! >‬‫ |{‬‫       ?‬‫        ‬‫|{      |    ‬‫‬‫         ?‬‫`_  ] |{      |‬‫   ‬‫    ?‬‫      |{  ‬‫  ? *‬‫     ‬‫             ‬‫‬‫    ‬‫`_   ] |{     ^}‬‫           ‬‫       ‬‫   ‬‫  ?          ‬‫            ‬‫     ‬‫          ^‬‫ ] |{        ‬‫           `_ ‬‫' '  '}!‬‫    ?‬‫‬‫      > ` ‬‫  ‬‫     ?  ‬‫ `_   ]     '‬‫      |{       ‬‫‬‫    =‬‫ޤ  !ޣ‬‫   ` '>     ^‬‫^\ !}‬‫  `  |      ‪!^ * #‬‬‫ '‬‫‬‫ ‬‫  ‬‫ ‪#    # # '   # #‬‬‫^!\!}!   !    ‬‫        _   ^}‬‫           ‬‫   ‪        #‬‬‫^!\    ‬‫  `  ‪^ # <  '   ##‬‬‫    ‬‫ ‪# # $  ##‬‬‫^\ ‬‫    ?       "‬‫      |{  ‪    #‬‬‫      ‬‫    ‬‫  ‬‫"     ‬‫     ‬‫`_   ] |{     ^‬‫           ‬‫?      " '  '}!‬‫‪#‬‬‫?            ^!‬‫      |{    ‬‫  ‬‫?     *' ‬‫‪12‬‬

                    !

  " # $%$

   &'  "  %("()()*᣹࿶ࠬࠗ࠶࠴ࡦࠣ࡟ࠡࡘ࡟࡯࠲ߩࠛࡀ࡞ࠡല₸ߦ෸߷ߔ૶↪᧦ઙߩᓇ㗀ਸຌ ᢘ੫
ߘߩ߹߹ߢߪ㔚Ḯߦ೑↪ߢ߈ߥ޿⒟㧘㐿᡼㔚࿶߽ߒߊߪ⍴⛊㔚ᵹ߇ዊߐ޿㔚ജ㧔ੜᧃ㔚ജ㧕ࠍ೑↪
ߢ߈ࠇ߫㧘りߩ࿁ࠅߦ޽ࠆ᭽‫߇ࠡ࡞ࡀࠛߥޘ‬㔚ജḮߣߥࠅ㧘ૐⅣႺ⽶⩄␠ળߩታ⃻ߦ⽸₂ߢ߈ࠆ㧚ߘߩࠃ߁ߥੜᧃ㔚ജߪ৻⥸ߦᄌേ߇ỗߒ޿㧚ᧄ⎇ⓥߢߪ㧘᣹࿶ࠬࠗ࠶࠴ࡦࠣ࡟ࠡࡘ࡟࡯࠲ߢੜᧃ㔚ജࠍ᣹࿶ߔࠆ႐วࠍᗐቯߒ㧘᣹࿶࿁〝ߩࠗࡦ࠳ࠢ࠲㧘ࠬࠗ࠶࠴ࡦࠣ๟ᵄᢙ㧘࠺ࡘ࡯࠹ࠖᲧ㧘+ ᰴ஥ߩࠗࡦ
ࡇ࡯࠳ࡦࠬ㧘෸߮ੜᧃ㔚ജߩ㔚ᵹ࡮㔚࿶․ᕈ߇ࠛࡀ࡞ࠡല₸ߦ෸߷ߔᓇ㗀ߦߟ޿ߡℂ⺰⊛෸߮ታ㛎⊛ᬌ⸛ࠍⴕߞߚ㧚     F  , G H, ,        F    ,  ,   + , + H,
 &   F &  ,  H        &  ,  - *    $ &  '  ,     & '  ,         +
  ,   I           I      +   + ,  &          + ,       J   & &       : ,         '       K  I'      '  ' - *    &         ,        I'                     -$            ,    &                           - %                        ,                        ./- : ,                ,  &  &; - (   < &         ,    &    =             & - %
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20

Experimental Analysis and Modeling of Dual-Polarized MIMO Channel
Student Number: 08M18068 Name: Yohei KONISHI Supervisor: Jun-ichi TAKADA

直交偏波 MIMO システムのための伝搬路特性に関する研究
小西 洋平
本研究においては, 4.5GHz 帯直交偏波 MIMO システムを対象としてマクロセル及びマイクロセル環境におけるフィー
ルド実験をもとに, 偏波特性の解析・モデル化を行った. その結果, マイクロセル環境においてはビル壁による多重反射の影響により水平偏波の減衰が顕著であるのに対し, 実験を行った 2 つのマクロセル環境においては, 互いに異なる減衰特性を示すことが観測された.

1

Introduction

2.1

Channel Sounding System

Rapid progress of wireless communication systems results Channel sounding was performed using Medav RUSK-Fujitsu
in the demands on more high speed and reliable wireless ser- wideband channel sounder [3] in all the three measurement
vices. At the same time, it is a big issue to develop spectral campaigns. As for the array, uniform rectangular array andefﬁcient wireless systems. In this context, multiple-antenna stacked uniform circular array are used for transmitter andtechnique (MIMO system) has appeared as a promising way receiver, respectively. Rubidium reference oscillators at bothto contribute these demands. Although MIMO systems of- base station (BS) and mobile station (MS) ensure carrier andfer signiﬁcant beneﬁts, the performance is heavily dependent switching synchronization throughout the measurement duon the condition of propagation channel, since such beneﬁts ration. The speciﬁcations of this measurement system areare exploited in multipath-rich environments. The inﬂuence summarized in Table 1.
of channel condition on the MIMO systems can be mainly
observed as performance degradation due to the spatial fad- Table 1: RUSK-Fujitsu Wideband MIMO Channel Soundering correlation among subchannels. Furthermore, the use of
Carrier frequency
4.5 GHz
multiple antennas results in the increase of equipment size.
Bandwidth
120 MHzIn this regard, adoption of polarization diversity technique
BS antenna array
Uniform rectangular array
into MIMO systems is proposed to realize compact MIMO
2 × 4 × 2 elements (row × col. × pol.)user terminal keeping inter-subchannel correlations low. InMS antenna array
Stacked uniform circular array
deed, the earlier studies validated that better separation among
2 × 24 × 2 elements (row × col. × pol.)channels can be achieved by using dual-polarized antennas [1].
V & H polarized patch antennas
As mentioned above, since the performance of wireless sysTransmit signal
Wideband multitone
tems in a real-world is determined by the propagation chanMax. delay setting 3.2 μs
nel condition, knowing the multipath channel including theeffect of depolarization through the channel plays a key role
in realistic system design. Up to now, despite a number ofTable 2: Summary of three measurement environments
measurement based studies have been conducted focusing
Small urban macrocell
Microcellon polarization behavior e.g. [2], some contradictory conKawasaki City Ota ward
In campus
clusions are found in these literatures.
Kanagawa
Tokyo
Tokyo Tech
In this context, to know the characteristics of polarization
BS height
85 m
30 m
1.79 min the propagation channel is still a major challenge in the
MS height
1.80 m1.65 m1.65 mMIMO channel modeling. Therefore, to know the depolarBS-MS distance
230 m
186 m
50 m
ization mechanism in the propagation channel is the main
∼ 400m∼ 276m∼ 125minterest of this thesis. More speciﬁcally, this study ﬁgures
Structure typeresidential &residentialbuilding
out the answers to the following important questions.
industrial
canyon
1. Do the different polarization pairs suffer same pathloss?
2. What are the causal factors of depolarization in the 2.1.1 Measurement Environments
propagation channel?
Layouts of each measurement setup are shown in Figure 1.
With each setup, measurement are conducted midnight undera clear weather and the MS was moved at a slowly walking
2 Channel Measurements
pace along the street. Distances between each MS positionsare 20 m in Macrocell and 10m in a Microcell scenario, startThe measurements were conducted in the areas around Tokyo, ing and ending with static measurements.
1. Kawasaki City, 2. Ota ward Tokyo and 3. Oookayama
In Kawasaki macrocell scenario (Figure 1(a)), BS was placed
campus of Tokyo Institute of Technology. Table 2 summa- on top of the building which is highest in this area. Most of
rizes the basic information of the environments.
other buildings are lower than half of BS height. In Tokyo
macrocell scenario (Figure 1(b)), the site consists of four21ZŽƵƚϭD^ƌŽƵƚ
^ZŽƵƚϮ

^ZŽƵƚϭ

ZŽƵƚϰ

ZŽƵƚϯ

ZŽƵƚϰ

20m
^60m(a) Kawasaki, Macrocell.

ZŽƵƚϯ

70m

ZŽƵƚϮ

(b) Tokyo, Macrocell.

(c) In campus, Microcell.

Figure 1: Layout of the measurement scenario
streets. Most of the measurement positions are categorized 3 Results and discussion
as non-line-of-sight (NLoS) or obstructed-line-of-sight (OLoS)
in both of the macrocell scenarios, and observed some LoS 3.1 Polarization Characteristics
paths are excluded in the analysis. In Tokyo Tech campus
microcell scenario (Figure 1(c)), the site is divided into 4 3.1.1 Formulation of Polarization Statesroutes, to analyze depolarization mechanisms more closely. To investigate the inﬂuence of polarization states on the sysThe routes 1, 3, and 4 in this scenario, the line-of-sight from tem performance, Cross-polarization ratio (XPR) and coMS to BS is completely blocked by the buildings, while the polarization ratio (CPR), are often used. Recently in [6],sight is open at all the MS positions in route 2.
authors discussed the inﬂuence of XPR and CPR on the capacity of dual-polarized systems. The authors concluded that
XPR is the most important parameter which dominates theperformance of polarized MIMO systems. The deﬁnition of
2.1.2 Parameter Estimation and Multipath Clustering cluster polarization ratios in this study is the power ratio of
Estimating the parameters from real-world propagation chan- the different polarization combinations.

nel is a essential element of measurement-based channel mod l∈Ck γVV,lBS(2)
XPRk,V = 20 log10 eling. maximum-likelihood parameter estimation algorithm
 l∈Ck γVH,l
named RIMAX is originally developed in [4]. The path delay (τ ), direction of departure (DoD) and arrival (DoA) areestimated for the 4 polarization pairs: co-vertical (γVV ), co l∈Ck γHH,lBS(3)
XPRk,H = 20 log10 horizontal (γHH ), horizontal transmission-vertical reception
 l∈Ck γHV,l(γVH ) and vice versa (γHV ). The data model is formulated
as superposition of K 6-dimensional Dirac deltas weightedby a 2 × 2 polarimetric path weights matrix.
 l∈Ck γVV,lMS(4)
XPRk,V = 20 log10  l∈Ck γHV,lK γHH,k γVH,kH (α, τ, φR , θR , φT , θT ) =γHV,k γVV,k l∈Ck γHH,lXPRMS
k,H = 20 log10   l∈Ck γVH,lk=1

×δ(α − αk )δ(τ − τk )δ(φR − φRk )×δ(θR − θRk )δ(φT − φTk )δ(θT − θTk )

(1)




(5)




 l∈C γVV,l
where φR , θR and φT , θT are the azimuth and elevation an(6)CPRk = 20 log10   k l∈Ck γHH,lgles at MS and BS, respectively. The α is the Doppler-shift,
but it is not included in this study due to the limitation ofsounding system. The estimated parameters by RIMAX is These parameters do not include the inﬂuence of measurethen, clusterized by using the automatic multipath cluster- ment antenna, so they only represent the degree of depolaring framework [5]. This is to treat estimated polarimetric ization in the propagation channel.
parameters in more accurate manner. The automatic clustering framework used in this study is based on following three 3.1.2 Theorey of Depolarization Mechanisms
steps:
Considering the measurement environments in this study, following factors are expected as dominant propagation mech1. Local optimization: k-means clustering
anisms which cause the difference between VP (horizontal
2. Global optimization: simulated annealingtransmission) and HP (vertical transmission) received power.

3. Determination of number of multipath clusters: aver- Macrocell scenarios
age rank aggregation• Over-rooftop diffraction
Therefore, the distance-measure is the most fundamental and• Over-rooftop diffraction + reﬂection from the walls
important basis of this framework. The detail is availableof the housesin [5].
22

paring the difference between two Macrocell sites, decay coefﬁcients are quite different. Especially in Tokyo measure• Reﬂection from vertical walls/pane (window glass) of ment, HP showed better transmission than VP. This results
the buildings
do not match with the results from existing studies, since
the VP is expected to show better transmission in shadow• Reﬂection from the ground (asphalt)
ing region. Table 4 shows the power proportion of upper andlower horizon against overall received power. It is evident• Diffraction at building cornerthat most of the power comes from upper horizontal direction
in both sites, but almost all the power is received from upperŝĨĨƌĂĐƚŝŽŶ
horizon in Tokyo measurement. These results indicates thedǆZĞĨůĞĐƚŝŽŶ
existence of unknown but inﬂuential other propagtion mech^ŝĚĞ
anisms, which come from upper horizontal direction. As forǀŝĞǁZǆ
Microcell scenario, faster decay of HP is clear. This is considered as due to the reﬂection from vertical walls, whichstrongly penetrate only the HP (Section 3.1.2).

Microcell Campus scenario(a) Macrocell scenariodǆZǆ

Table 3: Fitting Parameters for Cluster Gain vs. Distance

dŽƉ
ǀŝĞǁ

[dB]
Kawasaki

dǆ

Zǆ

^ŝĚĞ
ǀŝĞǁ

Tokyo
Campus(b) Microcell scenario

3.1.3

Cluster Gain of Different Polarization Pairs

Figure 3(a), 3(b) and 3(c) show the results from Kawasaki
City (Macrocell), Ota ward Tokyo (Macrocell) and Tokyo
Tech campus (Microcell), respectively. Cluster gain Pk,RT(subscripts RT indicates the polarization states in receive
transmit order) are computed by narrowband approach as
follow.

(7)
Pk,RT [dB] = 20 log10 
γRT,l l∈Ck

The cluster gain decay characteristics are shown as a function of BS-MS distance with linear ﬁtting line obtained fromthe method of least squares. In this study, decay characteristics are modeled as:
Pk,RT [dB] = (αmean + αdiﬀ ) × log10 (d) + β

(8)

αmean
−90.44−1.03−49.38αdiff−11.33
8.39
4.15
4.00
7.58
0.37

Mean gain−114.7−114.8−127.96−130.68−108.26−103.66

Table 4: Power proportion: [ Elevation at MS ]Roof/house
Ground[%]Overall
85.7 / 96.3
14.3/3.7
HH 51.2 / 63.5 46.9 / 63.7 77.1 / 58.9
HV
2.3/2.3
2.7/2.2
3.6/3.2
VH
4.7/3.5
3.8/3.3
10.4/7.5
VV 41.3/30.7
46.7 / 30.7
8.9 / 30.8

Figure 2: Illustration of propagation mechanisms.
Therefore, reﬂection is a dominant propagation mechanism
for all environments in this study. Especially in a Microcell
campus scenario, most of the received wave experience reﬂection from building wall or asphalt road. Earlier studies
provide theoretical way to understand difference of VP and
HP reﬂection characteristics for the canonical problems. For
example in case of reﬂection from vertical walls, behavior
of VP and HP is greatly different in terms of the reﬂection
coefﬁcient. Both coefﬁcients are unity at grazing incidence
and decrease with increasing grazing angle. But no reﬂection
phenomena exists only for the HP wave at a certain angle.
The angle is called as Brewster angle. For the inﬂuence ofdiffraction, it is well known that VP shows better transmission (higher received power) in shadowing region comparedto HP. So, this effect is considered as also applicable in thisstudy.

Pk,HH
Pk,VV
Pk,HH
Pk,VV
Pk,HH
Pk,VV

3.1.4

Cross- and Co-Polarization Ratios

This section presents the behavior of polarization ratios, which
is deﬁned in Section 3.1.1. From the deﬁnition of CPR,
higher CPR indicates the goodness of vertical transmission
against horizontal transmission. The severe loss of horizontal transmission in a Microcell in campus scenario is evident from the high CPR (4.5 [dB]). The main interest of
this section is to know whether XCPRs are dependent on
delay, azimuth and elevation DoA or not. To check the dependencies, the Spearman’s rank correlation coefﬁcients arecalculated, and shown in Table 5. Blue color indicates the
positive correlation, and negative correlation is indicated as
red color. Overlooking the table, no clear dependencies are
identiﬁed for all results. Especially in Tokyo measurement,
no dependence on the azimuth DoA is evident. It is interesting that the elevation dependencies of Kawasaki and Tokyo
measurement show opposite correlation (positive/negative).
This difference may come from the above mentioned unknown mechanisms, but it is difﬁcult to generalize and determine the causal factor only from these results. The further
analysis is remained as future work.

4

Conclusion and Future Works

4.1

Conclusion

where d is the distance between BS and MS, αmean , αdif f
In this study, multipath cluster polarization model is preand β are the site-speciﬁc constants. Table 3 shows the comsented based on the measured data from multiple scenarios
parison of ﬁtting parameters among three measurements. Com23

HV

VH

VV

Linear fit (HH)

- 60

−60

Cluster Gain [dB]

Cluster Gain [dB]

−80
−100
−120
−140
−160

Linear fit (HV)

Linear fit (VH)

−60

−60

−80

−80

Cluster Gain [dB]

HH

−100
−120
−140
−160

−180

2.35

2.4

2.45

2.5

2.3

2.35

2.4

BS−MS Distance: log (d) [d in m]

BS−MS Distance: log (d) [d in m](a) Macrocell, Kawasaki City.

(b) Macrocell, Ota ward, Tokyo.

10

−100
−120
−140
−160

−180

2.55

Linear fit (VV)

10

−180
1.7

1.8

1.9

2

BS−MS Distance: log (d) [d in m]
10(c) Microcell, In campus, Tokyo Tech.

Figure 3: Cluster gain behavior against BS-MS distance
1. Inﬂuence of coherent ‘true path’ in a estimated ‘path’
The estimated path by RIMAX is considered as sum(or split) of ‘true path’. So the effect must be taken into
account, especially in a cluster-based modeling. Morespeciﬁcally, cluster variation characteristics is needed
to be analyzed by continuous measurement with tracking functionality.

Table 5: Correlation coefﬁcients

Correlation coefficients Nakahara Minami 3 Campus
Delay
AzimuthMS
ElevationMS

XPRBSVXPRMSVXPRMSHCPRXPRBSHXPRMSHCPRXPRBSVXPRBSHXPRMSVXPRMSH
CPR

0.06

0.15

0.14

0.08

-0.04
-0.07

-0.15

0.11

-0.11

0.23

0.01

0.05

-0.2

0

-0.04

-0.12

0

-0.02

0.21

0.02

-0.05

-0.17

0.16

0.01

0.07

0.24

-0.04

-0.13

0.21

-0.04

0.02

0.19

0

-0.21

-0.02

-0.04

2. More detail analysis to know the dominant depolarization factors in a Macrocell scenario is necessary. Thisshould be clariﬁed by conducting more object-speciﬁc
measurement.

References
at 4.5 GHz. The contribution of this thesis is summarized as[1] V. Eiceg, H. Sampath, and S. Catreux-Erceg, “Dualfollows:polarization versus single-polarization MIMO channel
measurement results and modeling,” Wireless Communi1. The path loss behavior of vertical and horizontal transcations, IEEE Transactions on, vol. 5, no. 1, pp. 28–33,
missions are observed as different between two MacroJan. 2006.
cell scenarios. Although this study can not determine
the speciﬁc mechanisms, the results indicated the ex- [2] M. Shaﬁ, M. Zhang, A. Moustakas, P. Smith,istence of unknown but inﬂuential propagation mechA. Molisch, F. Tufvesson, and S. Simon, “Polarized
anism, which come from upper horizontal direction.
MIMO channels in 3-d: models, measurements and muIn Microcell scenario, the horizontal transmission suftual information,” Selected Areas in Communications,
fer severe path loss due to the reﬂection from verticalIEEE Journal on, vol. 24, no. 3, pp. 514–527, Marchwalls. This behavior is theoretically explained, and2006.
further the hypothesis testing results insisted that there
is no counter-evidence on this subjective observation. [3] [Online].
Available:
http://www.channelsounder.de/ruskchannelsounder.html
2. The inﬂuence of coherent paths in a cluster is identiﬁed as signiﬁcant. The results of this study shows that [4] A. Richter, “Estimation of radiocchannel parameters:
Models and algorithms,” Ph.D. dissertation, Ilmenau
the superposition of paths in a cluster should be based
University of Technology, 2005.
on a coherent sum approach.
3. XCPRs are identiﬁed almost independent on the chan- [5] L. Y. Materum, “Mobile station spatio-temporal multipath clustering of MIMO channel estimates of an urnel parameters such as delay, azimuth and elevation
ban cellular environment at 4.5 GHz,” Ph.D. dissertaDoA. However, the mean CPR indicated the bettertion, Tokyo Institute of Technology, 2009.
transmission of HP in Macrocell scenario, while VP
in Microcell campus scenario.
[6] W. F. Tsen and H.-J. Li, “Correlations and channel capacities for multi-polarized MIMO systems,” in The
20th Personal, Indoor and Mobile Radio Communica4.2 Future works
tions Symposium 2009, Tokyo, Japan, Sept, 2009.
In the future, following items must be analyzed.
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ߪⅣႺࠞ࡞࠹ࠍ๘Ꮉᵹၞߦㆡ↪ߒ㧘ᵹၞߩS࿅૕߆ࠄߥࠆද௛ߩᨒ⚵ߺߢⶄวⅣႺᖱႎߩ⫾Ⓧࠍⴕߞߚ㧚߹ߚ᳓ᢥ੍᷹ߩߚ߼ߦ@      D  ࡕ࠺࡞ࠍዉ౉ߒߚ㧚߹ߚⅣႺࠞ࡞࠹ߪ࿾ၞߢ೑↪ߐࠇ㧘ᧄ⎇ⓥߢⅣႺࠞ࡞࠹ߩ߭ߥᒻࠍ૞ࠅ਄ߍߚ㧚&   I& '                  ,  ;    #    ;     '     I&  ;     ' & ;     -;K(  , Y'   " Z   & &     - %

 '  

& '- C  ,      &    & "   &       

    ;      - C   ''  

&           ,               - %         &       ' &  '      - * 

 &            

& '- ) ', & &                - %      '           '   #   , % # -

 &<      I'   &&  B!   $# 1 ' - % ,         

'   "                   ;      & &        &  &           &I      & '- I  '   '      , '    

A   '    & &          &-      -H & & &   *    '        -  &&       , D     @  '  (- % &  #  & '              & '       ;             K '                (M*  () ,  

  &   & - % " K    F2 H    ;

 &&    (M*- "  +H ' & ,  [    #                  & FH  &    '   '               , M - ) ',  &   *    D    #           M*I   F                - M'  #      ," H,         

F+H           &  '    '        ,  ,           ,  & '         '  K      -      ' & F   " H,  ',          ! 
   

F?H :;       &    &     ;-        ' )    

3K\VLFDOPRGHO 
 

 $
 *  $ *,60DSV

%  䊶& '(  #    
"
#! 

 2  $  $   $# 1   ($!#  -$ 6  

25

2  -$ 6  * 

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+  

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/HJHQG/HJHQG,#  '

, '/'082' /'0)*+  

9 ' 39 

,

9 ' 39 ) 

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2 ' 4 #  !     /HJHQG/HJHQG:;..7'5</=0䎳䏈䏕䏙䏌䏒䏘䏖䎃䏓䏄䏙䏈䏐䏈䏑䏗䱊<+  

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!2'  ;..6,'?A;-5;B..4䎫䏌䏊䏋䎃䎝䎃䎖䎓䎑䎛䎚䛐

4' +4' +3

䎯䏒䏚䎃䎝䎃䎕䎛䎑䎗䎙䛐+  

䎤䏌䏕䎃䎷䏈䏐䏓䎑䎃䎖䎖䎑䎗䎔䛐+  
,) 
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2 9     76

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 䎕䎙䎑䎘䎜 ,  䎯䏒䏚䎃䎝䎃䎕䎘䎑䎛䎚䛐䎯䏒䏚䎃䎝䎃䎕䎛䎑䎗䎙䛐

䎕䎛䎑䎗䎙
䎕䎜䎑䎓䎗

䎤䏌䏕䎃䎷䏈䏐䏓䎑䎃䎖䎖䎑䎗䎔䛐,'?A;-5-B..4䎫䏌䏊䏋䎃䎝䎃䎖䎓䎑䎔䎖䛐䎯䏒䏚䎃䎝䎃䎕䎙䎑䎜䎛䛐

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䎕䎚䎑䎙䎖

䎕䎘䎑䎚䎗

 R #+ F  H- %       #  A'               "    䎫䏌䏊䏋䎃䎝䎃䎖䎓䎑䎛䎚䛐䎕䎙䎑䎘 $   F  H- %

'      ?R, & &      ;   䎫䏌䏊䏋䎃䎝䎃䎕䎛䎑䎛䎔䛐䎤䏌䏕䎃䎷䏈䏐䏓䎑䎃䎕䎛䎑䎙䎕䛐䎕䎙䎑䎗䎛 #  A'  F  H           - *   ,'?A;-5;B..4,'?A;-@B..4

䎖䎓䎑䎘

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       (  ( ) *   + ,- +. .%%  ( / . .%%/  . .%% (  ]) / %%0  - @   ,     '    '     - (  ,       +  %&    &   '       &      - %          &  - % ,       #  A'   ?+      && &                  F  %H- %    &   &   '   

' -F  H 

 #  I I# , ;K  ;  ,  # '    &    $         - & '        "         &     '   '      I   I - % 

'                #  '  ;   F  H- (&& <  !  &   - (  - (  ,  '  '     &    ;      #  I I# , A'   ,     & - %   F%:(%H- %       '  =         &            - C  ,   '   &   

C #     $   % # :'      "                   &&      &,     ',     & &        " -    @D  -           &  &     & - %                &     &   

'            -  &  

& '   ' '     -    '- %        &    '(     #  I I#   C #    @       D  F@DH    $ - %  &    '            &    &                     & -         &       '       

'   " - %   & +I+S- %  &       '     '   "     

=                - '    % # :'            %   F%:(%H- $    *                  ,  &    " - ) ',@D     & '    ,            FH' &        '       <(  F@D\ H       &           ,

'   "  & '    F+H

 &                  

&        &&   & - D&     '
F@D\(H        ,       %   F  H-& '- % #         &  ' & %               &   

&     ;                 & - *&

'   "         

 @D &      - +!    % # (    &&    ' -',&   &        Q   F@DQ\ H  '     "   ,    &    #  ' ,         #   &  - %     '    



    '         - %  &   !"*

 

         ,# #>- %  &     '  & -  $    '  # '                    ++       +   -+.   (/ 1 . 2' (0 - %      &  ,  '  
&        ,            -27

@DQ\@D\@D\(%A $'D&  C  A  F?UH

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+

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 "     '    # & F7  H- %  & '     

D&   FU-H  X'  & '              & ,  &    &   &   '         "  & ' '     #   '  - *  ,               #  I
 I# , % #  &  

 @D\

  B ) %              &     @D\   &- %     

H '    "    & &        &&   &  

  1+

+U    ,      +    -./     , -, "   #, (-  W  , - &  [ *   %   +!  &               -D    @    '   A#     M*,

 @D\(    & ', @D\(  #

@D\ &    &         '  .+/ >   3-㧘 A  :-㧘 )   :-㧘  )-㧦(           - *      "   A     ,  "  A  ' & '               , - "  A     㧘 - +Q㧘 +-  &'    , &' F%A H-  < .?/ 3 , W-, , M-, "    , W-    , %- !' &       QT     @D\(- %    < @D         - %           &&   ,   ,-!,  -, &&-+R!I+S, +-./ M  &   ' *      & ! M F'  H,

   # &-+?- 

.!/ M  &   ' *      & +! F&  

% "         - C  "   '  


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  C  #H, +?-.Q/ M  &   ' *       *    F

F&UU--- -X&U^#   UX U& XU #  UH-  ,M   :H, SS- "         % # *   %   F% #%-H  " 

4  

&           - %           '   A#     +Q- &'     '-& &

> U I, % +   $ D  , &&-RI,   ,      &   &           +H % "    ' &   #   -             +!  &         - &       ' &   -&   ,       , &         %'  $      I 

'  '  --   & '     &  @D\(    ( 

 #  I I#    !- + , +S-       

&      &    @DQ\ - ) ', 

'  % # (    %  - '  &     

Q'         %                 B          F   0- *    &        

 #  I #,   &   ;     #  I I# 

.R/        $  (   '    $      - *  &    F   H, %  #       & &  $ ,% # %-,           "    & ' ' -+, S!-           &&     & & - %,       !


 

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+ICIS   C*    _  +RI" IS

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+I IS*   ,   _ C #A     #  I I# I" I

A #    #   #  _

28    _        &   &   

  _

 I@ #+I IS  _     M  '

MECHANISMS OF URBAN EFFECTS ON LOCALIZED HEAVY RAIN IN
TOKYO USING NUMERICAL SIMULATION
Student Number: 08M18097

Name: Ryo SHIMOJUSupervisor : Manabu KANDA

᳇⽎ࡕ࠺࡞ࠍ↪޿ߚㇺᏒဳ㓸ਛ⽕㔎ߩ⊒↢ࡔࠞ࠾࠭ࡓߦ㑐ߔࠆ⎇ⓥਅ㊀ ੫ᧄ⎇ⓥߢߪ㚂ㇺ࿤ߢᄐቄߦ⊒↢ߔࠆዪ࿾⊛㓸ਛ⽕㔎߳ߩㇺᏒᓇ㗀ࠍ⹏ଔߒ㧘⊒↢ࡔࠞ࠾࠭ࡓࠍ
᣿ࠄ߆ߦߔࠆ੐ࠍ⋡⊛ߦ㧘᳇⽎ࡕ࠺࡞ࠍ↪޿ߡ⸃ᨆࠍⴕߞߚ㧚ߘߩ⚿ᨐએਅߩ੐߇᣿ࠄ߆ߦ
ߥߞߚ㧚ԘㇺᏒဳ㓸ਛ⽕㔎ߩ⊒↢ߦㇺᏒᐞ૗ᒻ⁁߇ᄢ߈ߊᓇ㗀ߔࠆ㧚ԙㇺᏒߪᶏ㘑೨✢਄ߦቅ┙⊛ߦ⊒↢ߔࠆⓍੂ㔕ߩߺߢߥߊ㧘ౝ㒽ߩᾲ߿᳓⫳᳇ߩಽᏓߦ߽ᓇ㗀ࠍਈ߃ࠆ੐ߢ㧘㚂ㇺၞߢߩ㓸ਛ⽕㔎ߦ❬߇ࠆⓍੂ㔕ࠍ⊒↢ߐߖ߿ߔ޿ᄢ᳇႐ࠍᒻᚑߔࠆ㧚ԚㇺᏒߩᾲߣㇺᏒᐞ૗ᒻ⁁ߪ
Ⓧੂ㔕ㅢㆊᤨߩ㒠᳓♽ߦ߽ᓇ㗀ࠍਈ߃㧘ߘࠇߙࠇ㒠᳓㊂ࠍჇടߐߖࠆ㧚

1. Introduction

Table 1 Numerical Settings for the 3 runs
Localized heavy rainfall in summer has attracted attention
in recent years. Rainfall sometimes reaches more than 20-50
mm per hour and this can cause urban floods. These localized
rainfalls are still difficult to predict. Urbanization is suspected to
be one of the key factors that generate localized heavy rainfall.
However, this has not been clarified. To investigate the urban
effect on rainfall, implementation of various urban parameters
to a weather forecast model is needed. Recently, urban canopy
model (UCM) and Anthropogenic heat and vapor (AH࡮AWV) have been developed for integration in weather
prediction models. Yet, urban geometry has not been includedin these models. Modification of the displacement height by theurban geometry is considered in this study. Displacement
height was calculated from detailed building information ofTokyo and thus represents the “bulk building landscape”.
Detailed displacement height was added to the original terrain
map to further define the boundary conditions of the weather
prediction model.
Firstly, localized heavy rainfall days were extracted andstatistically investigated, and the displacement heights forTokyo were calculated.
Secondly, three simulations of a typical sea breeze day were
executed with the following settings (Table 1): RUN1 includes
UCM, AH࡮AWV and the displacement height map; RUN2
considers only UCM and AH࡮AWV; while RUN3 does notinclude any of the above-mentioned models that the previous 2
simulations use.
Thirdly, 16 occurrences of one-day localized heavy rainfall
occurring between 2005 and 2008 were simulated, using anurban and a non-urban cases for simulation. By comparing theresults of these two case simulations, various mechanisms of
localized heavy rainfall affected by urbanization were analyzed.

RUN1
RUN2
RUN3

Displacement
height
○
×
×

UCM
○
○
×

AH࡮
AWV
○
○
×

Finally, how rainfall changes when rainfall cell goes overan urban area was investigated.

2. Statistics on anomaly of heavy rainfall
Ensemble-averaged rainfall was examined to detect any
anomaly of heavy rainfall and its relationship with the urban
landscape. Historical localized heavy rain days were extracted
from rainfall data of the Tokyo Construction Bureau River
Department, including six summer periods (July - September)of 2000-2006. The following are the conditions set for rainfall
data extraction (Fujibe et al. [1], Nakanishi et al. [2]) :1) Maximum daily temperature is above 30 C.
2) The day when rain starts anytime from 12:00 to 21:00 andends before 24:00.
3) 1-hour rainfall is more than 15 mm.
4) The rainfall events under the influence of the synoptic
weather were discarded.
Rainfall for each point were summed up and averaged. Spatial
deviation from corresponding averages were plotted to see thespatial anomaly of rainfall (Figure 1). Wards A, B and C in
Figure 1, which are on the north-west side of high rise buildingsin Shinjuku (D in Figure 1), had more than 40% rainfall thanother wards. The general sea breeze in this area is a north-west
one coming from Tokyo Bay, crossing Shinjuku, making thesethree towns to be downstream of Shinjuku. This result implies
high-rise buildings enhance rainfall cell in downstream regions.

29

4. Numerical analysis

B.Itabashi ward
A. Nerima ward

To evaluate how urbanization affects localized heavy rain,
Weather Research & Forecasting Model (WRF) was used.

4. 1 Urban effect on sea breeze from Tokyo Bay
August 10, 2004 was used for analysis. This day is typicalsea breeze day and localized heavy rain occurred aroundShinjuku. The 3 simulations previously mentioned were run.
A conspicuous difference for the wind and temperaturefield is seen between RUN1 and RUN3. Figure 4 shows wind
at 10 m and temperature at 2 m at 12:00. In RUN1, sea breeze
penetration was delayed. Also, the sea breeze, which contains
AH of Central Tokyo, changes direction and blows towards
Nerima (encircled in white in Figure 4). This is due to the dragcaused by urban geometry and thermals. This results in a higher
temperature for the north-west side of the 23 Wards of Tokyo
for RUN1 with respect to RUN3.
If there is no urban area, the sea breeze and water vapor willquickly move inland.

D.Shinjuku ward
C.Nakano ward



fig.1 Anomaly of rainfall in 23 wards

3. Dataset of urban displacement height
Urban geometry can be represented by displacement height.
Displacement height of Tokyo was calculated by using thefollowing equation (MacDonald et al.[3]):d  H (1  4.43p

( p 1)) ‫ޓޓޓޓޓ‬(1)

where H is the average building height and λp is building-toland ratio. To calculate these parameters, information of floor
space, number of stories and building use type were used.
Figure 2 shows the calculated displacement heights for Central
Tokyo. Almost the whole area is elevated more than 2m. Largecity areas E, F and G in Figure 2 are especially elevated at 12 26m. These areas have many closely packed high-rise buildings.
From Figure 3, which presents the sum of the displacement
height and terrain height, the height around Shinjuku (H in
Figure 3) is more than 40 m. The altitude within a 3-km radius
of Central Shinjuku looks like an isolated mountain.
E. Ikebukuro

F. Shinjuku

G. Otemachi

[͠]

36

24
fig.4 2 m temperature and 10 m wind field at 12:00(left:RUN1, right:RUN3)

[m]
24

On the other hand, the difference between RUN1 and
RUN2 is most obvious at the time sea breeze goes throughShinjuku (Figure 5). A change in wind direction creates aconvergence zone only for RUN1 (encircled in white in Figure5). At the same time, a strong updraft (6m/s) and cumulus wereanalyzed corresponding to this area. This results show urban
geometry clearly affects the generation of localized heavy
rainfall.

0

fig.2 Distribution of displacement height of Central
Tokyo

45
Altitude[m][m]60

H. Shinjuku
Shinjuku
0

fig.5 10 m wind at 12:44 (left: RUN1, right :RUN2)
Domain is green square in fig.4

fig.3 Sum of displacement height and terrain height30

0[kg/kg]
0.003

4.2 Case analysis of localized heavy rain
To extract various mechanisms of urban effect on localized
heavy rainfall, a total of 16 localized heavy rain events werenumerically investigated. Two cases were considered, the urban
case, which considers UCM, AH࡮AE and displacement
height, and the non-urban case (Table 2). The urban effect wasseen in 9 rainfall events. Moreover, the mechanisms of urban
effect were classified into 3 types. Following section shows
examples of each type.

0
fig.8 Difference (urban – no urban) of 2 m water mixing
ratio (left:1:00, right:8:00 16th August)
Vector represents wind of urban run-0.003

Table 2 Numerical Settings for Case Analysis
4.2.2 Urban effect on local scale (Rainfall cell develops
on sea breeze front)
Urban
Rainfall at 11:14 29th July 2008 is shown in Figure 9. Sea
No urbanbreeze from Tokyo Bay was analyzed. Rainfall at the sea
breeze front was resolved only in the urban run (encircled in
4.2.1 Urban effects on Kanto scale (Rainfall cell develops
green in Figure 9). Before sea breeze penetration, thein inland)thtemperature of Central Tokyo is higher for the urban run
Rainfall at the Kanto region at 13:00 16 August 2008 is
relative to the non-urban run. Urban heat effect enhances theshown in Figure 6. The rain band moved from mountain area to
updraft at the sea breeze front, as simulated by Yoshikado
the east side. The Rainfall cell developed and heavy rain was(1992). The results of this study suggest that the sea breeze
resolved at Central Tokyo only in urban run.
[mm] from Tokyo Bay generates isolated Rainfall cell in Central
40
Tokyo as an effect of urban heat.
Displacement height
○
×

UCM
○
×

AH࡮AWV
○
×

[mm]

1.0

0

fig.6 Rainfall in 30min. at 13:00 (left: Urban, right: No urban)
In the previous day, the sea breeze for non-urban run penetrates
more than the urban run (Encircled in green in Figure 7). Sea
0.0
fig.9
Rainfall
in
2
min.
at
11:14
breeze advected water vapor and makes inland atmosphere(left : Urban, right : no urban)
unstable. The Rainfall cell in the non-urban run was moredeveloped than that for the urban run. As a result, dry and cool
4.2.3 Urban effect on both Kanto and local scale
outflow of rainfall cell sweeps wet air out of the Kanto Plain for
Both Kanto and local scale effects were seen at 4th August
the non-urban run. On the other hand, in urban run, wet and hot
2008. Mid morning rainfall at the sea breeze was analyzed in
air remains near Central Tokyo at night and was advected
the urban run (not shown). At the same time, Rainfall cell
toward mountain area at dawn by valley wind (Figure 8).
developed at mountain area (encircled with green in Figure 10).
Therefore, atmosphere of mountain area was unstable in the
Before Rainfall cell in mountain area was formed, amorning of the 16th August 2008.
[mm]convergence zone developed for the urban run only (encircled
100
in red in Figure 11). When atmosphere over the Kanto Regionis stable, the sea breeze and valley wind converge. The Kanto
Plain was completely covered with the southerly wind(extended sea breeze) during the daytime. In theory, thisextended sea breeze becomes weak as the night comes. But for
the urban run, the thermal low due to the heat island leaves a0strong extended sea breeze in the night. As a result,
fig.7 Wind field at 15:00 15th August2008
convergence can be formed easily between the extended sea(left: urban, right:No urban)

31

[mm]
20

[mm]
60

[mm]
8

0

0

0
-60
-8
fig. 12 Difference of total rainfall from 12:00 to 14:30(left: RUN1-RUN2, right : RUN2-RUN3)

Table 4 Average altitude and variance of height of each run

fig.10 Rainfall in 30 min. at 10:30 4th August(left: urban, right: No urban)

RUN1(CNTL)
RUN2
RUN3
RUN4

fig.11 Wind field at 5:00 4th August(left : urban, right : no urban)

RUN2/CNTL

0.99㩷

The conclusions of this study were summarized as follows:
1. New simple method to reflect urban geometry to a weather
forecast model was devised. Urban geometry can trigger
the break out of localized heavy rainfall in Tokyo.
2. When localized heavy rainfall occurs, urbanization,
specifically by drag due to urban geometry and heat, affects
the Kanto scale weather more than the local scale.
3. When Rainfall cell goes over an urban area, it can develop
and increase the rainfall at least 100%, mainly by urban
heat. Although the effect of geometry can increase rainfall
by about 20%, the average and variance of displacement
height are proportional to the amount of rainfall and have arelationship with rainfall distribution.


Table 3 Numerical settings for Rainfall cell verification runs

RUN1(CNTL)
RUN2
RUN3

○
○
×

○
×
×

○
×
×

RUN4/CNTL
1.02

5. Conclusion

When Rainfall cell goes over an urban area, how eachurban factor affects rainfall was investigated. Weather datawhen rainfall cell develops at mountain area was used for eachruns as initial condition. Settings are shown in Table 3.

AH࡮
AWV

RUN3/CNTL
1.00



4.3 Development of rainfall cell going over urban
area

UCM

Variance of height297.39289.73300.82327.41
Table 5 Ratio of total precipitation of Tokyo 23 wards

breeze and the outflow of rainfall cell generated at the mountain
area.
The Kanto and Kanto + local scale effects on rainfall are
manifested in seven out the nine cases (78%).

Displacement
heightAverage altitude (m)
19.22
19.08
19.34
20.03

Difference of total rainfall from 12:00 to 14:30 is shown in
Figure 12. The UCM and AH࡮AWV increase rainfall 50 mm
(100%) or more above Central Tokyo. On the other hand, thedisplacement height increases about 6 mm (20%) at theleeward of Shinjuku.
To clarify the relationship of displacement height andrainfall, additional experiment was done. The distribution of
displacement height was modified (not shown). Table 4 shows
the average and variance of the altitude for each run. Results
show that the average and variance of altitude are proportional
to the amount of rainfall (Table 5). Moreover, they are closely
connected to the locality of heavy rainfall (not shown).

32

References[1] Fujibe,F.,Sakagami,K.,Chubachi,K.,Yamashita,K.,:Surface wind
patterns preceding short-time heavy rainfall in Tokyo in the afternoonof midsummer days,Tenki,49,395-405,2002[2] Nakanishi,M., Hara,Y.,:Characteristics of local winds associatedwith the intensification of short time rainfall in the Tokyo urban area in
the afternoon of summer days,Tenki,50,91-102,2003.
[3] Macdonald, R.W., Griffiths, R.F., and Hall, D.J., 1998. An
improved method for the estimation of surface roughness of obstacle
arrays, Atmos. Environ., Vol. 32, pp.1857-1864.
[4] Yoshikado, H., : Numerical study of the daytime urban effect and its
interaction with the sea breeze. J.Appl. Meteor., 31, 1146-1164,1992.

Study of propagation channel for wireless sensor network in the vicinityof human bodyStudent Number: 08M18105 Name: Yuuki TERAO Supervisor: Jun-ichi TAKADA

人体近傍の無線センサネットワークにおける伝搬チャネルに関する研究
寺尾優輝

近年，人体上での無線通信への関心が高く，医療，ヘルスケアのみならずエンターテイメントとしての需要も高まっている．コンピュータネットワークに関する国際標準化機関である IEEE（米国電気電子技術者協会）802 標準化委
員会の下に設置された 802.15.6 タスクグループ (TG) では現在，医療およびヘルスケアを主用途としたボディエリア
ネットワークの標準化が進められている．伝搬チャネルモデルは，無線通信方式の標準化プロセスの中で最初に規定する必要があり，同 TG においても BAN 標準チャネルを 2008 年 11 月に制定している．しかしながら，このチャネルモデルでは人体がチャネル特性に与えるメカニズムは考慮されておらず，使用するアンテナや被験者の影響を含んでいる．本研究では伝搬チャネル特性からアンテナの影響を切り分けるために人体による受信レベル変動のメカニズ
ムを究明する事を目的として，主に人体にアンテナを装着することによって生じるインピーダンス不整合， アンテナ
放射パターンの変化などについて考察する．

1

Introduction

A growing interest in the body centric wireless communication technology has been taken in various applications including medical, health care and consumer
electric industry for entertainment and wearable technology for fashion as well as space and military applications, the body area network (BAN) has become
more important to connect devices around the humanbody. Currently, IEEE 802.15.6 task group on wirelessbody area networks is discussing the standardizationof body area network mainly for medical and healthcare applications[1]. Among a few categories of BAN,
this research focuses on the channel between body surface and body surface. Firstly, path loss model shouldbe established among the standardized process in wireless communication standards, which was establishedon November 2008 in TG. However, in existing pathloss model, the antenna eﬀects are treated as a partof the channel, because it is diﬃcult to separate theseeﬀects from the measured channel responses in BAN.
The problem is that the channel models depend on theantenna and individual test subjects. In these models,there is no reproducibility when the antenna and thetest subject are diﬀerent, and consequently the linkbudget prediction will be diﬃcult. Therefore, moregeneralized channel model channel model is necessary.
This research to de-embed the antenna eﬀects fromhuman body focuses on investigating the mechanismof variation of the received signal level due to human
body, and studies changes of impedance matching andradiation pattern of antenna due to proximity to human body surface.

2

sen among the candidate frequencies in IEEE 802.15.6.
A vector network analyzer (VNA) was used to measurepath loss and two test subjects were employed for thismeasurement. The speciﬁcations are shown in Fig.1.
From measured data, the path loss model was obtained
from two test subject data. And I drew an approximation plane to compare with the measured data as
follows.
P L(d, f ) = α · log10 (d) + β · log10 (f ) + γ(1)

To obtain the coeﬃcients α, β, and γ of the approximation plane of Eq.(1), LMS algorithm was used. The
obtained values for α, β, and γ are 14.71, 3.96, and2.92 respectively. When antennas are attached on human body surface, there are some mutual interactions
between body and antennas, i.e. distance variabilityby the body movement, distortion of antenna radiation
pattern due to proximity to human body, polarization
rotation by the human body motion, propagation loss
due to the absorption, impedance mismatch of the antenna. So far, it has been considered that separating
antenna eﬀects from the measured channel responses
in BAN is very diﬃcult. Therefore, the antenna eﬀects
are generally treated as parts of the radio channel. In
this path loss model which was obtained in this time,the model only focused on distance variation between
Tx and Rx antennas. Therefore, all the factors havebeen mixed up. The problem is that this channel models depend on the antenna eﬀects and individual diﬀerences of test subjects. The changes of radiation pattern
and reﬂection coeﬃcient of antenna were investigatedto examine the antenna eﬀect from this model, when
the antenna was attached to human bodies.

Path Loss Model

In order to develop the BAN channel model, the propagation characteristics are represented by path loss.
Measurements were conducted in an oﬃce environment.
The frequencies of 444.5 MHz for wireless medical telemetric service (WMTS) in Japan, 611 MHz for WMTS
in US, and 953 MHz for active RFID in Japan were cho33

3

Antenna Radiation Pattern

3.1

Measurement Equipments

The measurement was conducted in radio anechoic chamber to remove the environment eﬀects. The setup consisted of VNA, transmitting and receiving antennas,

Table 1: Measurement speciﬁcations
Parameters
Values
VNA
Model: 8720ES(zero span mode)
Number of points
801
IF bandwidth
300Hz
Tx Antenna position
navel (ﬁxed)
Rx Antenna position 10 positions on bodyFrequency
444.5, 611, 953 MHz
Test subjects
2 persons(a) Free-space measurement(b) On-body measurement

䎚䎘䎚䎓Figure 2: measuremant

Path loss [dB]

䎙䎘
䎙䎓
䎘䎘

Z

䎘䎓
䎗䎘

Y

䎗䎓
䎖䎘
䎕

X

䎕䎑䎘

Distance [log10([mm])]

Distance [log ([mm])]
10

䎖
䎖䎑䎘

䎕䎑䎙䎘
䎕䎑䎙 444.5

䎕䎑䎚䎕䎑䎚䎘 611䎕䎑䎛

䎕䎑䎛䎘

䎕䎑䎜

䎖

䎕䎑䎜䎘
953

Frequency [MHz]Freq [log ([MHz])]
10

Figure 1: Path loss model

Figure 3: Coordinates for chip antennaantenna rotator, personal computer to control and tosynchronize the VNA and rotator, and to record themeasurement data output. The antenna pattern measurement was conducted on body in lay-down position.
The receiving antenna is a standard dipole antenna,
while the antennas under-test are three surface mountable dielectric chip antennas operating at for 444.5
MHz, 611 MHz, and 953 MHz, which were set in thetransmitter side[4]. In order to aboid the inﬂuence
from rotator, I utilized polystyrene form (100cm ×200cm × 50cm). The eﬀect of body to the antenna
pattern was investigated by comparing the antenna
without spacer (0 mm) and with 15 mm spacer. In
order to make a space between antenna and human
body, polystyrene foam, which is with very low permittivity of 1.03–1.05, was used as a spacer. To increase the validity of measurement data, the antenna
patterns for free-space and on-body of two male subjects are observed. The eﬀect of body to the antenna
pattern was investigated by attaching the antenna in
two positions namely as Y Z-plane as shown in Fig.2.
Co-polarization and cross-polarization patterns were
measured in each of the planes.

3.2

X
AntennaY

Z

Figure 4: Coordinates for body and antenna

on-body pattern was almost the same of free space pattern when antenna was separated by a 15 mm spacer.
Antenna patterns for two subjects as well as in freespace at 611 MHz in Fig.6. When antenna was directly
attached on human body surface without spacer, a perceivable diﬀerence of patterns was found by comparing
S1 and S2 results. The diﬀerence of ripple comes fromindividual breathing period. In this case, one measurement took 207 seconds. However, by using 15 mm
spacer, it was eﬀective to replicate the antenna radiation pattern, which does not depend on test subjects.
Moreover, in comparison with 0 mm case, it is possible
to say the pattern diﬀerences tend to vanish and become homogeneous. Therefore, it would be an useful
information in case of practical system designing.

Results

Antenna and human body coordinate are shown in
Fig.3 and Fig.4 respectively. Figures 5 are radiation
pattern of Y Z-plane. As a general trend, it was assessed that on-body pattern without spacer has a smaller
gain compared to free-space possibly due to the absorption of human body. while, the antenna gain of
34

4

Impedance Matching

To investigate the inﬂuence of human body to antenna
matching, dipole and chip antennas were used for thismeasurement. Human body and antenna coordinate
are shown in Fig.7. In this measurement, the frequencies of 444.5, 611 and 953 MHz were used, and two test

o

o

90

o

䣈䣴䣧䣧䢢䣕䣲䣣䣥䣧䣑䣰䣤䣱䣦䣻䢢䢲䣯䣯䣑䣰䣤䣱䣦䣻䢢䢳䢷䣯䣯

o

60

120

o

30

o

-45

180

-30

-45

180

z


-30

-150

o

0

o

o

o

30

150

ʔ
o

-15

dBd

0

z

-150

o

-120

o

-60

o

-90

(a) 444.5 MHz x-polarization(a) Spacer thickness 0 mm

o

o

90

o

䣈䣴䣧䣧䢢䣕䣲䣣䣥䣧䣑䣰䣤䣱䣦䣻䢢䢲䣯䣯䣑䣰䣤䣱䣦䣻䢢䢳䢷䣯䣯

o

60

o

o

-45

-30

o

o

o

o

-45

-30

ʔ
o

-15

0

dBd

z

z

o

o



o

-30

-150

o

-120

30

180

0

-30

-150

o

150

ʔ
o

-15

dBd

䣈䣴䣧䣧䢢䣕䣲䣣䣥䣧䣑䣰䣤䣱䣦䣻䢢䣕䢳䣑䣰䣤䣱䣦䣻䢢䣕䢴

o

60

o

30

180

90

120

o

o

150



o

-30

-90

120

ʔ
o

-15

o

-60

o

-30
dBd

o

o

-120

䣈䣴䣧䣧䢢䣕䣲䣣䣥䣧䣑䣰䣤䣱䣦䣻䢢䣕䢳䣑䣰䣤䣱䣦䣻䢢䣕䢴

o

60

o

o

o

150

90

120

o

-60

-120

o

-60

-90

-90

(b) 611 MHz x-polarization(b) Spacer thickness 15 mm

o

o

90

120

䎩䏕䏈䏈䎃䎶䏓䏄䏆䏈䎲䏑䏅䏒䏇䏜䎃䎓䏐䏐䎲䏑䏅䏒䏇䏜䎃䎔䎘䏐䏐

o

60

o



o

Figure 6: Comparison of antenna radiation patterns
for diﬀerent samples at 611 MHz

o

150

30

φ

Human body-45 -30 -15 o
0
dBd

o

180

Dipole antenna

Y
o

o

-30

-150

-120

Z

o

o
o

-60

Z

-90

(c) 953 MHz x-polarization

X

Figure 5: Antenna radiation patterns at diﬀerent frequencies

subjects were examined for this measurement. The
antenna was ﬁxed on navel with styrene foam and tripod stand, which is shown in Fig.8. The measured reﬂection coeﬃcients using dipole and chip antenna areshown in Fig.9 (a) and (b) respectively. The inﬂuence
to impedance matching has been almost completely ignored when the antenna is separated by using 15mm
spacer from the human body surface.

4.1

Frequency characteristics of reﬂection coeﬃcientFrequency characteristics around the center frequency
were investigated to obtain the variation mechanismof reﬂection coeﬃcients. Frequency characteristics ofthe reﬂection for dipole antenna and chip antenna areshown in Fig.10 (a) and (b), respectively. In the Fig.10
he resonant frequency was enormously shifted from
35

Z
Y

Y

X

Figure 7: Coordinates for body and dipole antenna

free-space, when antenna was attached without spacer.
The frequency shift for dipole antenna is more obvious
as the eﬀective wavelength changes due to the human
body, while the resonant frequency of chip antenna isdominated by the internal structure and less inﬂuenced
by the human body. When antenna was separated in
15 mm from human body surface change of reﬂection
characteristics is negligibly small. It would be useful
information in practical system design.

䢷䢲䣆䣫䣨䣨䣧䣴䣧䣰䣥䣧䢢䣤䣧䣶䣹䣧䣧䣰䣨䣴䣧䣧䢯䣵䣲䣣䣥䣧䢢䣣䣰䣦䢢䣱䣰䢯䣤䣱䣦䣻䢶䢲䢲䣯䣯䢤䢯䢴䢺䢵䣏䣊䣼䢤䢵䢲䢳䢷䣯䣯䢤䢯䢴䢷䣏䣊䣼䢤

䢴䢲
]
B
d
[
|
1
1
S
|

free-space
on-body 0mm
on-body 15mm䢢䢳䢲䢸䢷䢹䣏䣊䣼

䢲䢯䢳䢲䢵䢹䢷䣏䣊䣼

䢯䢴䢲䢯䢵䢲䢸䢵䢴䣏䣊䣼

䢯䢶䢲

Figure 8: Measurement setup

䢯䢷䢲 䢢

䎓

|S11| [dB]

䢶䢲
䢵䢲

䎐䎔䎓

䢴䢲

䎐䎔䎘

]
B
d
[
|
1
1
S
|

䎐䎕䎓

䎓

|S11| [dB]

䢷䢲䢲

䢸䢲䢲

䢹䢲䢲

Frequency [MHz]䣆䣫䣨䣨䣧䣴䣧䣰䣥䣧䢢䣤䣧䣶䣹䣧䣧䣰䣨䣴䣧䣧䢯䣵䣲䣣䣥䣧䢢䣣䣰䣦䢢䣱䣰䢯䣤䣱䣦䣻䢺䢲䢲䢻䢲䢲 䢳䢲䢲䢲 䢳䢳䢲䢲

䎔444.5MHz䎔䎑䎘

free-space
on-body 0mm
on-body 15mm

䢲䣯䣯䢤䢯䢸䢸䣏䣊䣼䢤䢳䢷䣯䣯䢤䢯䢳䢲䣏䣊䣼䢤䢢䢳䢲䢸䢳䢷䣏䣊䣼

䢲䢯䢳䢲

Freespace
Onbody 15mm
Onbody 0mm

䢷䢶䢻䣏䣊䣼

䢯䢴䢲䢸䢲䢷䣏䣊䣼

䢯䢵䢲

䎕

611MHz䎕䎑䎘䎖953MHz

䎖䎑䎘

䢯䢶䢲
䢯䢷䢲 䢢

(a) Dipole antenna
S11 and Variance (chip antenna)

䢴䢲䢲

䢵䢲䢲

䢶䢲䢲

䢷䢲䢲

䢸䢲䢲

䢹䢲䢲

Frequency [MHz]䢺䢲䢲䢻䢲䢲 䢳䢲䢲䢲 䢳䢳䢲䢲(b) Chip antenna (Test subject 1)䎃Figure 10: Frequency characteristics of reﬂection coefﬁcient for 611 MHz conﬁguration

䎐䎘
䎐䎔䎓
䎐䎔䎘

References

䎐䎕䎓
䎐䎕䎘
䎐䎖䎓 䎃
䎓䎑䎘

[1] ”Body area networks,” http://www.ieee.org/
15/pub/TG6.html

Freespace
Onbody 15mm
Onbody 0mm

䎔䎔䎑䎘

444.5MHz

䎕䎕䎑䎘

611MHz䎖[2] K. Yazdandoost, and K. Sayraﬁan-Pour,“Channel
Model for Body Area Network (BAN),” IEEE
802.15-08-0780-04, Nor. 2008.

䎖䎑䎘

953MHz(b) Chip antenna[3] Y. Hao, et.al., “Antennas and propagation forbody centric wireless communications,” in Proc.
IEEE/ACES 2005, pp. 586-589,

Figure 9: Reﬂection coeﬃcient

5

䢶䢲䢲

䎃

䢷䢲

䎐䎖䎓 䎃
䎓䎑䎘

䢵䢲䢲

(a) Dipole antenna（Test subject 1）

S11 and Variance (dipole antenna)

䎐䎘

䎐䎕䎘

䢴䢲䢲

[4] ”Chip antenna, Mitsubishi material,” http://
www.mmc.co.jp/corporate/ja/index.html

Conclusions

In this study, the changes of antenna radiation pattern
and reﬂection coeﬃcient were investigated on navel forde-embedding the antenna eﬀects from human body in
BAN. The reﬂection property was almost unchanged
when the antenna was separated 15 mm from the bodysurface. Moreover, using 15 mm spacer was eﬀective
to replicate the antenna gain which does not dependon test subjects. Although it was found that theseresults coincide with those for the mobile phones, they
still provide useful information for BAN system/device
design.
36

Utilization of Byproducts in the Process of Biodiesel Production from Jatropha
Student Number: 08M18134

Name: Tomoki HAYASHI

Supervisor: Ryuichi EGASHIRAࠫࡖ࠻ࡠࡈࠔ↱᧪ࡃࠗࠝ࠺ࠖ࡯࠯࡞Άᢱ⵾ㅧࡊࡠ࠮ࠬߦ߅ߌࠆ೽↢ᚑ‛ߩ೑↪ᨋ ⍮ノ
߹ߕ㧘☻ࠫࡖ࠻ࡠࡈࠔᴤࠍ㧘೨ಣℂᓟ㧘ࡔ࠲ࡁ࡯࡞ߣࠛࠬ࠹࡞੤឵ߔࠆߎߣߦࠃࠅࡃࠗࠝ࠺ࠖ࡯࠯࡞Άᢱ߅
ࠃ߮ࠣ࡝࠮࡝ࡦ(೽↢ᚑ‛)ࠍ૞ᚑߒߎࠇࠄࠍಽᨆߒߚ‫ߩࡦ࡝࠮࡝ࠣ☻ޕ‬෼₸ߪ⚂ 0.23 ߣ৻ቯ୯ߢ޽ࠅ‫ޔ‬ℂ⺰⊛ߥࠣ࡝࠮࡝ࡦߩ↢ᚑ㊂ߣᧂ෻ᔕࡔ࠲ࡁ࡯࡞ߩ๺ߦ╬ߒ޿‫ࡦ࡝࠮࡝ࠣޕ‬ਛߦߪᧂ෻ᔕࡔ࠲ࡁ࡯࡞ߣਛ㑆↢ᚑ‛ߢ޽ࠆࡕࡁࠝ࡟ࠗࡦ߇฽߹ࠇࠆߚ߼‫ޔ‬ฦ‫ޘ‬Ớᐲࠍ᷹ቯߒߚ‫ߢ޿ߟޕ‬㧘ᴤ⢽࿶៦ᓟߩࠫࡖ࠻ࡠࡈࠔᲖ(೽↢ᚑ‛)ࠍᾲಣℂߒᵴᕈ὇ࠍ૞ᚑߒߚ‫ޕ‬ᾲಣℂ೨ߩࠫࡖ࠻ࡠࡈࠔᲖߩൻቇಣℂ߿ᾲಣℂߦ߅ߌࠆ᳓⫳᳇㔓࿐᳇ߩ೑↪ߥߤߦࠃࠅ⴫㕙Ⓧ߅ࠃ߮⚦ሹኈⓍߪჇടߒߚ‫ߦࠄߐޕ‬㧘਄⸥ߢᓧࠄࠇߚࠫࡖ࠻ࡠࡈࠔᲖᵴᕈ὇ࠍ↪޿ߡࡔ࠲ࡁ࡯࡞߅ࠃ߮ࡕࡁࠝ࡟ࠗࡦߩਇ⚐‛ࠍ฽߻ࠣ࡝࠮࡝ࡦṁᶧߩᐔⴧๆ⌕ࠍⴕߞߚ‫ࠔࡈࡠ࠻ࡖࠫޕ‬Ზᵴᕈ὇ߪ
ࠣ࡝࠮࡝ࡦਛߩਇ⚐‛ࠍ⦟ᅢߦๆ⌕ߒߚ‫ߦ⵾♖ߩࡦ࡝࠮࡝ࠣޕ‬චಽߥᵴᕈ὇㊂߇ᓧࠄࠇߚߎߣࠍ⏕⹺ߒߚ‫ޕ‬feed oil. Two methods of DA were used: one was alkali
1. Introduction
neutralization (ALDA) and the other was acid
esterification (ACDA). The procedure of FP was same as
The jatropha fruit is a promising biomass especiallyprevious works1-2).
for the biodiesel production as its kernel-oil (crude
Transesterification
jatropha oil, CJO) is inedible and has no competition
The experimental conditions in the transesterification
with food oils. The utilization of CJO could contribute toare summarized in Table 2.2. The mixture of methanol
the concerns over the economic imbalance andand catalyst (sodium hydroxide) were added to thedestruction of ecosystem.
pretreated oil. The transesterification is a set of
In transesterification glycerol is produced assimultaneous reactions and is heterogeneous duringbyproduct. In current glycerol market high purity
reactions. Therefore, the liquids in the reactor were wellglycerol is required. In order to remove color and trace
mixed by a magnetic stirrer. The pretreated oils were
impurities from crude glycerol adsorbent such astransesterified to fatty acid methyl ester (biodiesel) andactivated carbon, ion exchange resins and molecularglycerol.
sieves are used. After jatropha seed is removed for
Purification of biodiesel and glycerol
biodiesel production, the shell approximately 40% of the
Biodiesel was washed by water. Crude glycerol was
fruit is remained and it may initiate the waste problem. A
washed by H3PO4aq to remove salt (soap) then methanol
more viable option would be conversion of this shell
was vaporized at 378K for 0.5hr.
waste into value added product, for example thermal
treatment of shell waste to produce activated carbon.
Table 2.1 Compositions of Jatropha oil and other plant oils
This dissertation tried to utilize byproducts inFatty acid composition
Plant oilbiodiesel process. Transesterification of CJO was carried
14:0
16:0
18:0
18:1
18:2
out to obtain biodiesel and glycerol and to measure
Jatropha
0
0.103
0.138
0.427
0.263
Palm
0.011
0.350
0.046
0.421
0.166
composition of glycerol. Thermal decomposition of
Corn
0
0.117
0.019
0.252
0.606
jatropha shell was conducted to obtain activated carbon.
Cottonseed
0
0.283
0.009
0.133
0.575
The yield and characteristics of the activated carbon
Peanut
0
0.114
0.024
0.483
0.320
were estimated. Adsorption experiment was carried out
Rapeseed
0
0.035
0.009
0.644
0.223
to confirm the feasibility of glycerol purification by the
Soybean
0
0.118
0.032
0.233
0.555
activated carbon from jatropha shell.
Sunflower
0
0.061
0.033
0.169
0.737
2.

Table 2.2 Experimental conditions in transesterification
MeOH/oil molar ratio[-]
6
NaOH/oil mass ratio as catalyst[-]
0.01
Reaction temperature[K]
333
Reaction time[h]1

Transesterification of crude jatropha oil

2.1 Experimental
Feed oil
The compositions of feed oil and other plant oils are
summarized in Table 2.1. CJO and model jatropha oil(MJO) was selected as feed oil in this study. The MJO
was made of a mixture of tripalmitin (C16) and triolein(C18).
Feed pretreatment
Since CJO contained high amount of free fatty acid,the feed pretreatment (FP) is necessary before
transesterification. FP removes phosphatide (degumming,
DG), FFA (deacidification, DA), and water (drying) from

2.2 Results and Discussion
The yield of pretreated oil in FP (YPO), the yield ofeach product (biodiesel and crude glycerol) intransesterification (YBDF, YCG), and overall yield of eachproduct in whole biodiesel production (YOA.BDF, YOACG)
were defined as,Mi
M PO(1) Yi(2) YOA ,i YPO u Yi (3)
YPO
M FO
M TO
Figure 2.1 shows the effect of DA method on the

37

1

1

0.8

0.8

YBDF [-] ,YCG [-]YPO [-]

yield in FP. YPO steeply decreased with xFFA,0 in the caseof ALDA, since the saponification of triglyceride wasmore remarkable with larger amount of NaOH used forthe deacidification2). In the case of ACDA, YPO’s werehigher than those in the case of ALDA.
The YBDF and YCG are plotted against xFFA,FP in Fig.2.2.
YBDF's were almost constant at about 1. YCG was close to
0.23, which value equaled to theoretical amount ofglycerol plus unconverted methanol in transesterificationin this study. The method of DA did not influence YBDF
and YCG.
The overall yields in biodiesel process are shown in
Fig.2.3. The method of DA influenced both YOA,BDF and
YOA,CG strongly. Triglyceride loss in ALDA was larger
than that in ACDA.
Table 2.3 shows the compositions of crude andpurified glycerol. Unreacted alcohol and intermediate
glyceride especially monoglyceride are remained inglycerol. In this study methanol and monoolein wereanalyzed since methanol was used in transesterificationand triolein was the highest mass fraction in CJO. By
alkali removal and methanol removal, amounts of salt
and methanol were reduced. Mass fractions of alcoholand monoglyceride in glycerol have to be less than
0.00002 and 0.0005, respectively. More purification
process such as adsorption by activated carbon isnecessary.

0.6
0.4
Key

0.2
0
0

Feed
CJO
MJO
CJO
MJO

DA

biomasses are summarized in Table 3.1. In order tocompare with other biomass, palm shell was also used as
biomass feedstock. The jatropha shell and palm shell waswashed with deionized water, dried at 378K for 24hr,
crushed and sieved from 0.425 to 1.7mm. As option, theshells were impregnated in 10wt% KOH or H3PO4
solution for 24hr, then filtrated and dried before thermal
treatment. KOH or H3PO4 are generally used as alkali
and dehydration reagent, respectively. Table 3.2 showsthe experimental conditions in thermal treatment. The
shells were thermally treated varying experimental
conditions, such as thermal treatment atmosphere(nitrogen (n), steam (s)), and thermal treatment
temperature. Solid products (char) from thermal
treatment and commercial activated carbon (charcoal,
Activated, Powder; Wako chemical) shown in Table 3.3
were analyzed to measure BET surface area and porevolume.
Table 3.1 Chemical compositions of Jatropha shell and other biomasses

Jatropha shell3)

BDF

0.6

CG

0.4

Feed
CJO
MJO
CJO
MJO

DA

ACDA

xFFA,0 [-]

0.15

0.2

0

0.005

0.01

0.015

xFFA,FP [-]Fig.2.2 Yield of biodiesel fueland crude glycerol intransesterification

Fig.2.1 Yield of pretreated oil
in deacidificationO

Volatile

Fix-C

Ash

43.13

5.53

1.33

50.01

90.26

0

9.74
3.97

Jatropha husk4)

N/A

N/A

N/A

N/A

71.04

24.99

Palm shell5)

47.6

5.38

0.66

41.38

72.7

23.6

3.6

Palm fiber5)

46.64

5.66

1.73

39.46

73.7

12.6

6.6

Palm kernel5)

41.47

5.77

3.00

43.49

80.3

10.3

3.2

Brazil nut shell6) 50.0
Coconat shell7) 47.00

5.8

0.7

43.5

76.1

22.2

3.6

6.06

1.02

45.92

79.9

18.2

1.9

Bagasse pitch7)

48.96

6.77

0

44.27

86.0

10.8

3.2

Molasses7)

59.22

7.48

0.08

33.21

75.4

20.6

4

Jatropha shell, Palm shell

Mass of feed[kg]

Particle size

[10-3 m]

0.03-0.04
0.425-1.7
KOH, H3PO4

Temperature[K]

Holding time[h]423-1073
0.5

Atmosphere

1

YOA,BDF [-] ,YOA,CG [-]N

Impregnation reagent

0
0.1

H

Feed

ALDA

ACDA

0.05

Proximate analysis

C

Table 3.2 Experimental conditions in thermal treatment

0.2

ALDA

Ultimate analysis [wt%]Material

0.8

Steam, Nitrogen

Pressure[atm]

1

Steam flow rate

[m3/h]

0.03

Nitrogen flow rate

[m3/h]

0.003 (Steam atm.)
0.002 (Nitrogen atm.)

0.6

BDF

0.4

CG

Feed
CJO
MJO
CJO
MJO

DA

Table 3.3 Experimental conditions in thermal treatment

ALDA
ACDA

0.2
0
0

0.05

0.1

0.15

0.2

xFFA,0 [-]Fig.2.3 Overall yield of biodiesel
fuel and crude glycerol in wholebiodiesel production

Run No

Feed

1
2

Atm.

Jatropha

923

Steam

w/o

Jatropha

1073

Nitrogenw/o3

Jatropha923

Nitrogen

KOH

4

Jatropha923

Nitrogen

H3PO4

5

Jatropha

1073

Nitrogen

H3PO4

6

Palm

923

Steam

w/o

7

Jatropha923

Nitrogenw/o

8

Table 2.3 Compositions of glycerol

Pyrolysis Condition
Temp.[K]

Impregnation

Commercial activated carbon (CAC)

Mass fraction [-]Methanol3.

Monoolein

GlycerolCrude glycerol

0.374

0.014

0.55

Purified glycereol

0.001

0.019

0.98

3.2 Results and Discussion
The yield of each product in thermal treatment was
expressed as,
Mi
(4)
Yi
M BF
In the case of steam atmosphere, the liquid productincluded steam.
The yield of solid product (YS) is shown in Fig. 3.1.
The YS decreased with increasing the treatment

Thermal treatment of jatropha shell

3.1 Experimental
The compositions of jatropha shell and other38

temperature due to the high consumption of the carbon.
The YS with impregnation was higher than that ofwithout. For the activation with H3PO4, the YS increased
with temperature in the range of less than 800K. H3PO4
worked as dehydrogenation reagent and promoted
porosity of carbon structure. For carbonization above
800K, the cross-section structure reached its thermal
limit and a lot of bond cleavage was produced. With
KOH impregnation, the YS decreased with temperature.
Alkalis such as KOH were able to interact with carbon
atoms and thus catalyze the dehydrogenation andoxidation reaction, leading to the suppression of tar
evolution and development of porosity.
The yield of liquid product (YL) is shown in Fig.3.2.
The liquid product was composed of the volatilecomponents from biomass and the water from the feed
steam. The YL in steam atmosphere was higher than thatin nitrogen atmosphere. The YL decreased withtemperature in the range of more than 800K. Tar were
decomposed into solid and gas.
Figure 3.3 shows the yield of gaseous product (YG).
The YG increased with temperature. In the case of steam
atmosphere, more char and steam were reacted and more
gaseous product was produced with higher temperature.
The H3PO4 impregnation provided higher liquid yieldthan KOH impregnation, whereas the KOH impregnation
provided high gas yield.
Figure 3.4 shows char characterization: BET surface
area and pore volume. BET surface area and pore volume
were higher with temperature. With impregnation, BET
surface area and pore volume were much higher thanthose without impregnation.

w/oJatrophanJatropha

n

KOH
H3PO4

Palm

s

w/o

101

3
1000
10
A BET

0.6
0.4

2
100
10



Atm.

Imp.

Jatrophas

w/oJatropha

Feed

n

w/o

n

KOH
H3PO4

Jatropha

11010

Jatropha

n

Palm

s
CAC

1.00
100

0.10-1
10

w/o

-2
10
0.01

1001

0
400

600

800

1000

T [K]

Fig.3.3 Yield of gaseous productin thermal treatment

4.

100.1-1
400

-3
10
0.00

600

800

T [K]

Fig.3.4 BET surface area andpore volume of char

Purification of glycerol using activated carbon
from jatropha shell

4.1 Experimental
The char from jatropha shell, and palm shell wereused as adsorbent. Also the commercial activated carbon

MeOH

Gaseous products
Liquid products

Solid products (Activated carbon)

Fig.4.1 Proposal biodiesel process

39

Biodiesel
Purification
Biodiesel fuel

H2O

1000

Jatropha shell

0.2

Na3PO4w/onGlycerols

Jatropha

MeOH
H2SO4
Esterification

104
10000

Imp.

Jatropha

Methanol Recovery
H2O
Purified glycerol
Glycerol Purification

1000

Fatty acid

Atm.

800

Fig.3.2 Yield of liquid productin thermal treatment

ABET [m2/g]

YG [-]

0.8

Feed

600

T [K]

T [K]
Fig.3.1 Yield of solid productin thermal treatment

Transesterification

0
400

1000

Oil Extraction

800

Pyrolysis

YL [-]

0.2

Crude glycerol
Alkali Removal
Phase Separation

w/o

0.6

H3PO4

s

w/o

NaOH

Palm

s

Pretreated oil

n

Palm

MeOH

n

0.8

w/o

1. DG
2. ALDA or ACDA
3. Drying

Jatropha

Jatropha

KOH
H3PO4

: Biodiesel fuel production: Glycerol purification: Thermal treatmentnFeed pretreatment

Jatropha

KOH
H3PO4

Crude Jatropha oil

w/o

n

0.2

Key

Imp.

s
n

Jatropha

0.4

1

Atm.

Jatropha

0.4

600

Feed
Jatropha

w/o
w/o

0.6

0
400

Key

1

Imp.

s
n

KOH
or
H3PO4
Impregnation

Atm.

Jatropha

YS [-]

0.8

Feed
Jatropha

Jatropha fruit

Key

Table 4.1 Experimental conditions of adsorption
Feed
Glycerol solution of
methanol or monoolein
Mass of feed[kg]
20  10-3
Mass fraction of
-4
-1
[-]
1.0  10 - 1.0 × 10
methanol or monoolein
Adsorbent
Activated carbon
from jatropha shell and palm shell
Commercial activated carbon-3
Mass of adsorbent[kg]
0.2  10
Shaking amplitude
[m]
0.04
5400
Shaking frequency[h-1]
Contacting time[h]72
Contacting temperature [K]303

 [ml/g]

1

was used to confirm whether the char could be useful asactivated carbon. The experimental conditions ofadsorption are summarized in Table 4.1. The char was
mixed with various concentrations of model glycerol
solution with methanol or monoolein and the mixtures
were put in the shaker at 303K for 72hr enough for theadsorption equilibrium. Methanol and monoolein wereselected as unconverted alcohol and intermediate
glyceride in transesterification. After the adsorption
experiment, the glycerol solution was separated from thechar by filtration. The initial and equilibrium
concentration of glycerol solution were analyzed.
I proposed an improved biodiesel process utilizing
the thermal treated product and the purified glycerol asshown Fig 4.1. CJO of 1ton/year is provided to obtain
biodiesel and purified glycerol. 2.143ton/year of jatrophashell is treated to activated carbon and used for glycerol
purification8). The amount of adsorption in order topurify glycerol was assumed the saturated amount ofadsorption.

0.25

0.25

0.2

0.2

qMO [g/g-AC]

qMeOH [g/g-AC]

4.2 Results and Discussion
The adsorption amount of each adsorbent wascalculated by this material balance equation,( x 0  x e ) M sol
(5)
qi
M AC
Figure 4.2 shows the adsorption isotherms of
methanol by the activated carbon. Figure 4.3 shows theadsorption isotherms of monoolein. Lines in the figures
were plotted by Langmuir isotherm equation. The
amount of adsorption of each chemical increased withtemperature of thermal treatment. The Langmuir
isotherm equation was used to quantify the saturated
amount of adsorption. Saturated amount of adsorption ofeach adsorbate increased with BET surface area. These
relationships are shown in Fig.4.4 and Fig.4.5.
The capacity of activated carbon for methanol andmonoolein (YAC·q*i) is plotted against the yield of
methanol or monoolein in purified glycerol (YPG,i) in
Fig.4.6. YPG,i was mass ratio of methanol or monoolein
relative to CJO. YAC was mass ratio of activated carbon
relative to CJO. The diagonal line in Fig.4.6 means thatthe activated carbon from jatropha shell can fully adsorb
the component in glycerol. And it shows that all the plotsof activated carbon are much higher than the diagonalline. Obviously the activated carbon from jatropha shellwould have enough adsorption capacity for removing theyielded amount of methanol and monoolein in glycerol.

0.15
0.1

Key Run No
1
2
3
4
5
6
8

0.05
0
0

0.02

0.04

0.06

0.08

Key Run No
1
2
3
4
5
6
8

0

0.1

0

0.02

0.04

0.20

q*MO [g/g-AC]

0.20
0.15
0.10
Key Run No
1
2
3
4
5
6
8

0.1

0.15
0.10
Key Run No
1
2
3
4
5
6
8

0.05
0.00

2000

0

ABET [m2 /g]
Fig.4.4 Saturated adsorption
amount of methanol against
BET surface area
100 1

1000

ABET [m2 /g]

Literature Cited
1) Sinthupinyo P.; Master Thesis, Tokyo Tech (2007)
2) Hayashi T.; Bachelor Thesis, Tokyo Tech (2008)3) Sinthupinyo P. et al.; SCEJ Sendai, C121 (2008)
4) Sardar P.; SPRERI annual report (2005-2006)
5) Luangkiattikhum P. et al.; Bioresource Technology,
99, 986-997 (2008)
6) Bonelli P. et al.; Bioresource Technology, 76, 15-22(2001)
7) Das S. et al.; Energy, 27, 405-414 (2002)
8) Sinthupinyo P. et al.; ISEEE Reyong, 305 (2009)

2000

Fig.4.5 Saturated adsorption
amount of monoolein
against BET surface area

0.1
10-1

YAC q*i [g-i/g-AC]

q*MeOH [g/g-AC]

0.25

1000

0.08

Fig.4.3 Adsorption amount ofmonoolein

0.25

0

0.06

xMO [-]

Fig.4.2 Adsorption amount ofmethanol

0.00

NomenclatureA= Surface area
[m2/g]M= Mass of material[kg]q= Amount of adsorption[g/g-AC]q*= Saturated amount of adsorption [g/g-AC]T= Temperature in thermal treatment [K]= Mass fraction[–]xY= Yield of product[–]<Greek>= Pore volume[ml/g]<Subscript>
0= Initial stateAC= Activated carbon
BDF= Biodiesel fuelBET
= BET adsorption model
BF= Biomass feedstockCG= Crude glycerole= Equilibrium state
FFA= Free fatty acidFO
= Feed oil
G= Gaseous product in thermal producti= Component i
L= Liquid product in thermal product
MeOH = Methanol
MO= Monoolein
OA= Overall biodiesel production
PG= Purified glycerol
PO= Pretreated oil
S= Solid product in thermal productsol= SolutionTO= Transesterified oil

0.1
0.05

-2
100.01

-3
0.001
10

Key
MeOH MO-40.0001100.00001
10-5

Thermal treatment
Temp.[K]
923
1073
923
923
1073

Atm.
s
n
n
n
n

Imp.
w /o
w /o
KOH
H3PO4
H3PO4

0.01-2
0.1-1
1010
10
10
YPG,i [-]
Fig.4.6 Adsorption capacity of activatedcarbon against yield of methanol ormonoolein in glycerol
0.0000110-50.0001
10-4

Conclusion

In biodiesel production yield of crude glycerol was
close to 0.23. The method of deacidification influenced
overall yield of products. The useful material was
produced by thermal treatment of jatropha shell. Steamatmosphere and impregnation with KOH or H3PO4 were
improved the char as activated carbon. The activatedcarbon from jatropha shell adsorbed impurities in crude
glycerol. In overall biodiesel process there was enoughamount of activated carbon from jatropha shell to purify
glycerol.

0.15

xMeOH [-]

0.05

5.

0.001
10-3

40

Assessment of Users’ Profitability and Perception of Residential Photovoltaic
Systems under Failure Risk
Student Number: 08M18140

Name: Toshihiro Mukai

Supervisor: Naoya ABEᨾ㞀ࣜࢫࢡࢆ⪃៖ࡋࡓఫᏯ⏝ኴ㝧ගⓎ㟁ࢩࢫࢸ࣒࣮ࣘࢨ࣮ࡢ
཰┈ᛶ࡜ㄆ㆑ࡢホ౯ྥ஭ Ⓩᚿᗈ
ఫᏯ⏝ኴ㝧ගⓎ㟁ࢩࢫࢸ࣒࡟ࡣࠊ1㹼2 ๭⛬ᗘࡢ๭ྜ࡛ఱࡽ࠿ࡢᨾ㞀ࡀ⏕ࡌࡿࡇ࡜ࡀ᫂ࡽ࠿࡟
࡞ࡗ࡚࠸ࡿࠋࡋ࠿ࡋࠊ࣮ࣘࢨ࣮࡟ᨾ㞀ࣜࢫࢡ࡟㛵ࡍࡿ᝟ሗࡀ㐺ษ࡟ఏࢃࡗ࡚࠸࡞࠸ྍ⬟ᛶࡀ࠶ࡾࠊ⌧≧ࢆホ౯࣭ゎ᫂ࡋ࡞࠸ࡲࡲᬑཬ⟇ࢆㅮࡌࡿࡇ࡜ࡣࠊᑗ᮶ⓗ࡟ᾘ㈝⪅ࡢ୙ಙឤࢆ୙ᚲせ࡟ᘬࡁ㉳ࡇࡍㄏᅉ࡜࡞ࡿࠋ
ࡑࡇ࡛ᮏ◊✲ࡣࠊᨾ㞀ࡀ PV ࢩࢫࢸ࣒ࡢ཰┈ᛶ࡟୚࠼ࡿᙳ㡪ࢆホ౯ࡋࡓୖ࡛ࠊ࣮ࣘࢨ࣮ࡢㄆ㆑
ࢆព㆑ㄪᰝ࡟ࡼࡾ㞟ィࡋࠊᨾ㞀ࣜࢫࢡ࡟ᑐࡍࡿㄆ㆑ࡢ㐪࠸ࢆࡶࡓࡽࡍせᅉࢆศᯒࡋࡓࠋࡇࢀࡽࡢ
ศᯒࢆ㏻ࡋ࡚ࠊࡼࡾ㛗ࡃ PV ࢩࢫࢸ࣒ࢆ฼⏝ࡍࡿࡓࡵࡢ♫఍ⓗయไࡢᚲせᛶࢆ᳨ウࡍࡿࠋ

Recently the government of Japan and local
governments have implemented both R&D supportand market-based measures to accelerate thediffusion of PV system. There is, however, a danger
that the users don’t correctly recognize the risk offailures. Conducting diffusion policies without
examining the current public perception relevant to
the risk of failures is not only inefficient but also
likely induces consumers’ distrust on the PV industry.
Hence, the objective of this study is to examine
the necessity of countermeasures for the risk offailures from the aspect of users’ profitability. Firstly
a model was developed and users’ profitability
considering the risk of failures was simulated
throughout the systems’ lifetime. To economicallyassess it, Net Present Value method (NPV) is
conducted. Also, the users’ perception of the risk offailures was assessed by conducting a questionnaire
survey in Kakegawa City, Shizuoka Prefecture, andfinallythecausingfactorsofusers’
misunderstandings about failure risk and profitabilityof PV systems were analyzed.

1. Background and Objectives
To diffuse residential photovoltaic (PV) system
in a society, it is necessary for the PV system users torecognize the risk of failures appropriately foradequate and sustainable generation of electricity
without unnecessary disruption. While previous
studies show that PV systems fail with a certain rateof probability, PV systems are often regarded as“maintenance free” products, implying that thecurrent users may not be well-informed of the actual
characteristics of PV systems such as profitability
and failures.
Wakabayashi et al. (2009) shows a result of the
field research with the sample number of 725 PV
system users that 21.8 % of the total systems haveexperienced some kind of failures or troubles (with
1.0 % module failures and 9.9 % inverter failures,
respectively) [1]. Kato (2009) indicates more severe
result of the research with the sample number of 257
users that, within a decade after installation of PV
system, 14.0 % of modules and 17.1% of inverters
had failed respectively [2].
In parallel with the field researches, thesestudies have conducted surveys on users’ perceptionof the risk of failures. The results indicate that a gap
exists between users’ recognition and the actual
properties of the system, and that it is difficult for theusers to find out failures of PV systems bythemselves. Especially, failures which occur onsurrounding modules are almost impossible to be
noticed by general users because the system still
continues to generate electricity with reduced output
level. If the users continue to use the system with
unrecognized failures on modules or other equipment,
it may spawn a serious generation loss during thesystems’ long lifetime.

2. Modeling Users’ Profitability
2.1. Net Present Value method
NPV method is an economic tool to makedecisions on public projects or company activities.
The methodology of NPV, firstly, confirms the whole
economic losses and gains of an investment, and then,calculates the Net Present Value (NPV) or theInternal Rate of Return (IRR) to compare with thebenchmark rate of return. NPV can be written as:(1)

41

where, ,andrepresent, discount rate, aseries of net benefits starting from the first year (n=1),
and assumed duration of the project, respectively.
IRR can be calculated as a discount rate when NPV
becomes zero.
In this study, discount rate is assumed as 1%, a
relatively low rate, because the users are purchasing
PV systems without high profitability.
2.2. Conceptual Model
Fig.1 shows the concept of the developed model
for assessing the impact of the risk of failures on theusers’ profitability.
The economic gains by purchasing a PV system
are: (1) decrease of electricity charges by using theelectricity generated by the PV system, (2) income byselling electricity. If some components of the PV
system fail, economic loss emerges as a decrease inthese incomes. The costs, on the other hand, aremainly considered as: (1) initial investment topurchase the PV system, (2) periodical payments formaintenance by manufacturers and (3) unanticipatedpayments for repairing or replacement of failed
system equipment. These economic parameters are
considered as inflow and outflow of users’ profit.
The electricity power generation of a PV system
is calculated by the following equation

Fig.1. Correspondence of Equation (2) to Conceptual Model

experienced failures might have resistance, anxiety or,
potentially, distrust on PV systems as an industrial
product which might fail again.
In addition to the possible factors above to makedifference on the user’s resistance to the risk offailures, ten other questions were asked in thequestionnaire survey to use as explanatory variables
in ordered logit model. Nine other questions, forclassifying the users’ resistance and applying as
dependent variables, were also asked by the survey.
3.2. Ordered Logit Model
As a method to analyze the response data
gathered by the questionnaire survey, ordered logit
model (OLM) [3] [4] was adopted. Suppose
dependent variable y is ordinal number {0, 1, 2, …,
J}, latent variable y* is assumed as following;(2)
where, EAS, PAS, HAg, and GS are representing
electricity production, standard test condition (STC)
array maximum power, solar irradiation, and STC
intensity of solar irradiation (GS=1), respectively.
Correspondence of the equation to the developedmodel is shown in Fig.1.
Under these assumptions, electricity generation
per month were calibrated and the results were in therange of 77.1 [kWh/kW*month] to 95.1
[kWh/kW*month] verifying reliability of the model.

y* = xβ + ε,

ε|x ~ Normal (0,1)

(3)

where
xβ = x1β1 + … + xkβk
In these equations, xi and βi represent explanatory
variables and estimated coefficients respectively. And
ε is an error term which is assumed to follow
standardized normal distribution on each xi.
Each response probability P{y = (0, 1, 2,…,J)|x} is estimated by the following equations;

3. Methodology for Assessing Users’ Perception

P(y = 0|x) = P(y* ≤ α1|x) = P(xβ + ε ≤ α1|x)  = Λ(α1 - xβ)P(y = 1|x) = P(α1 ≤ y* ≤ α2|x)  = Λ(α2 - xβ) - Λ(α1 - xβ).
.
.
P(y = J-1|x) = P(αJ-1 ≤ y* ≤ αJ|x)  = Λ(αJ - xβ) - Λ(αJ-1 - xβ)P(y = J|x) = P(y* ≥ αJ|x)  = 1 - Λ(αJ - xβ)3.1. Hypotheses to test
To assess the users’ perception of the risk offailures, a questionnaire survey was conducted for theusers in Kakegawa City, Shizuoka Prefecture, andtested responses with following hypotheses.
Firstly, the resistance to the risk of failures
might be different among generations, and thenumber of years of PV system use. For example,
compared to the elderly, middle aged users could be
more interested in the economic return since they
have more uncertainty in their longer life.
Moreover, it is probable that those who have

(4)

These response probabilities represent the occurrence42

of yi for a particular set of x.
The expected value E(y|x) can be simply
estimated by the equation;
E(y|x) = a0P(y=a0|x) + a1P(y=a1|x) + … +aJP(y = aJ|x)

4. Results and Discussions
4.1. Users’ Profitability under Failure Risk
Tab.1 lists the initial values and parameters used
for the Monte Carlo simulation (the calculation
number was five thousands), and the results areshown in Tab.2 and Fig.2.
The result in Tab. 2 confirms the well known
fact that the profitability of PV systems is still low.
The most important part of the result is that the riskof failures requires 5% of users large expense burdenin the range of 700,000 to 1,400,000 yen, while 50%of all users are required the burden of expense forless than 250,000 yen. It is regarded that sellers
supply PV systems to users without explaining thiseconomic impact of failures.
The standardized regression coefficients in Fig.2
especially show the large impact of inverters’ failureson the users’ profitability. It is larger than that of the
Performance Ratio, which is commonly used as anefficiency indicator for residential PV systems [5].

(5)Estimates of the expected values can be compared at
different values of the explanatory variables to obtain
partial effect for discrete xi.
Tab.1. Parameters Assumed in the Model
Min.
Max.
Const.
Data
value value
value
sources
Solar Irradiation per116.65
METPV3month [kWh/m2]
STC array power
3[kW]
Performance Ratio
66.1
81.6
Ueda(2009)[%]
Price rate of PV
661,000
NEF
system [JPY/kW]
Rate of selling
50
55
NEF
electricity[%]
Price rate of sellingTokyoelectricity
Electric
24[JPY/kWh]Power Co.
Electricity price
22.86
TEPCo.
[JPY/kWh]Failure occurrence
0.11
Kato(2009)
on module [%]Failure occurrence
0.14
Kato(2009)
on inverter [%]Parameters4.2. Users’ Perception of Failure Risk
Before analyzing the responses gathered by thequestionnaire survey, with 98 observations,explanatory variables were chosen by checking
Spearman’srankcoefficientstoexcludemulticollinearity of explanatory variables.
Tab.3 lists the descriptive statistics of variables
chosen for OLM regression analysis. As it can beseen from the explanatory variables age, nmonth andnewhs, the sample data seems to be consistent andfollows a population of PV system users living in
Kakegawa City well.
Although nine dependent variables, relative tothe profitability or technical characteristics including
the risk of failures, were analyzed, statisticallysignificant results were obtained on only twovariables, willfail and willfnd, listed in Tab.3.
Tab.4 shows estimated regression coefficients,
standard errors, and significance level of eachvariable. The most interesting finding is that the users
joining in a civic organization which is active on PV
systems (representing the variable org) tend to thinkthat their PV systems might fail (willfail), while those
who just wish to contribute to environmental issues(env) think in an opposite way. By calculating
equation (5), where org ascends from 0 to 1, theexpected value of willfail also ascends from 3.5 to 4.3,
while, where env ascends from 1 to 5, the expectedvalue descends from 4.2 to 3.3.
The reason for these results is the considerationthat civic group members share information and learn
from each other about PV systems, but theTab.2. Cumulative Probability Density and Corresponding NPVs
CPD
NPV (JPY)-1,434,420 (Minimum Value)
5.0%-693,175
10.0%-610,629
25.0%-394,51250.0%-247,27975.0%-111,391
90.0%-22,37695.0%
13,45755,699 (Maximum Value)

Fig.2. Standardized Regression Coefficients of Users’
Profitability and Risk of Failures43

Tab.3. Descriptive Statistics of Explanatory and Dependent Variables for OLM Regression Analysis

age
fail
nmonth
newhs
env
econ
renew
org
willfail
willfnd

Meanings of variables
the number of age
if respondent has experienced failures, 1
the number of months using PV
if a respondent installed PV when bought a new house, 1
Were you encouraged to purchase PV for contribution on
environmental issue? (5 = strongly, 1 = totally not)Were you encouraged to purchase PV for economic return?
(5 = strongly, 1 = totally not)if a respondent uses other renewable energy technologies, 1
if a respondent joins in civic activity related to PV, 1
Do you think your PV will fail?
(5 = strongly think so, 1 = totally not)If your PV fails, do you think you can find it out by yourself?
(5 = strongly think so, 1 = totally not)Mean
56.7450.094
30.411
0.369

Std.Dev.
13.6200.294
32.295
0.485

Min.
29
0
0
0

Max.
86
1
108
1

4.204

0.824

1

5

4.418

0.759

1

5

0.196
0.095

0.399
0.295

0
0

1
1

3.388

1.118

1

5

3.133

1.081

1

5

5. Conclusions

Tab.4. OLM Regression Results about
Users’ Perception of the Risk of Failures
willfail
willfndage-0.03260.0200
0.02160.0208fail
0.06580.1910
0.88750.8553
nmonth-0.0003
0.0340 ***
0.00820.0101newhs
1.1849 **-0.3977
0.53600.5381env-0.5825 *-1.0034 ***
0.33650.3591econ-0.04480.8081 **
0.3176
0.3352
renew
0.19361.4224 *
0.84820.7690org
2.7713 **0.7849
1.2108 
1.1968 
χ2
13.98 29.08 p-value
0.0823 
0.0003 
Statistical significance: *** p<0.01; ** p<0.05; * p<0.1

The main finding of this study, in evidently, isthat the risk of failures, especially which on the
inverter, has relatively high impact on users’
profitability, and the current users have not recognize
it properly. In particular, users who wish to contributeto the environmental issues tend to think that their PV
systems will not fail. To use PV systems sustainably,
it is critical to inform the risk of failures to users.
The future direction of this study will be toprobabilistically examine the risk of failures ingreater detail, considering other components offailures or troubles such as failures on bypass diodes
or deficient work of installation. There is also a needto assess social costs by failures, especially those on
modules, which are hard to find out by the users
alone but continue generating electricity withdecreased output.

environment-friendly users are, on the other hand,satisfied only with the installation of a PV system on
their houses and not so much interested afterwards. In
case the users encounter failures, they will bedisappointed and distrust the PV market. It is impliedby these findings that networking has a positive effectto improve the resiliency of the PV market.
It is equally interesting that the longer users use
PV systems (nmonth), the more people think that
failures on PV systems are easily found out (willfnd).
A long and safe duration of use might eventually
make users to trust PV systems, and feel confident
enough to do it. But the risk of failures certainly
increases as time goes on. It is more necessary tosupport users in a longer term.
In this analysis, no significant findings relative
to the users’ perception of the profitability could beobtained. It might be partly because the users are
confused whether PV systems are profitable or not
and the government has recently provided several
types, and amount, of subsidies for residential PV
systems. The effect of these subsidies on the users’
perception should be analyzed as a further study.

References[1] Wakabayashi H., T. Igarashi, K. Otani, T.
Takashima (2009) Users’ awareness of residential PV
systems’ failures. IEE Japan Technical Meeting ofPower and Energy, 08 – 3.
[2] Kato K. (2009) Reality of the maintenancecondition of residential PV systems. AIST Fifth
Annual Symposium of Research Center for
Photovoltaics, pp. 179 – 180.
[3] Greene W. H. (2008) Econometric Analysis: Sixth
Edition. Pearson Prentice Hall.
[4] Wooldridge J. M. (2002) Econometric Analysis of
Cross Section and Panel Data. MIT Press.
[5] Ueda, Y., K. Kurokawa, K. Kitamura, M. Yokota,
K. Akanuma, H. Sugihara (2009) Performance
analysis of various system configurations on
grid-connected residential PV systems. Solar Energy
Materials and Solar Cells, 93, pp. 945 – 949.
[6] Gnedenko B., I. Pavlov, I. Ushakov, S.
Chakravarty(1999)
Statistical
Reliability
Engineering. Wiley Interscience.

44

A NUMERICAL STUDY ON TURBULENCE IN THE URBAN CANOPY LAYER
AFFECTED BY THE ATMOSPHERIC BOUNDARY LAYER
Student Number: 08M18157 Name: Yoshimi YAMASHITA Supervisor: Manabu KANDA

ᄢ᳇Ⴚ⇇ጀߩᓇ㗀ࠍ⠨ᘦߒߚㇺᏒࠠࡖࡁࡇ࡯ጀߩੂᵹߦ㑐ߔࠆ⎇ⓥ
ጊਅ ૫⟤
ᧄ⺰ߢߪ LES ࡕ࠺࡞ࠍ૶ߞߡ㧘ㇺᏒࠠࡖࡁࡇ࡯ጀౝߩ⍍㑆⊛ߥᵹࠇ႐ߦߟ޿ߡ⎇ⓥߒߚ㧚ࠬ࠻࡝࡯࠻ࠠࡖ࠾ࠝࡦߩ⍍㑆⊛ߥᵹࠇ႐ߪᐔဋ႐ߣߪోߊ⇣ߥߞߡ߅ࠅ㧘ਛߢ߽․ߦ㧘ᄢⷙᮨ਄᣹ᵹ(ࡈ࡜࠶ࠪࡦࠣ)߿ᒝ޿᷵ࠍᏎߊᵹࠇ(ࠠࡖࡆ࠹ࠖ᷵)ߪ㋦⋥ᣇะߩᾲャㅍߦᄢ߈ߊነਈߒߡ
޿ࠆ㧚ᦝߦ㧘ࠬ࠻࡝࡯࠻ࠠࡖ࠾ࠝࡦߩ୘‫ߩޘ‬ᵹࠇ႐ߪዪᚲ⊛ߦ⊒↢ߒߡ߅ࠄߕ㧘ធ࿾Ⴚ⇇ጀߦ⊒㆐ߔࠆ⚵❱⊛ੂᵹ᭴ㅧߦ߆ߥࠅኻᔕߒߡ⊒↢ߒߡ޿ࠆ㧚

1 Introduction

2 Simulation Condition

Recently, various environmental problems in cities, such as

In this study, the UCL affected by the ABL is simulated. An

heat island and concentrated heavy rainfall, are worsening. To

LES model, PALM (Parallelized Large-eddy simulation Model),figure out these problems, it is important to investigate thewhich is developed in Hannover University, is employed, andturbulent characteristics within the Atmospheric Boundary Layer

TSUBAME grid cluster system in TITech was used to calculate(ABL) above an urban setting, because turbulence determines the(Castillo et al [7]).

transport of heat, momentum and scalars between the surface and

The computational domain, which has a size of 2560m ×

the atmosphere. Various studies have revealed that these processes

2560m × 17096.0m, is large enough to simulate the whole ABL.

are dominated by turbulent coherent structures (CS), which is

The grid spacing, which is 2.5m, is small enough to resolve theobserved in the Surface Layer (SL) (Inagaki [1]).

flow detail in the UCL. 1024 cubes of 40m (=H; building height)

Flow field in the Urban Canopy Layer (UCL) has been also

dimension are arranged uniformly on the ground with a plane areastudied. Uehara et al. [2] investigated the characteristics of thedensity is 0.25. To simulate the real ABL, the initial temperature ismean flow in a street canyon under various atmospheric stabilities

set up as follows; temperature on the ground was initially set up as

by wind tunnel experiment. Kanda et al. [3] observed that the

300K, the temperature gradient from 0m to 800m as 0.07 K/m, andinstantaneous flow in a street canyon is completely different fromthat from 800m to the top as 0.74K/m. The initial wind velocity

the mean flow. Takimoto [4] observed strong upward motions inwas set up as 20m to the streamwise (x) direction. The heat flux is

Particle Image Velocimetry (PIV) experiment at a comprehensive

supplied from the ground and the roof, and set as 0.10 K࡮m/s.

outdoor urban scale model (COSMO). These upward motions
might be related to the CS in the SL. Since previous researches

3 Result and discussion

about the urban canopy did not consider the effect from the CS in

3.1 Mean flow in the street canyon
Characteristics of the mean flow field at the street canyon are

SL, interactions between the UCL and the SL above is unclear.
On the other hand, turbulence in the SL is affected by the

discussed using phase averaged data compared with the results

mixed layer (ML), as Inagaki and Kanda [5] demonstrated that thefrom PIV experiment (Takimoto [4]) measured in COSMO. The

turbulent CS is affected by the outer scale (ML) eddy. Therefore,

phase average was obtained by averaging all the 2H horizontal

in simulating the SL, it is necessary to include the whole ABL.

square sections which includes one cube.

Zhang [6] simulated the whole ABL above the UCL, resolving the

Figure 1 shows the streamwise-vertical (xz) cross sectional

structures by LES (Large Eddy Simulation). The turbulent CS is

vector map, which passes the center of building, and the potential

also observed in this simulation. This LES model is useful to study2

a connection between the SL and UCL because it included the ABL
and the urban structures in the simulation. However the flow in the

1.6

UCL is not investigated in his study because the resolution is not

1.2

(b)

301.8

1.4

z/H

fine enough.

302

(a)

1.8

301.6

1
301.4

0.8
0.6

This present study investigates the characteristics of the

301.2

0.4

0.2

instantaneous flow fields in the UCL with the same LES model

301

0

0

using smaller grid spacing. Specifically, the relation between the0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8x/H

2 θ (K)

Fig-1 Vector map of mean flow (a) Simulation (b) PIV

UCL and the turbulent CS in the SL is also analyzed.
45

2

2
(2)

1.8

2
(3)

1.8

1.6

1.4

1.4

1.2

1.2

1.2

1.2

1

1

z/H

1.6

1.4

z/H

1.6

1.4

1

0.8

0.8

0.8

0.6

0.6

0.6

0.6

0.4

0.4

0.4

0.4

0.2

0.2

0.2

0.2

0

0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8
x/H

2

0

0
0

0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8
x/H

2

1.0
0.5
0

1

0.8

0

1.5

(4)

1.8

1.6

z/H

z/H

2

(1)

1.8

-0.5

-1.0
-1.5

0
0

0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8
x/H

2

0

0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8
x/H

2

Vortcity(1/sec)Fig-2 Example of classifications

the canyon is insignificant, there is no obvious vortex sheet.

temperature contour. Velocity is normalized by the mean velocity

The air can easily go upward, and the vectors in this flow areat z=2H, 2 . According to Figure 1, an eddy is blowing between

not large.

the blocks, which is called cavity eddy. For the PIV results [4], theb) Flushingcenter of this eddy is located at the upstream side of the block. This

Takimoto [4] observed instantaneous large scale upward

anomaly may be due to the difference in stability for the two

motion and named it “flushing”. Flushing is defined as “more thanexperiments. In this simulation, the stability is near-neutral, while

55% of vectors satisfy the condition of  > || in street canyon”.

in the PIV experiment the stability is slightly unstable. Moreover,This definition is also applied in this study. All the xz-crossin the PIV measurement, wind direction is not always parallel withsections which pass through the center of a building are used in thethe x-direction. The potential temperature in the canyon at z=H isextraction of flushing events (992 sections). Thirty-one flushing

lower than that at the roof since heat is also supplied from the roof.

events are extracted. In Figure 3 (a), an example of a contour

The potential temperature by the leeward wall is lower than that atshows the spanwise vorticity. The large scale upward wind occurs

the windward wall since there is a strong downdraft at the leeward

and continues above the building. The vorticity is small since thewall.

velocity gap is very small.

3.2 Instantaneous flow in the street canyon

Figure 4 (a) shows the ensemble-averaged vector map of all

Instantaneous flows in the street canyons are completely

the sections where flushing occurs. Here, the velocity is normalized

different from the mean flow. They can be classified into fourby 2 and the contour shows potential temperature. It isgroups using its vorticity distribution as an index. Moreover,relatively warmer in the canyon where flushing occurs.

large-scale upward wind, which is called flushing by Takimoto [4],Figure 5 shows the streamwise-spanwise (xy) cross sectionaland flows into the canyon, which is referred as cavity eddy, arevector map of ensemble averaged flushing near the ground (z =important events.

0.0625H) and at roof level (z = H). Ensemble averaged flushing isa) Classifying the flow fields

obtained by averaging all the 3H horizontal square sections which

Figure 2 shows an example of the vector map of each flow

includes two cubes. Because flow converges at z = 0.0625H andclassification group. They are classified by the vortex sheet

does not diverge at z = H, the flow from the y-direction at

developing from the windward roof. The vortex sheet is locatedz=0.0625H continues to flow upward (i.e. flushing). Thus, flushing

between a fast flow field above the canopy, and the slow flow fieldoccurs with the upgathering of air at the lower region of the UCL,

within the canopy. Thus, the vortex sheet is treated as the boundary

and transporting this air toward above the canopy. Also, thebetween the flows. The following are the definition of groups andpotential temperature in the street canyon is higher because thetheir characteristics.
(1)

spanwise convergence transports heat.

Fully capped: in this group (Figure 2 (1)), the vortex sheet is

Figure 7 (a) illustrates the sensible heat flux profile wheredeveloped from the windward roof and continues to the otherflushing occurs within and above the canopy. Dashed-line shows

roof. Thus, the flow fields within and above the canopy are

the average from all the canyons, while dotted-line for the otherseparated.
(2)

canyons from flushing. When flushing occurs, the sensible heat

Opening capped: in Figure 2 (2), the vortex sheet divides theflux within and above the canyon is larger than the mean sensible

flow field as well, then the vortex sheet opens up and goesheat flux. The value is more than 0.10 K㨯m/sec, which is the heatover the UCL. There is an updraft of slow flow from theflux from the ground. This means that the heat is gathered from the

UCL, penetrating the SL.
(3)entire canyon and brought to the flushing points. This demonstrates

Caving capped: the vortex sheet is within the canyon (Fig.2

that the flushing events contribute to the heat transport from the(3)). The fast flow field above the canopy is brought down

ground to the atmosphere, and flushing is quite important event in

into the canyon, increasing the velocity in the canyon.
(4)considering the relation between the SL and the UCL.

None-capped: there is no peak in the vorticity (Fig.2 (4)).

c) Cavity Eddy

Since the velocity gap between flow fields within and above46

Figure 3(b) shows the instantaneous cavity eddy. The flow

the positive vortex sheet developing from the windward roof

just above the building is very fast and comes down into thecontinues down into the canyon just in front of the leeward roof.

canyon along the leeward wall. This downward motion makes a

Moreover, a strong negative vortex sheet is developed between thestrong eddy (i.e. cavity eddy). Focusing on vorticity distribution,

positive vortex sheet and the leeward wall. This flow field can be

2

2
(a)

1.8

1.4

1.4

1.2

1.2

z/H

1.6

z/H

1.6

1

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0.5

than 70% of vectors satisfy the condition of w < 0 and0√u2 + w 2 > |√u2 + w 2 | in a quarter leeward region of the street
canyon, where |√u2 + w 2 | represents the averaged √u2 + w 2
in the target xy-section in a street canyon. All the xz-cross sections

-1.0
-1.5

2

1 1.2 1.4 1.6 1.8
x/H

In this study, this cavity eddy is defined as follows: more

-0.5

0
0.2 0.4 0.6 0.8

1.0

1

0.8

0

classified into group (3).

1.5
(b)

1.8

0

0.2 0.4 0.6 0.82

1 1.2 1.4 1.6 1.8
x/H

passing through the center of a building are used, wherein 37

Vortcity(1/sec)events are extracted as the defined cavity eddy.

Fig.3 Example of (a) Flushing, (b) Cavity eddy
(a)

1.8

1.4

1.4

1.2

1.2

z/H

1.6

1

0.8

0.6

0.6

0.4

0.4

0.2

0.2
0

0

0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8
x/H

in Figure 4 (b). For the mean flow, a stronger cavity eddy exists

301.8

and cooler downdraft enters intensely along the leeward wall, thus,

301.6

transporting cooler air into the UCL.

1

0.8

0

302
(b)

1.8

1.6

z/H

Ensemble averaged vector map from these sections is shown

2

2

0

2

0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8
x/H

301.4

Figure 6 shows the xy-cross sectional vector map of

301.2

ensemble averaged cavity eddy at z = 0.0625H and z = H. Since301

each cavity eddy has a spanwise directionality, the flow field’s

θ (K)

2

negative spanwise flow is transformed to make it symmetrical with

Fig.4 Vector map of ensemble averaged (a) Flushing (b) Cavity

respect to y-axis. At z = 0.0625H the downward flow caused by aeddy (Xz-sectional profile through the center of building)
3

3
(b)
2.5

2

2
y/H

y/H

(a)
2.5

1.5

1.5

1

strong cavity eddy reaches the leeward side of the upstream block302(i.e. the upstream side of the street). At z=H the spanwise flow is

301.8

smaller than that of z=0.0625H. In this level, the velocity in the

301.6

upstream side is smaller than that in the downstream side. In both

301.4

figures, the upstream side is warmer than the downstream side. As

301.2

a consequence, when cavity eddy occurs, flow on one side of the301

canyon street is warmer and faster than the other, and the air goes

1

0.5

0.5

0

0

0.5

1

1.5

2.5

2

0

3

0

0.5

1.5

1

x/H

2.5

2

θ (K)

3

to the warmer side of the canyon.

x/HFigure 7 (b) shows the sensible heat flux profile within and

Fig.5 Xy-sectional vector map of ensemble averaged Flushing

above the canyon where cavity eddy occurs. Dashed-line shows the(a) z=0.0625H (b) z=H
3

3
(b)

2.5

2.5

2

2
y/H

y/H

average from all the canyons as well. Within the street canyon,

302

(a)

1.5

1

0.5

0.5

heat flux above UCL is not larger than the mean flow. Thus, cavity

301.6

1.5

1

more heat transports upward than by the mean flow. However the

301.8

eddy transports the heat locally in the street canyon.

301.4

3.3 Relation between flow in the UCL and theturbulent CS

301.2

301

0

0

0.5

1

1.5

2.5

2

Figure 8 shows the streamwise velocity distribution at z =

θ (K)

0

3

0

0.5

1

x/H

1.5

2.5

2

3

H/16 and turbulent CS at SL. The Low speed streak at z = 2H isx/H

Fig.6 Xy-sectional vector map of ensemble averaged Cavity

visualized as a CS by illustrating the negative fluctuation of theeddy (a) z=0.0625H (b) z=H
3.0

(a)

mainstream velocity. This fluctuation contains only the inner scale
motion, distinguished by spatially filtering the instantaneous(b)
Cavity eddy
All

2.5

2.5

2.0

2.0

From this figure, the spanwise velocity is converging under the

1.5

streak. The flow field in the UCL is not localized but well

1.0

organized and is affected significantly by the SL.

z/H

z/H

3.0

1.5
1.0
FlushingAll
Others

0.5
00

0.05

0.10

0.15

w’θ’

0.20

0.25

fluctuation velocity using an area of 10H×10H (Castillo et al. [7]).

Figure 9 shows the CS and the points where flushing occurs.

0.5
00

0.03

0.16

0.09

0.12

0.15

In this figure, the CS is visualized by sweep-ejection events, which

0.18

w’θ’

are obtained from quadrant analysis. Black lines show the sections

Fig.7 Sensible heat flux profile within and above the street

where flushing occurs. Flushing events are extracted from thecanyon (a) Flushing (b) Cavity eddy

canyons of all the xz-cross sections. According to Figure 9,47

flushing events occur along ejections, of which  ′ is negative

boundary layer using large-eddy simulation, Urban Climate′and  is positive. Thus, under the ejection events, flushing

News, International Association for Urban Climate, 33: 19-22,

occurs frequently. Since an ejections correspond to low speed

2009.

streaks, air converges in the spanwise direction at z=0.0625H under2500

ejections. In fact, the flow converges along the spanwise directionin the street canyon during flushing.

0

2000

Figure 10 illustrates the sweep-ejection events and the points
where cavity eddy exists. Most cavity eddy occurs between

1500

-2

1000

-4

500

-6

䌹(m)

ejections and sweeps (white region). This is the region where theair flows in a spanwise direction at z=0.0625H. In fact, the street
canyon with cavity eddy has a spanwise flow at z=0.0625H.

4 Conclusion
In this study, the flow in the UCL affected by the ABL is wellsimulated with LES. The following are the deductions from the0simulation results.

0

500

1000

zInstantaneous flow fields at the street canyon, which arecompletely different from the mean flow, can be classified into

1500
x(m)

2000

2500

u’
(m/s)

Fig.8 Comparing the distribution v’ at z=0.0625H with low speed streak

four groups, using their vorticity as the index.

in surface layer (yellow; v’>2.0m/s, green; v’<-2.0m/s)

zFlushing, a large-scale upward wind in the street canyon, is the2500

upgathering of air and heat at the lowest level of the street
canyon and brought up above the canopy. It has significant
contribution of heat transport.

2000

zCavity eddy, a strong eddy blowing down into the street canyon,transports the cool air from the SL into the canyon. At the lowest

1500
䌹(m)

level, the air going only to the warmer side of the street.
zThe flow field in the UCL corresponds to the turbulent CS in the1000

SL. Specifically, flushing frequently occurs under an ejection
event, and cavity eddy between ejections and sweeps.

500

Reference[1] Inagaki, A.: ‘Atmospheric turbulence over an array of massive

0

cubes’, Phd thesis, Tokyo Institute of Technology, 2008.
[2] Uehara, K., Murakami, S., Oikawa, S., and Wakamatsu, S.:

0

500

1000

1500
x(m)

2000

‘Wind Tunnel Experiments on How Thermal Stratification

Fig.9 Flushing points and sweep/ejection

Affects Flow in and above Urban Street Canyons’, Atmos.

(red; sweep, blue; ejection)

2500

Environ. 34, pp.1553–1562, 2000.
2500
0[3] Kanda, M., Moriwaki, R., and Kasamatsu, F.: ‘Large Eddy
Simulation of Turbulent Organized Structure Within andAbove Explicitly Resolved Cube Arrays’, Boundary-Layer

2000
0

Meteorol. 112, pp.343–368, 2004
[4] Takimoto, H.: ‘PIV measurements of atmospheric turbulence

1500
0
䌹(m)

within and above an outdoor urban scale model’, Master thesis,
Tokyo Institute of Technology, 20091000
0[5] Inagaki, A. and Kanda, M.: ‘Turbulent flow similarity over anarray of cubes in near-neutrally stratified atmospheric flow’. J.

500

Fluid Mech., 615, pp101-120, 2008.
[6] Zhang, J.: ‘LES study on hierarchical turbulence organized0

structure above urban canopy’, Master thesis, Tokyo Instituteof Technology, 2009.
[7] Castillo, M. C. L., Zhang, J., Inagaki, A., Kanda, M. and Letzel,

0

500
0

100
0 0
1000

150
00
1500
x(m)

2000

Fig.10 Cavity eddy points and sweep/ejection

M. O.: ‘Coherent structures of a neutrally stratified urban(red; sweep, blue; ejection)

48

2500

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Process of absorption oil separation using solvent extraction and distillation
Student Number: 08M18170

Name: Yoshihisa YOSHIMURA

Supervisor: Ryuichi EGASHIRA

๋‫ਁۥ‬ЈඥŴᔕသඥǛဇƍƨǳȸȫǿȸȫԈӓ඗ƷЎᩉȗȭǻǹӴ஭ ΰʁ
ǇƣᲦԈӓ඗ƴԃǇǕǔ঺ЎƷɟᢿ ǭȎȪȳᲦȡȁȫȊȕǿȬȳ ƓǑƼਁЈ๋‫঺ۥ‬Ў ȡǿȎȸȫᲦ൦ƔǒƳǔȢȇȫኒƴ‫ݣ‬ƠƯᲦᚘምƴǑǓൢ෩෩࠯ᘖ᧙̞Ǜਖ਼‫ܭ‬ƠƨŵƭƍưᲦԈӓ඗Ʒ๋‫ਁۥ‬ЈƴƓƚǔਁЈႻǛे‫ܭ‬ƠƨӷȢȇȫฆӳཋƷ‫ׅ‬ЎҥᔕသǛᘍƍᲦЭᡓƷਖ਼‫ܭ‬ኽௐǛ‫ࣱܭ‬ႎƴᄩᛐƠƨŵƞǒƴᲦƜǕǒƷኽௐƓǑƼȗȭǻǹᚘምƴؕƮƍƯᲦԈӓ඗Ǜԃᆷእᙐእ࿢ࡸ҄ӳཋƱӷእ࿢҄ӳཋሁƱƴЎᩉƢǔȗȭǻǹǛӳ঺Ღ੩కƠƨŵ
component i in liquid phase, that in vapor phase, the total
1. Introduction
Absorption oil (AO), one of the coal tar fractions (b.p.
pressure and saturated vapor pressure of component i.
=470~550K), contains nitrogen heterocyclic compounds,
Thermodynamic equations consist of two parts: vapor such as quinoline (Q), homocyclic compounds, such as
liquid equilibrium part and liquid - liquid equilibrium
2-methylnaphthalene (2MN), etc., which are useful aspart, and overall equation shows vapor - liquid - liquidindustrial raw materials. Currently, this AO is, firstly,
equilibrium. The UNIFAC thermodynamic model andseparated into acid, base, and neutral fractions by
Antoine equation were applied estimating the activity
reactive extraction with acid and base, and these threecoefficient, γi, and saturated vapor pressure of component
fractions are, secondly, provided to downstream step toi, PiS.
Table 1 Mass fraction of component i in AO
be separated into respective compounds. In this first
Nitrogen heterocyclic compounds
Homocyclic hydrocarbons etc.
separation step, there are some problems such as
Q
IQ
IL
N
1MN
2MN
BP
DBF
0.07
0.02
0.04
0.03
0.10
0.24
0.06
0.10
difficulty in solvent recovery and equipment corrosion.
2.2. Result
To solve these problems, several alternative methods,
2.2.1 Liquid-liquid equilibriumsuch as solvent extraction method, have been studied.
The two liquid phases region in quaternary system is
Solvent extraction technique can separate AO into
shown in Fig.1. For example, solvent extraction
nitrogen compound and the other compounds by polarity
calculation was carried out with the conditions indifference. In one of these studies[1], distillation was
Table 2, and extract phases on tieline were obtained.
applied to recover the extraction solvent, mixture of
Table 3 shows composition of feed, solvent, and extract
methanol(M) and water (W). In this distillation, thephase.
bottom product formed two immiscible liquid phases;
Table 2 Calculation connditions for solvent extraction
extracted components rich and water rich phases. These
Feed
model AO
were easily separated by decantation. As result, thisthree type ofSolventmethanol aqueous solution
study showed that not only recovery of solvent, but also
Mass ratio of solvent to feed, S/F[-]
1further separation between nitrogen compound and thePressure, P[atm]
1others were achieved.
Temperature, T[K]
293
In this study, the AO separation process including theTable 3 Composition of feed, solvent and extract phasesolvent recovery was investigated. In the first, the phasesolvent
extract phasefeedequilibrium of representative components of AO andsolA solB solC extA
extB
extC
Q
0.197
— — — 0.0181 0.040
0.06
solvent components was calculated. Next, the mixture of
0.803— — — 0.0003 0.003
0.02
2MN
AO and solvent components were separated by a simple
M—
0.3 0.5 0.7 0.2635 0.442
0.61
distillation. Finally, some AO separation processes wereW—
0.7 0.5 0.3 0.7181 0.515
0.31
investigated by calculation.
M
2. Phase equilibrium in AO and solvent components
2.1. Calculation
The mass fraction of some main compounds in AO is
extC
shown in Table 1. Q and 2MN are largest components in
extB
nitrogen heterocyclic compounds and homocyclic
extA
compounds in AO, so these were selected asQ
representative components. Phase equilibrium in
Q-2MN-M-W quaternary system was calculated. In thisW
tie line
calculation, following equations were used,< Thermodynamic equation >yi P   iA xiA PiS   iB xiB PiS (i  1, 2, , C )(1)
2MN< Material balance equation >
Fig.1
Phase
diagrams
at 293K and 1atm
F  V  LA  LB
(2)
2.2.2
Vapor-liquidandvapor-liquid-liquid
Fzi  Vyi  LA xiA  LB xiB (i  1, 2, , C )(3)equilibrium
Vapor-liquid and vapor-liquid-liquid equilibrium arewhere xi, yi, P and PiS denote the mole fraction of53

phases at higher temperature in the cases of RunNo1, 2,
10, and 11.In this run, the content of Q in the feed for the
distillation was relatively high.
Figure 3 shows mass fraction in each phase in
RunNo6 and 10. In these graph, effect of temperature onphase equilibrium that average composition fixed in extAand extC are shown also. Feed in RunNo6 and extA had
near water and methanol content, and feed in RunNo10
and extC had near water and methanol content. If
compare the change of mass fraction with increment of
temperature in RunNo6 with phase equilibrium, they hadsame tendency of increment. But the orders of massfraction of Q in RunNo6 were different from that in thecase of extA, and it was due to difference of feed
composition. As result, phase equilibrium by calculation
was confirmed qualitatively.

100

10-1

10-1

10-2

100
10-1

xi , yi [-]100

xi , yi [-]xi , yi [-]difficult to present in tetrahedral coordinate like Fig.1, sothat it is discussed by using Fig.2. These graphs show
effect of temperature on phase equilibrium, and averagecompositions in each graph were fixed in thecomposition of extA, extB and extC. In the figure, resultof only Q and W are shown. When temperature waslower than boiling point, there was one liquid phase. In
area b, there were vapor and liquid phases. In all case,liquid phase was water phase. In area c, there were one
vapor and two liquid phases. In the case of when watercontent was relatively high such as extA, this tree phases
region was narrow. In area d, there were vapor liquid andliquid phases. In the case of extA, liquid phase was waterphase. On the other hand, In the cases of extB and extC,
liquid phase was oil phase. In area e, there is one vapor
phase.

10-2

10-2
Batch simple distillation

10-3
10-3

10-3

a)
10-4
340

350

b) c) d)

e)

360

380

370

10-4

a) b) c)
390

10-4
340

350

T [K]

360

d)
370

e)
380

10-5
340

390

ii) extB
100

xi , yi [-]

10-2

10-3

350

d)
360

e)

370

380

phase
vapor phaseliquid phaseseparated two liquid phases
vapor phaseliquid phaseseparated two liquid phases

370

380

a) L regionb) V-L regionc) V-L-L regiond) V-L regione) V region

i
Q
Q
Q
W
W
W

phase
vapor phaseliquid phaseseparated two liquid phases
vapor phaseliquid phaseseparated two liquid phases390
Phase equilibrium by calculation
i
Q
Q
Q
W
W
W

i) RunNo610-1

xi , yi [-]

i
Q
Q
Q
W
W
W

10-1

360

T [K]

100

a) b) c)

350

T [K]

i) extA

10-4
340

䖃
䕺
䕧
䖃
䕺
䕧

phase
vapor phaseliquid phaseseparated two liquid phases
vapor phaseliquid phaseseparated two liquid phases

10-2

10-3

10-4
340

390

350

360

370

380

390

T [K]

T [K]

iii) extC
Fig.2 Effect of temperature on phase equilibriumii) RunNo10
Fig.3 Mass fraction of component i in each phase

3. Batch simple distillation of AO and solvent mixture
3.1. Experimental
The experimental conditions are shown in Table 4.
The real extract phase and model extract phase and wereused as feeds for the distillation. The composition ofthese feeds are shown in Table 5. The data of RunNo1㹼6
were from previous study[2].
3.2. Result
The still liquids consisted of two immiscible liquid

Based on these results, two flows for solvent recovery
were proposed (Fig.4). One is that bottom productconsist one liquid phase. Solvent components arerecovered as top product, and bottom product become
nitrogen heterocyclic compounds product. But whenwater content in extract phase is high, this flow can’t beapplied because bottom product becomes pure water. The
other is that bottom product is separated into aqueous
phase and organic phase. Solvent components arerecovered as the mixture of top product and aqueous
phase. Organic phase become nitrogen compounds
product.

Table 4 Experimental conditions of batch simple distillation
Mass, F[kg]
0.05-0.06
Sampling interval[min]3Distillation time[min]
45
Initial temperature[K]room temperature

Run No
Q
IQ
IL
N
1MN
2MN
BP
DBF
M
W

1
real
0.02370.00570.00880.00150.00330.00770.00220.0028
0.658
0.291

2
real
0.01050.00250.00250.00020.00040.00080.00030.00030.535
0.442

Table 5 Mass fraction of component i in extract phase
3
4
5
6
7
8
real
model model model model model
0.00278 0.006 0.0007 0.00004 0.006
00.00063—————0.00050—————0.00001—————
0.00003—————0.00003 0.006 0.0007 0.00003
00.006
0.00003—————0.00002
—
—
—
—
—
0.315
0.730 0.5417 0.30647 0.664
0.664
0.697
0.258 0.4568 0.69347 0.330
0.331

54

9
model
0.007
—
—
—
—
0.003
—
—
0.654
0.336

10
model
0.022
—
—
—
—
0.007
—
—
0.655
0.316

11
model
0.042
—
—
—
—
0
—
—
0.641
0.317

solvent recovery was not achieved. In RunNoA2, bottom
product became pure water as expected, and A3,
specification could not be satisfied. In Case1 or 2,
solvent recovery was achieved by flowղ. On the other
hand, in Case3, that was achieved by flowձ.

solvent component
top product+

Table 8 Result of each RunNo
RunNo Case ext Solvent recovery
A1
1
A
䐠
B1
1
B
䐠
C1
1
C
䐠
A2
2
A
䖷
B2
2
B
䐠
C2
2
C
䐠
A3
3
A
䖷
B3
3
B
䐟
C3
3
C
䐟

aqueous phasebottom productorganic phase
nitrogen heterocyclic
compounds productFig.4 Schematic diagram of flow for solvent recovery
4. Process of AO separation
4.1 Process scheme
The schematic flow sheet of AO separation process isshown in Fig.5. The AO is separated roughly into
nitrogen heterocyclic compounds and other compounds
by using solvent extraction with methanol aqueous
solution. Single-stage extraction was applied. Next, thesolvent components in extract phase are recovered by
distillation and nitrogen heterocyclic compounds productis obtained. For this distillation, multi-stage distillation
was applied.

Mass fraction
Figure 6 shows the mass fractions in top product andnitrogen compounds product. In Case3, water was notcontained in the nitrogen heterocyclic compounds
product. On the other hand, when applied Case1 or 2, it
is inevitable that water was contained in the nitrogencompounds product with amount of solubility. The
products that extracted component content was over 90%
were obtained.

0.8

0.8

0.6

0.6

䕔 Q
䕔 2MN
䕔 M
䕔 W

RunNo

RunNo

ii) In nitrogen heterocyclic
compounds product, xi,nitro
Fig.6 Mass fraction of component i in top product andnitrogen heterocyclic compounds producti) In top product, xi,top

Fig.5 Schematic diagram of AO separation process
4.2 Calculation
4.2.-1 Solvent extraction
Single-stage extraction was carried out with same
conditions in chapter 2 (Table 2), and extA, extB and
extC were used as feed of following distillation.
4.2.-2 Distillation
This calculation was conducted by the commercial
process simulator CHEMCAD III. Table 6 shows thecalculation conditions of the distillation. Thermodynamic
model was same as that in previous chapter. The
conditions and specifications of distillation calculation
are summarized in Table 7.According to the processsuggestions in chapter3, three kinds of cases were
examined.

Yield
Figure 7 shows the yield in top product and nitrogencompounds product. In Case1, Y2MN,nitro was larger thanYQ,nitro. It is due to small solubility of 2MN in the
aqueous phase. On the other hand, in Case2 or 3, YQ,nitro
was larger than Y2MN,nitro because 2MN was contained in
top product. Further separation between nitrogencompounds and the others was occurred.

Case2

D = Fz M +F z W

reflux ratio, R = 1

Case3

Wx W < 1䖷10-4

reflux ratio, R = 1

RunNoi) In top product, Yi,top

C3

C2

B2

A1

C3

B3

0

C2

0

C1

Yi, nitro [-]

0.4
0.2

B2

Table 7 Specifications in each CaseSpec 1
Spec 2
Case1
D = Fz M
Wx M < 1䖷10-4

0.6

0.2

C1

Yi, top [-]liquid at boiling point
min|T F-TnF |

0.4

A1

Feed
Feed stage,n F

0.6

B1

[kg]
[atm]

15
total condenser
1
1

䕔 Q
䕔 2MN

0.8

B1

0.8

1
䕔 Q
䕔 2MN

B3

1

Table 6 Calculation conditions of distillation

Number of stages
Condenser
Extract phase flow rate, F
Pressure, P

C2

0

B2

0.2

C1

0

0.4

B1

0.2
(Nitrogen heterocyclic compounds…) Nitrogen heterocyclic
compounds productraffinate phase(Homocyclic compounds, …)

䕔 Q
䕔 2MN
䕔 M
䕔 W

C3

0.4

B3

xi, nitro [-]1

B1xi, top [-]1

A1

feed (AO)

Extractor

extract phase

Distillator(Methanol, Water, …)
solvent

A1

nitrogen heterocyclic
compounds productC3

extract phase

B3

extract phaseC2

top productB2

ղsolvent component

C1

ձ

RunNo

ii) In nitrogen heterocyclic
compounds product, Yi,nitro

Fig.7 Yield of component i in top product and nitrogen
heterocyclic compounds product4.3 Result
The results are shown in Table 8. In RunNoA2 and A3,

55

2MN = 2-methylnaphthalene
AO = absorption oilBP= biphenylD= distillate
DBF = dibenzofuranE= extraction solventF= feedi= component iIL= indole
IQ= isoquinolineN= naphthalene
nitro = nitrogen heterocyclic compounds productQ= quinoline
top = top product<Superscript>A= liquid phase AB= liquid phase B
S= saturated vapor
Literature Cited[1] Salim. C, Egashira. R; Journal of the Japan
Petroleum Institute, 49, (6), 326 (2006)[2] Yoshimura.Y; Bachelor thesis, Tokyo Tech (2008)

5000
4000
3000

2000

C3

B3

C2

B2

C1

0

B1

1000

A1

Energy requirement, Q [kj/kg-feed]

Energy requirement (reboiler duty)
Figure 8 shows the energy requirement that is equal toreboiler duty in the each run. The energy requirement in
Case3 was higher than that in Case1 or 2. In addition, therange of distillation temperature in Case3 was very wide.
In Case1 and 2, the highest temperature in the column
was about 372K, but that in Case3 was about 510K.

RunNo

Fig.8 Energy requirement, Q
When water content in extract phase is low such as
extC, Case2 shows high purity, further separation
between Q and 2MN and low energy requirement. Whenwater content in extract phase is high such as extA, it isnecessary to select Case1 because bottom productbecame pure water so solvent recovery can’t be achievedin Case2 or 3.
4. Conclusions
First, by calculation of phase equilibrium, it was
confirmed that when water content in feed was highvapor-liquid-liquid three phases region was small andliquid phase was water at high temperature. On the other
hand, when water content was low, liquid phase was oil.
Next, by batch simple distillation, phase equilibrium by
calculation was confirmed qualitatively. Based on the
result of phase equilibrium estimation and batch simple
distillation, two type of solvent recovery were proposed.
Finally, process of AO separation was estimated andaccessed, and AO separation and solvent recovery wereachieved.
Acknowledgment
The author wish to thank JFE Chemical Co., Ltd. forproviding coal tar absorption oil.
NomenclaturesC= number of components[-]D= mass of distillate[kg]
E= mass of extract phase[kg]
F= mass of feed[kg]
L= mole of liquid phase[mol]Q= energy requirement[kj/kg-feed]R= reflux ratio[-]S= mass of extraction solvent[kg]
V= mole of vapor phase[mol]W= mass of bottom product[kg]x= mole or mass fraction in liquid phase [-]y= mole or mass fraction in vapor phase [-]z= mole or mass fraction in feed[-]Y= yield<Greek>γ= activity coefficient[-]<Subscript>
1MN = 1-methylnaphthalene

56

FUTURE ESTIMATION OF THE AIRCRAFT CO2 EMISSIONS FOR FLIGHTSBETWEEN JAPAN, CHINA AND SOUTH KOREA
Student Number: 08M18186

Name: Ying QIAOSupervisor: Shinya HANAOKA

ᣣਛ㖧〝✢ߦዞ⥶ߔࠆ⥶ⓨᯏ߆ࠄߩ CO2 ឃ಴㊂ߩ዁᧪ផቯ༜ 㗧ᧄ⺰ᢥߢߪ‫⥶ޔ‬ⓨᯏ߆ࠄߩ CO2 ឃ಴㊂ࠍ㜞޿♖ᐲߢផቯߔࠆߚ߼‫ޔ‬ᣣਛ㖧㑆ߩ࿖㓙⋥ⴕଢߦ⌕⋡ߒ‫ޔ‬2008 ᐕߩ CO2 ឃ಴㊂ࠍ〝✢೎ߦ▚಴ߒߚ‫ޕ‬Grey ੍᷹ࡕ࠺࡞ߦࠃࠅ‫ޔ‬2020 ᐕߩหߓ〝✢ߩ⥶ⓨᣏቴ㔛ⷐࠍ੍᷹ߒ‫⥶ޔ‬ⓨᯏߩᯏ᧚ࠨࠗ࠭߇หߓ᭴ᚑߢ޽ࠆߣ઒ቯߒߡ‫ޔ‬CO2
ឃ಴㊂ࠍផቯߒߚ‫ޔߚ߹ޕ‬ᯏ᧚ࠨࠗ࠭ࠍᄌᦝߒߚᤨߩ CO2 ឃ಴㊂ߩᄌൻࠍಽᨆߒߚ‫ޕ‬
aimed at the world’s flights. There is few research
for focusing on specific regions or someindividual routes.
This paper selects the three economic growthnations in North Asia, Japan, China and Korea, as
the target to analyze the current situation andfuture trends. Firstly, based on the aircrafts ofinternational direct flights among these nations,the amount of CO2 emissions is obtained in 2008.
Secondly, by forecasting the passenger traffic for
aircraft of 2020, CO2 emissions in 2020 isapproximately estimated. Finally, this paper
analyzes the impacts of changing the aircraft size
on CO2 emissions.

1. Introduction
In current years, with the development ofsocio-economic globalization, international trade
and tourism using air transport have been
becoming increasingly common. In the mean time,the issue of CO2 emission caused by thephenomenon deserves our serious consideration.
The latest surveys show that the amount of CO2
emission in aviation sector has contributed to forapproximate 2.5%~3% [1] of the world’s total
amount. In addition, it is estimated that the airtransport passenger traffic will show a growthtrend in the future [2], which likely increase CO2
emission and arouse adverse impacts. Thus, it isimportant to explore how to predict the future
CO2 emission of aircraft and how to adopt someefficient methods to decrease the emissions.
So far, different nations have varied attention
degrees to research the CO2 emission of aircraft.
Europe and North America conducted a large
number of researches in relation to calculating thecurrent CO2 emission of aircraft and predicting
the future trend. Also, they formulated relatedplans to limit CO2 emission from civilian aircraft.
Asian countries, however, spent less resource instudying CO2 emission of aircraft. Recently, alongwith the economic and trade development, thedemand for aircraft in some Asian countries led
by China is becoming increasingly large. Basedon the data from ICAO (International Civil
Aviation Organization), the average growth rateof demand for aircraft in Asia is 6.1% from 2002
to 2015, which is larger than that of world
average growth rate (4.4%). With unpredictable
future trend of the Asian aviation market, the airtransport demands are expected to rapidly
increase, as well as CO2 emissions. Therefore,should be useful for Asia to research on CO2emission of aircraft.
Some researches for CO2 emission of aircraft

2. Estimation of current CO2 emission
2.1 Basic concepts

Figure1. The concept sketch map of each steps
during the process of flight
Note㧦1 feet=0.3048m, 3000 feet equals to 914.4m.
Source: CO2 Emissions from Fuel Combustion (2004)

There are two phases of flights needed to betaken into account for calculating aircraft CO2
emission, i.e. cruise and LTO (Landing and Take
Off) cycle. When the aircraft flies at the height of
3000ft above, it is called cruise. The rest of thestages is called LTO cycle, which includes 4
modes. They are take off, climb out, approach,and idle mode. Figure 1 shows the conceptual

57

respectively. Equation 2.3 describes the method of
calculating the total CO2 emission for each airline.

sketch of each step during the process of flight.

2.2 Data collection

Table2.

The data for calculating CO 2 emission are
obtained from OAG (Official Airline Guide) time
table, LTO cycle Engine Emission Data fromICAO and BADA (Base of Aircraft Data) fromEuro Control Experimental Center. At first,
international direct flights between China, Japan,
and Korea are extracted from OAG time table,
including 112 flights between China and Japan,
78 flights between China and Korea, 68 flights
between Japan and Korea. They are shown in
Table 1. In addition, all aircraft type of each flight,
arrival and departure time, and flight frequency
per week can be found. Secondly, 4 modes time of
LTO cycle referred to ICAO provision, i.e. takeoff 0.7 min, clime out 2.2 min, approach 4 min,and idle 26 min. Therefore, the flight cruisingtime can be calculated by subtracting LTO timefrom the whole flight time (the differences
between arrival time and departure time).The
transit flights are not considered in this research.
Concerning each mode fuel flow of LTO cycleof each aircraft model, the engine data ofcorresponding aircraft type can be extracted,
which are used in direct flights between China,
Japan and Korea, from 2005 Jet Engine
Specification Database of Home of the X-Plane
Flight Simulator Community. Afterwards eachmode fuel flow of LTO cycle of corresponding
engine will be obtained from ICAO Aircraft
Engine Emissions Databank of 2008 version.
From BADA, the fuel flow at maximum altitude
is selected as the cruising fuel flow of eachaircraft model.

The fuel flow of aircraft type  each mode fuel flow in LTO cycle   f (2.1)
QLTO cycle     mode time engine䇭 number  

Qcruising  cruising time cruising fuel flow f‫ޓޓ‬(2.2)‫ޓ‬f : ‫ޓ‬CO2 EI ( Emission‫ޓ‬Index)‫ޓ‬(3.15kg CO 2 / kg Fuel)

Q2008   QLTOcycle㧗Qcruising  freq  52week (2.3)
freq : frequency per week

2.4 Result
The total CO2 emission of international flights
among these three nations is 4353.0 thousand tons.
China-Japan flights has the largest proportion
which accounts for 45.76%, 1992.0 thousand tons,
while Japan-Korea flights accounts for 28.96%,
1261.0 thousand tons, and China-Korea flights
accounts for 25.28%, 1100.0 thousand tons.

Table1. Flights between China, Japan and Korea

3. CO2 emission forecast in 2020
It is required to forecast the aircraft passenger
traffic before forecasting the CO2 emission in2020.

3.1 Passenger traffic forecast
When forecasting passenger traffic, it isessential to consider about how to choose theforecasting model and select the sample data.
Since external factors makes a great impact on
this forecast, such as the development  ofsocio-economic development, natural disasters, it
makes passenger sample data became fluctuated.
In addition, limited valid data sample wasprovided in air transport field. According to thefeature of aircraft passenger traffic forecast, thispaper selected the Grey Theory Model — GM(1,1) forecasting model[3].
Usually, GM (1,1) model requires less

2.3 CO2 emission calculation method
By using Equations 2.1, 2.2 and 2.3, cancalculate the CO2 emission of all airlines between
China, Japan, and Korea in 2008. Equation 2.1
and 2.2 are used to calculate the LTO cycle CO2
emission and cruising CO2 emission of each flight58

sequential data, and, thus, it satisfies data
limitation in forecasting aircraft passenger traffic.
Generally, 4 or 5 years of time series data are
used.

Average relative residual is classified into 4levels to identify the accuracy of model. The
detail criteria are shown in Table 3.
Table 3 GM (1,1)forecasting model accuracy

3.2 Formulation of GM (1,1) model
Given there are n samples of a series,
X(0)={X(0)(1), X(0)(2), X(0)(3), …, X(0)(n)}Ǆ
Step1: Do AGO (Accumulated Generating
Operation) once through Equation 3.1. A newseries is obtained like X(1)={X(1)(1), X(1)(2),
X(1)(3), …, X(1)(n)}.

3.4 Data
TFS Module (Traffic by Flight Stage) fromICAO data will be adopted. Considering wide
fluctuation in passenger traffic, make predictions
based on the latest information that is thepassenger data from year 2004 to 2008. The
database includes passenger data within five years,
less than five year and transit flights in 258 flights
between China, Korea and Japan. Passenger data,
more than 3 years, make forecast based on GM (1,1) model. For 3 and 2 years passenger data, makeuse least of square and first-order linear equation
method respectively. The data processing ofmodule GM (1,1) utilizes numerical analysis
software MATLAB R2007b.

k

X (1) (k )   X ( 0) (m) (3.1)
m 1

Step2 㧦 The
Equation 3.2

establishmentofdifferential

dX (1)
 aX (1)  b (3.2) (a,b :unknown coefficient)
dt


"a 



Step3㧦Given a  b  ˈcalculate a utilizing! 
least-squares method.
a  ( BT B) 1 BT Y n

(3.3)

#
#

"

$
$

1 (1)
X (1)  X (1) (2)2
1 (1)X (2)  X (1) (3)
B
2

1 (1)
X (n - 1)  X (0) (n)
! 2

Here,

#

$


1

1


1


" X (0) (2)

X (0) (3)
Yn 
 

(0)
! X ( n) 

3.5 Results of passenger traffic forecast
This paper makes predictions on passengers of
258 airlines. In 2020, passengers will be
1.086×107 between Japan and China, 1.59 times
that of 2008(0.684×107). Between Japan and
Korea, passenger will increase to 1.450×107, 1.68
times of 2008(0.864×107). And passengers will be
3.551×107 between China and Korea, 4.86 timesof 2008(0.731×107). The total passenger will be
6.087×107 between three nations, 2.67 times thatof 2008(2.279×107).

Step4: Substitute a, b into differential Equation
3.2, obtain forecasting modelb  ai b"
X (1) (i  1)  X ( 0) (1)
e 

(3.4)

     !   a    a  (1)X (i 1) is forecasting model of X (1).
Step5 㧦 Do IAGO(Inverse Accumulated
Generating Operation) once to forecastingmodel X (1) (i  1) , obtain the forecasting modelof original series X ( 0 )

(0)

X


(1)

3.6 CO2 emission in 2020

(1)

By assuming the aircraft type and flight time inyear 2020 are the same with those in year 2008,
all kinds of fuel flow remains unchanged. In otherwords, only flight frequency per year changes
while CO2 emission for each flight remains thesame compared with those in 2008. So for eachflight, the amount of CO2 emission is proportional
to passengers. From Equation 3.9, and 3.10, theCO2 emission in year 2020 can be calculated.

(i  1)  X (i  1) X (i ) (3.5)

3.3 Model residual test
After solving the forecasting value, it isrequired to do residual test for examining theaccuracy of forecasting models. ThroughEquations 3.6, 3.7and 3.8, the value of averagerelative residual can be obtained.
A)

Residual:% (0 ) (i ) airline passenger2020 airline frequency2020
airline passenger2008 airline frequency2008( 0) (i) i 1,2,3 ,n (3.6)
        Q 䯷Q   total
 frequency                % (i)  X (i ) X
( 0)

B)

( 0)



Relative residual: e( 0) (i)cruisingQ  e(0) (i) % (0)(i) 100%  i 1,2,3,n (3.7)
  
(0)

X

C)

Q2020㧩

(i )Average relative residual: e ( 0) (avg) e ( 0) (avg) 

LT O

2020

䯷QLT O  total frequency2008

cruising

CO2 emission2020(3.9)
CO2 emission2008

passenger of each airline2020
 Q2008 (3.10)
passenger of each airline2008

The total CO2 emission is about 9709 thousand
tons from flights between China, Korea and Japan,

1 n (0) e (i) (3.8)
n i 159

aircraft (B767). The number of flight frequency F
will be calculated for this medium-sized aircraft
through Equation 4.3 as follows:               approximately 2.23 time that of 2008(4,353
thousand tons).

4. The effect on the CO2 emission bychanging aircraft size

F

䯴 F

2020

(large - type)  model seat  load factor䯵

medium seat(B767) load factor(4.3)

4). Based on the result of F calculated from step 3
and the formula of CO2 emission, the CO2
emission Q medium of medium-sized aircraft will
be calculated. By comparing with the CO2
emission of large-sized aircraft, calculate thedifference for the two CO2 emissions. Finally, thetotal difference Q change will be obtained by
calculating the sum of CO2 emission differencesfrom 28 flights.
Qchange  Qlarge Qmedium (4.4)

Adopt simulation changing the size of theaircraft to evaluate the effect on the CO2 emission,and use the sample of those flights with large typeaircraft and the demand for more than 100
thousand passengers in 2008. Specifically, there
are two cases involved: large type aircraft tomedium size, and large to small size. It is
assumed that the seat load factors are 70% foreach flight and no new models of aircraft are
adopt since then to 2020. Based on the result of
calculation number of standard seating by using
TFS data, the aircrafts can be classified into three
types: large type with more than 300 seats;
medium type with 150 to 300 seats and small typewith less than 150 seats. There are total 28 flights
are adapted: 6 flights between Japan and China, 8flights between Japan and Korea, 14 flights
between China and Korea, which are showed on
the Table 4 below:

4.2 Results
In case 1(changing large-sized aircrafts withmedium-sized aircrafts), the CO2 emission of
aircrafts is reduced about 43.06 thousand tons. In
case 2 (from large-sized aircrafts to small-sizedaircrafts), the CO2 emission of aircrafts is reduced
about 182.45 thousand tons. According to theresults identified above, it can be concluded thatchanging large-sized aircrafts into medium or
small ones can reduce CO2 emission.

Table4. The extracted flights between
China, Japan and Korea

5. Conclusion
In this paper, the CO2 emission in 2008 can bemastered based on the research that used for 258
international direct airlines among China, Japanand Korea. By using GM (1,1) model, it can bepredicted the passenger traffic for each flight andcalculated the CO2 emission in 2020, 2.23 times
that of 2008. The simulation of changing the sizeof the aircraft was adopted to evaluate the effecton the CO2 emission. It has been found that theCO2 emission can be reduced by using medium or
small size aircraft instead of using large typeaircraft. This paper does not take account of thecase that there are new engine aircrafts model
entered in the future. Considering this case, theCO2 emission forecasting method should makefurther adjustments.

4.1 Calculation
By taking one flight as an example, the method
to calculate CO2 emission resulted from changing
large-sized aircraft (B744) into a medium-sizedaircraft (B767) is as follows.
1). Select the large-sized flight, and calculate thefrequency in 2008 (F2008) before changing theaircraft size. The predicted frequency of flights in2020 (F2020) can be calculated through the
Equation 4.2 as follows:
F2008 (Frequency) Revenue Passenger
seat  load factor(4.1)

F2020 (Frequency)  F2008 (Frequency) Forecasting passenger traffic in 2020
(4.2)
Passenger traffic in 2008

Reference[1] Kim, B. et al.: System for Assessing Aviation’s
Global Emissions (SAGE) Version 1.5: Global Aviation
Emissions Inventories for 2000 through 2004.
Washington,DC,USA,2005.
[2] Airbus Industrie: Global Market Forecast: The
Future of Flying 2006-2025. Airbus S.A.S., Blagnac
Cedex, France, 2006
[3] Deng Ju long. Introduction to grey system theory[J].
The journal of Grey System, 1989, 1: 1-24

2). Calculate the total CO2 emission forlarge-sized aircraft Q large based on the Equation
2.3 and the results of F2020.
3). Select one medium-sized aircraft˄B767˅toreplace all the large-sized ones, and all thepassengers are transported by using medium-sized60

Numerical Analysis of Micro-Scale Cavity Flow
Student Number: 08M18192 Name: Xin XIN Supervisor: Yoshihiro MOCHIMARU

䉨䊞䊎䊁䉞ౝ䈜䈼䉍ᵹേ⸃ᨆࠪࡦࠠࡦᧄ⎇ⓥߢߪ㧘࠽ࡆࠛࠬ࠻࡯ࠢࠬᣇ⒟ᑼ㧔Navier-Stokes equations, એ㒠 N-S ߣߔࠆ㧕ࠍᡰ㈩ᣇ⒟ᑼߣߒ㧘=0.1~0.001
ߩ▸࿐ߢ, ࠬ࡝࠶ࡊᵹࠇߩႺ⇇᧦ઙߩਅߢ, ࠬࡍࠢ࠻࡞Ꮕಽᴺࠍ↪޿ߡߔߴࠅᵹേࠍ⸃ᨆߔࠆߎߣߦࠃࠅ㧘ࠬࡍࠢ࠻࡞Ꮕಽ
ᴺߩ⸃ᨆኻ⽎▸࿐ߩ᜛ᄢࠍ࿑ࠆߎߣࠍ⋡⊛ߣߔࠆ㧚⸃ᨆ଀ߣߒߡ㧘ੑᰴరߩࠠࡖࡆ࠹ࠖౝߩ㕖࿶❗ᕈ࠾ࡘ࡯࠻ࡦᵹ૕ࠍ⸃ᨆ
ኻ⽎ߣߒߡ㧘ߔߴࠅߩႺ⇇᧦ઙߩਅ㧘ᐳᮡᄌ឵ࠍⴕߞߚ⸘▚ⓨ㑆ߦ޽ࠆᧂ⍮㊂㧔ᵹࠇ㑐ᢙߣ᷵ᐲ㧕ࠍቢ஻ߥ⚖ᢙ㧔ࡈ࡯࡝ࠛ⚖ᢙ㧕ߦዷ㐿ߒߚᓟ㧘ⓨ㑆਄ߩ⁛┙ᄌᢙࠍ৻ߟಽ㔌ߒ㧘න৻⋥੤ᩰሶࠍᒻᚑߒᡰ㈩ᣇ⒟ᑼࠍ৻ᣇะߩߺ㔌ᢔൻߒߡ⸃߈㧘ᤨ㑆ㅴⴕߢቯᏱ⸃ࠍ᳞߼ࠆ㧚Key words: numerical simulation, spectral finite difference scheme, wall slip, Navier-Stokes equation1.

equation including the velocity-slip boundary condition withthe parameter of  has been used in this report.
A micro-scale rectangular two-dimensional cavity flow isnumerically analyzed with a slip boundary condition, using
a spectral finite difference scheme, through a boundary
fitted conformal mapping system. A semi-implicit time
integration scheme in a diagonal predominant form isapplied to get a steady-state solution.

Introduction

As the number of applications of micro electro mechanicalsystem (MEMS) increases, flows of micro-channel andmicro-cavity to be analyzed are also increasing [1]. The flowin micro-scale shows different behaviors as compared withthat in macro-scale. One possible basic step inunderstanding these processes is numerical study of fluid
flow through a micro-scale cavity. In case of a small scale, thecontinuum approach under no-slip boundary condition isinvalid. A dimensionless parameter that determines thedegree of appropriateness of the continuum model is a
Knudsen number, , which is defined as the ratio of thefluid mean-free-path λ to the macroscopic length scale ofthe physical system L,
λ
 =
(1)


2.

Analysis

2.1 Basic Equations
A system of partial differential equations are formulated
from a vorticity transport equation in a boundary-fitted
conformal coordinate system, where the rectangular region
is mapped into a circular region in  for which

Figure 1 describes different regimes of fluid flows. As 
increases, rarefaction effect becomes more pronounced andeventually the continuum assumption breaks down.

exp( + ) ≡  =

(1 +
(1 −

)
)

(2)

where
is an analytic function of +in a Cartesian(),
coordinate ,dimensionless, such that along the closed
boundary is real and varies monotonously from −∞ to−∞ with increasing . In this case if the said domain isexpressed by −∞ <  ≤ 0 , − <  <  , then thegoverning equations lead tovorticity transport equation    
1 2  2 
+
−
=

+

     Re  2 2(3)

Relationship between the stream function and the vorticity
is given by2 2  = −  2 + 2 Fig. 1 Knudsen number regimes
The regimes are variable according to different flows. It has
been pointed out that for a system with  < 0.001, thefluid flow can be treated as continuum. For  > 10 thesystem can be considered as a free-molecular flow. The fluid
flow for 0.001 <  < 10 , which often appears in the
MEMS [1]. Due to these results, the regime is treated as
0.001 <  < 0.1 and the solution of the Navier-Stokes(4)

together with
=




61

( , ) 
2
()
,
= ′
, ! ≡ exp⁡
| ||1 + |2(, ) !

: ≡horizontal width of the cavity/
: Jacobian: Reynolds number(5): dimensionless time based on ⁄: ≡ ($⁄ )√,
: Cartesian coordinate system,  : elliptic coordinate system$: kinematic viscosity of the fluid: dimensionless stream function ⁄: dimensionless vorticity 
In general, for a given simply-connected cavity domain, is
not determined uniquely [2]. In the current case, at least thefollowings (6), (7) are possible:
1 + ∑∞
=1 @ cos 2A=− sin A@ =−2sinh2 ()sinh(2 + 1)sinh(2 − 1)
A≡

&0

(6)

(7)

2.3

2.2 Formulation by Spectral Finite Difference Scheme
In case of periodic base functions, it is necessary to set up acoordinate system such that the physical quantities are
periodic at least along one coordinate.
In order to express the physical quantities and todecompose the governing equations into each spectral
component, the following Fourier series is used.
∞

(9)

'=1
∞

(, , ) = % &' (!, ) cos ' + % *' (!, ) sin ''=0

&' and *' … are the Fourier coefficients, which are thefunctions of argument ! and .
Jacobian, , is an even function in this case and also can be
decomposed as(11)

Substituting these expressions shown above into theoriginal governing equations (3), (4), gives the following form
∞

'=0

2.4

(12)

'=1

(13)

3*' (!, ) = 0(14)

The spatial variable  is separated and two systems ofsimultaneous partial differential equations (with respect toone spatial variable ! and dimensionless time  ) are
obtained.
Vorticity transport equation
&0

&4
1  2 &4− 
− 42 &4  = 5&4
Re  2sinh(7) ⁡sinh(7) + sinh(4 ∙ 7)

(19)

(20)

Finite-Difference Approximations

Partial derivatives are approximated by using the meshpoint.
For time, it is simulated by a forward difference scheme.
3(!, ) 3(!,  +△ ) − 3(!, )
(21)
=

△
For space, they are simulated by central difference schemewith errors of :(ℎ2 ).

which gives
3&' (!, ) = 0

Grid Generation Method

Where 7 is a suitable positive constant. This results insubstantially doubly-exponentially transformation on thephysical space. In the limit 7 → 0 it produces a uniform grid
spacing in !.
7: grid generation parameter

'=0

∞

(18)

B=

∞

% 3&' (!, ) cos ' + % 3*' (!, ) sin ' = 0

 2 *4
&0 s4 − − 42 *4  = C*4
 2sinh[(4 − )7]! = !4 +1 − B D
+ 1E
sinh7

(10)

'=1

(, ) = % &' () &-* '(17)The computational space is discretized by boundary-fitted
curvilinear coordinate grid which is also called as structured
grid.
Let max+2 points distribute on !(0 ≤ ! ≤ 1) for a line ofradius 1 in length of the computational space. !4 +1 is on
the boundary and !0 is in the centre. They are bothallocated explicit. Discretization on the computational space
may be made in the following non-uniform grid spacing, thatis, the coordinate ! for the -th grid point is given by

∞

'=0
∞

 2 &4
&0 &4 − − 42 &4  = C&4
 2

Decomposition itself can be executed analytically and theresults may be evaluated in terms of numerical integration.
In such way, one spatial variable can be separated to give asystem of simultaneous partial differential equations.
In the analysis the series is assumed converging
asymptotically as a number of terms tends to infinity. The
resulting system of simultaneous partial differential
equations can be solved by truncating the series up to acertain order (in this study the number of terms equals to10) and by a finite difference approximation which accepts
non-uniform grid spacing a great deal.

| | ≤ ⁄2 , 0 ≤ ≤ .
: horizontal: vertically upward
H : dimensionless cavity height(, , ) = % &' (!, ) cos ' + % *' (!, ) sin '(16)

Relationship between the stream function and the vorticity
is given by(8)+

1  2 *4*4
− 
− 42 *4  = 5*4
Re  23(!n , )ℎ−1=HI 3(!n+1 , )(ℎℎ  + ℎ−1 )!
ℎ − ℎ−1+J
K 3(!n , )ℎ ℎ−1−ℎ+H
I 3(!n−1 , )ℎ−1 (ℎ + ℎ−1 )

(15)

62

(22)

 2 3(!n , )
=H
! 2
ℎ

2
I 3(!n+1 , ) (ℎ + ℎ−1 )−2+JK 3(!n , )ℎ ℎ−1
2+H
I 3(!n−1 , )ℎ−1 (ℎ + ℎ−1 )ℎ: mesh spacing

2.5

Numerical Scheme

BB = −=2⎧! 2 O−(−ℎ) − ℎ P − 1 㧔26a㧕opening⎪  ℎ2
!


⎨
⎪
⎩

(−ℎ)

1 !

Kn 

(−ℎ)

(26)

(26b) walls

The Fourier coefficients (both sine and cosine) of  can becomputed by the integral to (26a) and (26b).

2.7

2.8

(24)

&0 (!0 = 0, ) = &0 (!1 , ) = 0(25)

c0 (!0 = 0, ) = c0 (!1 , ) = 0

(26)

(10)

Substituting these expressions into the original governing
equation (4) which can be written as
2 
1

= J− − ! K
! 2 ! 2
!

(22)

(28)

Convergence Criterion_4 =

max| +1 (!, ) −   (!, )|≤ &_| +1 (! ∗ ,  ∗ )|(29)

_4 =

max| +1 (!, ) −   (!, )|≤ &_|+1 (! ∗ ,  ∗ )|(30)

Table 1 Parameters
HeightWidth
Grid No.
Grid Generation Parameter 7
Partition of Angle(23)

This can be considered as the relation between vorticity andstream function at the opening of the cavity.
On the walls, as postulated by Navier [3], the slip velocity is
assumed to be proportional to the wall shear.
BB = S J K
 BB& = 0 ( = 0, 1, ⋯ , ^), *' = 0 ( ' = 1,2, ⋯ , ^)3. Results and Discussion
3.1 Results

Use a finite number (the 2nd order) of terms of the Taylor
series to approximate above function at the point ! = 1. The
following was obtained.
!2 2
1

O−(−ℎ) − ℎ P −
2
 ℎ
!


(27)

∗
: superscript at the location where the maximum is
given
2.8.2 Time IntervalThis analysis requires a convergent solution with semiimplicit time integration scheme. To make the same order
between left and right sides in the matrix operations insolving simultaneous equation, the following condition is
used
∆
≈1
(31)
B 2 ∙ B
: grid interval∆: time increment

2.6.2 Boundary Conditions
At the opening of the cavity, the velocity is assumed tocontain only horizontal component. It can be expressed as

=0


& = 0 ( = 0, 1, ⋯ , ^), *' = 0( ' = 1,2, ⋯ , ^)2.8.1 Convergence Conditions
The convergence criterion of the solution is executed on thephysical space.
The error rate of ε_max by the step as follows are defined as

2.6.1 Auxiliary Conditions
Since the point ! ≡   = 0 is by no means a singular point,
as a necessary condition the following hold:c0 M!0, N c0 M!0, N
=
=0
!
!

Initial Conditions

The flow can be supposed stood still at  = 0 as an initialcondition.

2.6 Boundary Conditions

=

(25)

Summing up(23)

As for partial differential equation, analytical solution
cannot be obtained because of a non-linear reason except a
special case. We can obtain two sets of simultaneous
equations (one is for sine the other is for cosine.) by forming
the separated equations (15)~(18). The band scheme isadopted to create the coefficient matrices.
The discretized simultaneous equations can be integrated
semi-implicitly. Systems of simultaneous algebraic equations
are solved with the triangular factorization method (LU
decomposition).

1 
= 1,
 

1 

=  J K= −
 BB

1

22
2 × 10−1
⁄500

(24)

Where S is an accommodation coefficient and in thecurrent case S = 1.
It also be expressed by vorticity  as
Fig. 2 The boundary-fitted coordinate at grid number=2263

non-uniform grid

uniform grid

Kn=0.1Kn=0.010.04

1Flow rate

Location

0.02
0.8
0.6
0.4

0
-0.02
-0.04
-0.06

0.2

-0.08

0
0

5

10

Grid No.

15

0

20

200

400

600

800

1000

Grid No. on the boundary

Fig. 8 Comparison of steady-state flow rate at two different

Fig. 3 The grid profile at grid number=22tv
3.2 Discussion
It is understood well that the speed of getting solution is fastenough with the method. This is the feature of the spectral
finite difference scheme. However, because the convergencecriterion is carried out on the physical space, not thecomputational space, it took too much time to transformbetween the two spaces for the criterion
As factors affecting the accuracy of the numerical solution,
there are truncation error and discretization error. A Fourier
seriesexpandedbytrigonometricfunctions{1, cos , sin  ( = 1, 2, ⋯ )} is complete. Both sides areexactly equal to the infinite number of terms. Approximation
to ^3 finite number of terms causes truncation error.
Because higher frequency decreases more sharp and rapidly
It has been confirmed that the approximation will converge
well when ^3 tends to score. In this study, ^3 equals to 10.
Truncation error in discretization is based on the Taylor
series expansion, the present study has the second orderaccuracy.

Fig. 4 Steady-state streamlines at kq=1, tv=1×10-2,
Relative Error=3×10-4

Fig. 5 Steady-state streamlines at =1, =1×10-1,
Relative Error=3×10-4
1

Relative Error

stream function

4.
Conclusion
In this study a micro-scale rectangular two-dimensional
cavity flow was numerically analyzed with a slip boundary
condition, using a spectral finite difference scheme. ranged from 0.1 to 0.001 under slip flow boundary
conditions.
As the results, application of the said method to the slip
boundary cavity flow is confirmed. Although the rarefied
effect is important in this range, character of the continuum
still remains. Therefore, N-S equations are still applicable
methods in this case. The phenomenon was found that theflow rate in the boundary layer grows when  increasing.
We can say that slip effect contributes to the net momentum
exchange. These numerical models proved that  plays amajor role in the slip flow.

0.1

0.01

0.001

0.0001
0

1000

2000

3000

4000

5000

6000

7000

Iter. No.

Fig. 6 Relative Error at kq=1, tv=1×10-2
1

Relative Error

stream function
0.1

0.01

5.
Reference
1. C. M. HoC. TaiY. Micro-electro-mechanical systems(MEMS) and fluid flows. 30 : Ann. Rev. FLuid mech, 1998.
579.
2. MOCHIMARUYoshihiro. Effectiveness of a apectral
Finite Difference Scheme to Natural Convection. 8thInternational Symposium on Computational Fluid
Dynamics, 1999.
3. WhiteM.Frank. VISCOUS FLUID FLOW. p49 :McGRAW-HILL INTERNATIONAL EDITION.

0.001

0.0001
0

1000

2000

3000

4000

5000

6000

7000

Iter. No.

Fig. 7 Relative Error at kq=1, tv=1×10-1

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A ROBUST IMAGE MATCHING METHODTO LOCAL AFFINE TRANSFORM
BY USING SCALE INVARIANT FEATURES
Student Number:08M18223 Name:Jiaping Li Supervisor: Yukihiko Yamasita
スケール不変特徴点を用いた画像の局所アフィン変換に頑健な対応点検索法李佳平

特徴点に基づくマッチング手法の利用と研究が，コンピュータビジョンの分野において急速に発展している．そこで本研究では Scale-Invariant Feature Transform(SIFT) を利用した局所アフィン変換に頑健な対応点検索法を提案する．
SIFT は，特徴点の検出とその点の特徴量の抽出を行う手法である．SIFT により検出される特徴点とその特徴量は，画像の回転・スケール変化に対して不変であるが，アフィン変換に対して不変ではない．
本研究では，SIFT によって対応付けられた局所領域間のアフィン変換関係を利用し，対応点を再検索するアルゴリズムを提案する．そして，それを C 言語で実装し，その有効性を示す．

1

INTRODUCTION

points. 3) Calculation of their orientations. 4) Generation of their feature vectors. In 1), the key point
candidates and their scales are detected by the DoG(Difference of Gaussian) process. In 2), from the candidates, the key points are chosen by their main curvature. Next, the sub-pixel-position is speciﬁed for eachkey point. In 3), the histogram is generated from gradient information on the key point. Its orientation is
calculated from the histogram. The invariance to rotation is obtained by this operation. In 4), the area fordescribing features is rotated in the direction of theorientation determined previously. Afterwards, thefeature vector is calculated by using gradients within
the rotated area. The gradient at a point in the featurearea is calculated and is quantized into 8 directions.
The area is separated in 16 blocks and the histogramof the gradients is calculated in each block. Finally,
the feature vector is normalized. The invariance ofa feature vector for lighting and shading comes fromthis normalization.

Image matching is a fundamental technique incomputer vision. It is used for object or scene recognition, 3D structure construction from multiple images, stereo correspondence, and motion tracking.
SIFT (Scale-Invariant Feature Transform)[1][2] is
one of the best methods of image matching. SIFT extracts distinctive invariant features from images thatcan be used to perform reliable matching between different views of an object or a scene. The features
are invariant to scaling and rotation, and are shownto enable robust matching up to a substantial range of
afﬁne distortion and change in 3D viewpoint. However, it is not fully afﬁne invariant.
I propose a new search method, which is robust
for local afﬁne transformation to ﬁnd corresponding
points, even when images are transformed locally by
afﬁne transformations .

2

OVERVIEW OF SIFT

SIFT is a method to detect and extract local feature
vectors that are reasonably invariant to changes in illumination, rotation, scaling, small changes in viewpoint, and noises in an image. We will explain thealgorithm of SIFT.

2.1

Algorithm of SIFT

The process of SIFT consists of two stages, the detection of the key point and the generation of feature
vector. Furthermore, they are divided into the following 4 steps. 1) Detection of key point candidates andtheir scale. 2) Selection and localization of the key

Figure 1: Orientation

69

The afﬁne transformation matrix between the corresponding areas can be calculated by the three pairsof corresponding points as follows.
 
 


x1 x2 x3
a b txx1 x2 x3y1 y2 y3
y1 y2 y3 .
= c d ty
1 1 1
0 0 1
1 1 1
(5)
Then, we have
  



x1 x2 x3
x1 x2 x3 −1
a b tx
c d tyy1 y2 y3
= y1 y2 y3
0 0 1
1 1 1
1 1 1
(6)
In general, the postural change of an object and thechange of 3D viewpoint can be approximated by a local afﬁne transformation.

Figure 2: Feature vector

2.2

Matching

The matching is done by comparing the feature
vectors. The pair of feature vectors of which distanceis the smallest is extracted as matched points. The
distance is evaluated by the following expressions.
d(vkI1 , vkI2 ) =

128

kI

kI

∑ (vi 1 − vi 2 )2

(1)

i=1

To reduce the error rate, only the nearest point thatsatisﬁes the condition(2)
d1 < wd2
d1 :The distance to the nearest point
d2 :The distance to the second nearest point
w :Thresholdis extracted as a matched point.

3

Figure 3: Afﬁne transformation

5 Proposed method
The afﬁne transformation causes the distortion of
the histogram in the feature vector.
The proposed method detects the afﬁne transform
matrix between the local areas in two images by using
SIFT corresponding points. The local area is transformed inversely by the afﬁne transform matrix to reduce the effect of the afﬁne transformation. Then, theorientations and feature vectors of key points in thelocal area are rebuild. By using the new feature vectors, the corresponding points are searched.

Homogeneous coordinates

The homogeneous coordinates of (x, y) is usually
written as (ξ1 , ξ2 , ξ3 ) which satisﬁes:
ξ2
ξ1
(3)
x = ,y = ,ξ3ξ3where at least one of ξ1 ，ξ2 ，ξ3 is not 0.
Two tuples of homogeneous coordinates such as(ξ1 , ξ2 , ξ3 ) and (λξ1 , λξ2 , λξ3 ) express the same point
for any non-zero scalar λ.

4

5.1 Neighborhood detection
At least three corresponding points are necessary
to calculate the afﬁne transformation matrix between
the local areas. I explain the neighborhood detection
to search the corresponding points. At ﬁrst, the algorithm detects the corresponding points in the neighborhood of the feature points which could not ﬁnd it’scorresponding point at the ﬁrst matching. The numberof detected corresponding points is be determined bya threshold to stop the detection. The result depends
on this threshold.
Figure 4 shows two images to be matched. Figure 5
shows the corresponding points between the two images by the ﬁrst matching. Figure 6 shows a result of
search of corresponding points in this neighborhood.
In Figure 5, we set the threshold 5.

Afﬁne transform

An afﬁne transformation in Euclidean space is one
that preserves the collinearity relation between points(ﬁgure 3). It consists of linear transformations (rotation, scaling or shear) and a translation (shift). An
afﬁne transformation can be represented by a matrix
using homogeneous coordinates.


1 0 tx0 1 ty
0 0 1



a
c
0

b
d
0

0
0
1




=

a
c
0

b
d
0

tx
ty
1



(4)

70

5.3 Algorithm
I make a program to evaluate the proposed method.
It’s process is as follows.
step1： Obtain two sets of the feature points by the
SIFT process.
step2： Obtain the set of corresponding points (C1)
between two images by matching process.
step3： Obtain two sets of the feature points (F1,F2)
whose points could not be matched at step2. Obtain more than 3 corresponding points in theneighborhood of the point in F2 by the neighborhood detection. Obtain the afﬁne transform matrices calculated by all combinations of corresponding points. Obtain the new set of the feature points(F3) of the local area transformed inversely by thetransform matrix.
step4： Obtain the new corresponding points (C2) bymatching from F1 and F3.
Figure.8 shows the ﬂow chart of the program forthe experiment.

Figure 4: Input image

Figure 5: Corresponding points

Figure 6: Search for corresponding points in the neighborhood

5.2

Afﬁne transformation and feature
rebuild

After the local afﬁne transformations, the orientation and feature vector were recalculated. Figure 7
shows the result of the local afﬁne transformation.
The orientation and feature vector become similar to
those of the base image.

Figure 8: Flowchart of the proposed algorithm

6 Experiment
I applied the proposed method to several types ofdifferent camera viewpoint image sets such as planar objects, 3D objects, constructions. The numberof matching points, run time and the number of incorrect matching are compared to those by the original SIFT. Several different thresholds of neighborhood detection are examined. The specs of computer

Figure 7: Afﬁne transform71

thresholdfor experiment is shown in Table 1.
CPU：
メモリ：

Genuine Intel(R) CPU T2300 @1.66GHz
2G

corresponding points

SIFT

Table 1: Experiment environment

6.1

3

Result of experiment

As an example of nice results, the result of the input
image Figure 5 is show in Figure 9 and Table 2 withthe thresholds for neighborhood detection. The pinklines show the corresponding points obtained at step
2. And the blue lines show the new corresponding
points obtained at last. The number in the bracket
in Table 2 shows the number of incorrect matching.
We can increase correct corresponding points by the
proposed algorithm.
threshold

4

5
Figure 10: Experiment result 2threshold
SIFT
3
4
5

corresponding points

w=0.6
matching：11(4) run time：1.92s
matching：11(4) run time：7.09s
matching：12(5) run time：15.87s
matching：13(6) run time：30.14s

3

Table 3: Experiment result 2

4

and the number of feature points. Some incorrect corresponding points are obtained because of the incorrect estimation of the afﬁne transformation matrix.

5

7 Conclusions
Figure 9: Experiment result 1

threshold
SIFT
3
4
5

I proposed a new search method to ﬁnd the corresponding points that are robust for the local afﬁnetransformation by using SIFT algorithm. I showed its
advantage by experiments where the number of correct corresponding points was increased.
For future tasks, we have to improve the algorithm
to reduce the incorrect matching and the run time.

w=0.6
matching：18(0) run time：0.56s
matching：21(0) run time：1.17s
matching：25(0) run time：3.29s
matching：27(0) run time：6.94s
Table 2: Experiment result 1

REFERENCES
A bad result because of the similarity of the local
areas is shown in Figure 10 and Table 3.

6.2[1] D. G. Lowe:Object recognition from local scaleinvariant features, IEEE International Conference on Computer Vision (ICCV), pp.11501157(1999)

Discussions[2] Hironibu Fujiyoshi:Extract feature vector by gradient base -SIFT and HOG- , PRMU, pp.511517(2007)

From the result, we know the run time will increasesigniﬁcantly according to the increase of threshold72Geolocation of Emergency Network Radios in a Post
Disaster Area
Student Number: 08M18230 Name: Mutsawashe GAHADZA Supervisor: Jun-ichi TAKADA

被災地での狭帯域緊急無線通信ネットワークによる地理位置情報に関する研究
ガハザ

ムツァワシェ

本研究では, 被災地において適用可能な防災無線端末の位置特定手法について検討を行った。TDOA（電波の到来時間差）及び AOA（電波の到来角）という二つの手法を用いて、シミュレーションを行った。その結果、GNU Radio-USRPというソフトウェア無線プラットフォームを利用した実装の観点から、干渉計の原理に基づいた AOA 手法の方が相応しいことが確認された。

1

Introduction

Geolocation of radio signal emitters is an old problem that has received a lot of attention from many
researchers. [1] gives detailed overviews of localization
techniques from a signal processing point of view. [2]concentrates on positioning algorithms. Indeed manymethods to solve the geolocation problem exist in literature and practical applications are plenty in diﬀerent
scenarios. The choice of an appropriate method is afunction of many factors which include implementation
cost, and the type of radio signals to be localized. This
work assumes that a natural disaster has occurred ina major city [8]. As a result many emergency network
teams converge to try and carry out rescue operations.
Unfortunately they begin to interfere with one anotherafter setting up their radio communications networks.
The task at hand is to design a wireless sensor network(WSN) that is capable of pinpointing the position of
all radio signals within the area to an appropriate degree of precision. In oder to come up with a suitable
solution, a number of assumptions based on a realistic
scenario have to be borne in mind. As in any implementation centered solution, evaluation of all possible
solutions in an analysis whose scope is the system as
a whole is imperative. This thesis focuses on two geolocaton methods, namely the time diﬀerence of arrival (TDOA) method and the angle of arrival (AOA)
method with a view to be able to select the most suitable geolocation method for a post disaster scenario.

2

The Problem Statement andObjectives

The diagram in Fig.1 shows the WSN that is supposed
to determine the geographical coordinates of each of
the emergency network radios within the scene. Before
proceeding further it is necessary to come up with theassumed conditions for the disaster scenario as wellas other factors to do with geolocation solution beingsought.
• A preliminary survey done revealed the transmission frequency for emergency radios in Japan in
the 100 MHz to 1 GHz range. Therefore thistransmission frequency range shall be adopted
for the purposes of this work.
73

Figure 1: Disaster Struck Area
• The emergency radio signals are narrowband.
• The WSN to be designed for geolocation purposes shall have no communication with the emergency network emitters.
• The GNU Radio-USRP[9] software deﬁned radio
platform is the targeted implementation platform
for the geolocation solution.
• The sensors shall be about 100m apart.

2.1

The Problem

Given the typical disaster scenario, with the GNU RadioUSRP software deﬁned radio platform, what geolocation method would be most suitable to implement inorder to get reasonable localization accuracy?

2.2

Objectives

The objectives of this work are as follows :• To evaluate the suitability of the Time Diﬀerenceof Arrival (TDOA) based geolocation method for
implementation in a typical post disaster scenario.
• To evaluate the suitability of the phase interferometry based Angle of Arrival (AOA) measurement geolocation method for implementation ina typical post disaster scenario.

3

Localization Methods

R12(X3;Y3)

R31

One way of obtaining the location of a mobile station
is what is known as mobile based localization. In thismethod the mobile station measures a given signal parameter from multiple transmitters and estimates its
own postion. A good example is the global positioning
system (GPS). A GPS receiver receives signals fromfour satellites with information about the satellite position and time at which the signal was transmitted.
Knowing the time of arrival of the received signal, thereceiver is able to calculate the time diﬀerence of arrival of the signals from the satellites. However for the
purposes of this work, a geolocation method like theGPS can not be used. This is because the radio signal
emitters to be localized are assumed to have no GPS
receivers. This work seeks to passively locate theseemitters. An opposite way of doing things is to let
sensors in a network do measure the relevant signal
parameters and then use these measurements to localize the emitter. This second method is called network
based localization. For purposes of this work, network
based localization is focused on as it is the one suitable
for the scenario under consideration.
Several localization methods do exist. The received
signal strength (RSS) method determines the distanceof the emitter from the sensor by measuring the signal
strength of the received signal. The accuracy of thismethod is severely reduced by fading ﬂuctuations in a
multipath environment and the uknown body eﬀect tothe transmitted power and the signal’s directivity. It
is also necessary to know the transmitted power andthe pathloss model in order to estimate the distancetraveled by the signal. For these reasons, and since noknowledge of the transmitted power is assumed in the
scenario under consideration, the RSS method could
not be adopted for geolocation. Another method iscalled the time of arrival (TOA) method in which thetime the signal travels from the emitter to the sensor
is used to estimate how far away it is from the sensor.
For this method it is necessary to know the emitter
timing. For this reason this method can not be employed for the disaster scenario being considered. Two
methods that can potentially be used are known as thetime diﬀerence of arrival (TDOA) and angle of arrival(AOA) methods.

4

TDOA geolocation method

One potential method that could be used is called thetime diﬀerence of arrival (TDOA) method. In thismethod, the time diﬀerence of the arrival times at eachpair of sensors is measured and the time diﬀerences aretranslated into range diﬀerences. Hyperbolic equations
that describe these range diﬀerences and whose solution localizes the emitter are the determined. Fig.2
shows the loci of the hyperbolic functions and theirintersection at the emitter. This method has the advantage over the TOA method since it does not requiretime synchronization with the emitter. However synchronization with among sensors is still necessary.

74

Sensor 3
R3(x;y)
Emitter
R1
R3

Sensor 1(X1;Y1)

Sensor 2(X2;Y2)

Figure 2: TDOA basic principles
Table 1: Geolocation Parameters TDOA Simulations
TDOA Estimation
SCD Estimation Algorithm
Positioning
Number of sensors
Sensor Coordinates
Emitter coordinates

4.1- GCC (Weighting function=1) [3, 4]-SPECCOA (Cyclostationary) [3, 4]
FAM (ﬀt Accumulation Method)[6]Chan’s Method [5]3(0;0),(-1000;0),(1500;1500) m(500;500) m

TDOA Measurement

Measurement of the tdoas at the sensors can be doneusing two diﬀerent classes of methods. One class of
methods is called the generalized cross correlation (GCC)
methods while the other is called cyclostationary methods [3, 4]. The advantage of the cyclostationary methods is that it becomes possible to seperate signals whichmight be transmitting at the same frequencies if they
have diﬀerent hidden periodicities within them. However the signal processing requirements are relatively
more demanding.

4.2

TDOA Positioning

Positioning involves approaximating the solution to thenon-linear equations obtained. Of the several methods
in the literature, Chan’s method [5] was found to be
the most suitable in the scenario under consideration.
It gives an explicit and non-iterative solution.

4.3

TDOA geolocation error simulation

Fig.3 shows the simulated error for an additive whitegausian noise channel. The adopted simulation parameters are as in Table 1. In this simultation the sychronization error was neglected for both the gcc andcyclostationary tdoa measurement. Its aim was only
to investigate the error due to tdoa estimation if we
assume a given signal to noise ratio. The geolocation
positioning error, rT DOA , is given by the root meansquare error value as in Eq.1


N
1 
rT DOA = ((x̂n − x0 )2 + (ŷn − y0 )2 ) (1)N n=1where (x̂n , ŷn ) are the estimated (x, y) coordinates
while (x0 , y0 ) are the true values of the (x, y) coordinates.

Positioning Error as a function of SNR for GCC

[m]

1000

rTDOA

2000

ε

3000

0

Table 2: Simulation Parameters for AOA
Number of sensors
Sensor positions

0

5

10
SNR[dB]

15

Emitter position
Antenna elements
per sensor
Fading Model

20

Positioning Error as a function of SNR for SPECCOA

[m]

1000

rTDOA

2000

ε

3000

0

0

5

10
SNR[dB]

15

Antenna element
Seperation
Number of signals
Angular spreads
Number snapshots
Number of trials

20

Figure 3: TDOA geolocation error

3√(-100;0),(50,-50
3)√(50;50 3)(0;0)
2
Clarke’s Model for
Reighlay fading withlimited power spectrum
λ/2
64
0, 3, 6, 9, 12
1000
100 000

To avoid ambiguity in the measured phase set L <λ
2 . This phase diﬀerence can be estimated by the crosscorrelation of the two signals at the two antenna elements as in Eq.3. The phase of the resultant crosscorrelated product is the phase diﬀerence between thetwo signals.
φ = angle

1TT{x1 (t)x∗2 (t)dt}(3)0x1 (t) and x2 (t) are the two signals while ∗ denotes
the complex conjugate of the signal.

5.2

Figure 4: Interferometry basic principles

4.4

A simple positioning algorithm that takes the mean of
the positions determined from measurements from all
relevant pairs of sensors has been applied to this work.

Synchronization of Sensors

Synchronization based on the Precision Time Protocol(PTP) IEEE1588 standard could be implemented as it
is fairly cheap to do so. Use of software generated timestamps, as could be done in the GNU Radio software,
implies a synchronization error of about 2.8μs [7]. Assuming the speed of light for the electromagnetic wave,
this would imply an error of about 840m due to synchronization alone. Use of GPS synchronization for
all the sensor nodes would also be too expensive for a
WSN. Therefore TDOA geolocation method could not
be implemented in this scenario.

5

AOA geolocaton method

In this method the angle of arrival of the signal at multiple sensors is estimated using phase interferometry.
[10] discusses how to model the propagation environment for MIMO performance evaluation.

5.1

Geolocation based on AOA measurement using Phase Interferometry

In Fig.4 the phase diﬀerence of the signals at the twoantennas is given by:
φ=

2πL sin(θ)λ

AOA Positioning(2)

where λ is the wavelength of the signal, L is the antenna separation distance and θ is the AOA to be determined.
75

5.3

Simulation of Phase interferometry
based AOA Estimation Geolocation
methodThis simulation was done in order to investigate theeﬀect of angular spread due to multipath on the accuracy of the AOA measurement, and hence the accuracy of the geolocation solution for the post disaster
scenario given the capabilities of the target implementation hardware which is the GNU Radio - USRP [9]software radio platform. The simulation parameters
are shown in table 2.
It is is important to know the eﬀect of noise andangular spread of the signal in the multipath environment. The fading model assumes 64 signals which are
subjected to Rayleigh fading with limited uniformly
distributed angular spread. It is assumed that there isno direct line of sight between the sensors and the emitter, except for zero degree angular spread. The model
assumes three sensors placed at suﬃcient height suchthat there are no scatterers around the sensors but
there are plenty of scatterers around the emitter. For
1000 snapshots, 100 000 trials were repeatedly done
averaged.
Fig.5 shows the geometrical model employed. Three
USRP[9] sensors were placed equidistant from eachother, at the circumference of a circle of radius 100m,with the emitter placed at the center of the circle.
Each of the sensors has two omnidirectional antenna
elements whose endﬁre direction is tangential to thecircle at a given point. The AOA at a given sensor is

12

°

AS=0
10

AS=3°

8

AS=9

AS=6°
°

ε θ [degrees]

AS=12°

6
4
2

0
−10

0

10
Average SNR [dB]20

30

Figure 6: AOA error
Figure 5: The assumed geometrical conﬁguration of athree sensor network

10

°

AS=0

AS=3°

8

°

AS=6

°

AS=9
ε r [m]taken as the angle between the direction of arrival of
the signal and the broad side direction of the sensor.
This means the AOA at each sensor must ideally be
zero.

°

6

AS=12

4

2

5.4

Results

0−10

Fig.6 and Fig.7 show the variation of the observed
angle estimation error and the corresponding position
estimation error as functions of the average signal tonoise ratio and SNR. At high signal to noise ratio theerror is dominated by the eﬀect of angular spread. The
AOA error is the standard deviation of the error, given
by


N
1 
θ = 
(θ̂n − θ0 )2
(4)
N n=1
where θ̂n is the estimated AOA and θ0 is the true valueof the AOA. N is the number trials used. Similarly the
positioning error, r , is given by the root mean squareerror value as in Eq.5


N
1 
r = 
((x̂n − x0 )2 + (ŷn − y0 )2 )
(5)
N n=1
where the symbols have the same meaning as insection 4.3. For the 100m radius considered the error caused by fading for the angular spreads simulated
is less that 10m at relatively high SNR values. The
high SNR values may be safely assumed for emergency
radios since they have to communicate over relatively
long distances.

6

Conclusions

An evaluation of the suitability of several geolocation
methods in a post disaster area was conducted. Focus
was put on TDOA and AOA with a view to implement
them on the GNU Radio - USRP software deﬁned radio platform. It was found out that TDOA could not
be cost eﬀectively be employed. Phase interferometry
based AOA geolocation was investigated. The eﬀect ofangular spread and SNR on the error performance of
the AOA measurement and the resultant geolocation
error was investigated through simulation.
76

0

10
Average SNR [dB]20

30

Figure 7: AOA geolocation based position error

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