ISSN 1880-8468
Technical Report ofInternational Development Engineering࿖㓙㐿⊒Ꮏቇႎ๔TRIDE-2012- 03

February 29, 2012

Abstracts of Bachelor Theses
Presented in February 2012

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

Preface

Bachelor theses of Department of International Development Engineering, Tokyo Institute ofTechnology were presented successfully on July 29, 2011, February 21, and 29, 2012. This
technical report consists of the abstracts of those theses.

iTechnical Report of International Development Engineering
TRIDE-2012-03
Table of Contents(Completing in September 2011)
Multilink RSSI Measurement for Wireless BAN using ZigBee Devices....................................................................................................... Takashi YAMAZAKI

1

Recognition of mumerical value of electric emergy meter on Android devices................................................................................................................ Wansheng YAN

3

未利用海藻資源からのバイオエタノール生成...........................................................................................................ヒョウ・ウンテキ

5(Completing in March 2012)
Experimental study on grip-release mechanism of slanted multi-beam
structure with Ti-Ni super elastic wire............................................................................................................ Kazuhito EMURA

7

Selective Catalytic Reduction of Nitrogen Oxides with Propene over
(Ti,Zr)2O4 catalyst supported by Mo...................................................................................................................... DongIl KIM

9

A study the decompositon of nitrous oxide over TiO2-CeO2 catalysts............................................................................................................. Yutaro FUKUCHI

11

Employment data analysis and Relationships of employment support in Tokyo
Institute of Technology...................................................................................................... Akihito TAKANASHI

13

A Study on the effectiveness of Ecodriving Program on Fuel Economy.............................................................................................................. Ayuka TAKEDA

15

Effects of Temperature and Solution Type on Corrosion Behavior at
Acceleration Test of Paint-coated Steel Plate with Defect.................................................................................................................... Kenji WADA

17

A study on the operation and maintenance performance of wind power plants
owned.......................................................................................................... Teppei KATATANI

19

Acid-catalyzed biodiesel production from triolein......................................................................................................................... Xiao HAN

ii

21

Crushing sound analysis of snail fossils under compressive loading using
audible frequency measurement.......................................................................................................... Shuhei KITAKATA

23

Fast Visual Object Categorization by Clustering with Hamming Distance...................................................................................................................... Joowon Kim

25

A Study on Renewable Energy Funds Initiated by NPOs in Japan........................................................................................................Keisuke KUSUNOKI

27

A study on the perceptions of drinking water quality and risk in Shenyang,
China............................................................................................................................ Lu GAO

29Development of time domain channel sounding system of BAN and evaluationof SIMO diversity............................................................................................................ Sho KOBAYASHI

31

TiO2 光触媒による水中のテトラクロロエチレン(PCE)の光分解................................................................................................................................ 左 皓

33

タケからのバイオエタノール生成における微細化とアルカリ処理の効果............................................................................................................................ 徐 芸萌

35Investigation of carbonation depth of real structures in The Philippines andLaos.............................................................................................................. Satoshi TAKAKI37

東アジア-北米航路のコンテナ荷動き量予測モデル...................................................................................................................... タニ・ウン

39

Safety space for car and motorcycle in the mixed traffic flow in Vietnam................................................................................................................Chu Van HUNG

41

Enhancement of the composting of sub-critical water-treated food waste using
acetic acid-degrading yeast.............................................................................................................. Songhua ZHENG

43

Consideration of stress distribution in an antlion's nest using advantage ofangle of repose......................................................................................................................... Lin TANG

45

Magnetic Shielding from Solar Energetic Particles for Manned Deep Space
Explorations...................................................................................................... Yasuhito NAKATANI

iii

47

Effect of operating condition on solvent extraction of La with PC-88A..................................................................................................... Kazuma NAKAMURA

49

都市境界層における大気乱流及びフラックスの年間スペクトル解析........................................................................................................................ 野口 淡海

51

チャネルサウンディングへのＩ／Ｑスキューの影響...................................................................................................ファム・ヴァン・フェ

53

Influence of circumferential strain of disk surface On pin-on-disk adhesion
contact.............................................................................................................. Dooyoung BAEK

55

Optimum Duty Ratio of Boost Switching Regulator for Charging Tiny Electric
Energy to Capacitor.............................................................................................................. Yoshito HONDA

57

Wideband IQ Imbalance Compensation Technique using Spectrum Analyzer..............................................................................................................Yuta MARUICHI

59

Synthesis of zeolites from lake sludge for heavy metal removal....................................................................................................................... Xuebin LIU

61

Temperature Distribution Change by Point Contact Current................................................................................................ Kazuki WAKABAYASHI

63

実都市幾何形状を考慮した都市境界層のLarge Eddy Simulation............................................................................................................................ 渡辺 修

iv

65

Multilink RSSI Measurement for Wireless BAN
using ZigBee Devices
Student Number: 05B25458

1

Name: Takashi Yamazaki

BackgroundNowadays, body area networks (BAN), which are shortrange networks on or near human body, is catching great
attention. To date, BAN technologies have been applied
for speciﬁc objectives such as military, medical purposes,
but it is said that BAN will become more popular technology in various applications. In particular, it is expected that BAN is utilized in medical or healthcare
purposes. BAN is expected to be applicable for Medical Information and Communication Technology (MICT)
using networks comprised of implanted devices or sensor
devices on body surfaces.
We have to take into account the eﬀects of radiation
to a human body during radio communication. Devices
must communicate under restrictions such as SAR (speciﬁc absorption rate) and EMC (electro magnetic compatibility) regarding radio power. Thus, it is diﬃcult tomake communication reliable in BAN. Developing reliable communication systems with low radiation power isgreat challenge.
In previous studies, variation of radio propagation
channels have been measured and BAN channel models
have been developed. However, measurement scenarios
were limited because the conventional measurement system was connected to the antennas with cables. The
lengths of coaxial cables limited activity range, and insome cases, tensions of cables causes the positions of antennas to move. Also, the number of ports of instrument
equipments is limited, so it was diﬃcult to measure theproperties with multiple sensor nodes.

2

Supervisor: Jun-ichi Takada

Figure 1: Crossbow IRIS Mote

3

Multilink RSSI measurement
system

In this research the measurement system is composed of
10 ZigBee devices, one transmitter and nine receivers.
Transmitter sends a message packet which contains thepacket ID with a 12 second interval. When receiver devices detect a message packet, they record the receive
signal strength indicator (RSSI) value and the packet
number.

4

Experiments and Results

In the experiments, the channel responses were taken
in two scenarios, “walking” and “running”. One transmitter node and nine receiver nodes were ﬁxed on bodysurface (Fig.2) and the transmitted signal was recorded
periodically at every receiver node simultaneously during
walking or running in the hallway.
Figure.3 shows the measurement result of path gain
variations in walking scenario at wrist, shoulder andwaist. The proposed measurement system is applicablein the wider range of scenarios than the cable-connected
system. These results are useful in the developmentof BAN channel models and evaluation of cooperativetransmission techniques.

Objectives

Cooperative transmission systems have been proposed asreliable transmission systems with samll radiation power[1]. To construct this system, we should be carefully investigated the characteristics of BAN multilink channels.
This research aims at constructing a wireless multilink
system, to measure the variation of BAN channels using
ZigBee devices (Fig.1). Using this system variation of
BAN multilink channels due to the body motion can be
5 Conclusions
investigated
ZigBee is a short range radio communication protocol In this research, a channel measurement system using
and it has signiﬁcant features of low power consumption ZigBee devices has been proposed to realize the cable-freeand ﬂexible network topology.
dynamic BAN multilink channel measurement. Some1

Table 1: Correlation coeﬃcient in walking scenario
Node

Wrist

Upper arm

Shoulder

Chest

Waist

Thin

Ankle

Ear

Back

Wrist
Arm
Shoulder
Chest
Waist
Thin
Ankle
Ear
Back

1
0.31
0.054
-0.058
0.054
0.162
0.008
0.064
0.08

1
0.172
-0.0320.07
0.244-0.062
0.118
0.102

1
0.152
0.124
0.062
0.014
0.116
0.172

1
0.162
-0.042-0.064-0.044
0.114

1
0.184
-0.072
0.128
0.026

1
0.044
0.106
0.028

1
0.072
-0.016

1
0.054

1

−20
Wrist
Shoulder
Waist

−30

Path gain [dB]

−40
−50
−60
−70
−80
−90
0

2

4

6

8

10

Time [sec]Figure 2: Positions of sensor devices

Figure 3: Measurement result in walking scenario

tennas and Propagation (EUCAP), Proceedings of
the 5th European Conference on, pp.2987–2991, 2011

BAN multilink measurements are conducted, and thecorrelation properties of the channels are obtained.

[5] S.L. Cotton, G.A. Conway, W.G. Scanlon: “A TimeDomain Approach to the Analysis and Modelingof On-Body Propagation Characteristics Using Syn[1] Xigang Huang, Hangguan Shan, Xuemin Shen: “Onchronized Measurements at 2.45 GHz,” Antennas andenergy eﬃciency of cooperative communications in
Propagation, IEEE Transactions on, Vol,57, Issue:4,
wireless body area network,” Wireless Communicapp.943–955, 2009
tions and Networking Conference (WCNC), IEEE,
pp.1097–1101, 2011

References[2] Minseok Kim, J. Takada: “Statistical Model for
4.5-GHz Narrowband On-Body Propagation Channel
With Speciﬁc Actions,” Antennas and Wireless Propagation Letters, IEEE, vol.8, pp.1250–1254, 2009[3] L. Hanlen, V. Chaganti, B. Gilbert1, D. Rodda,
T. Lamahewa, D. Smith: “Open-source Testbed for
Body Area Networks: 200 sample/sec, 12 hrs Continuous Measurement,” Personal, Indoor and Mobile
Radio Communications Workshops (PIMRC Workshops), 2010 IEEE 21st International Symposium on[4] D.B. Smith, L.W. Hanlen, T.A. Lamahewa: “A newlook at the body area network channel model,” An2

Recognition of mumerical value of electric emergy meter on Android devices
Student Number：０７−０４４６８

1

Yan Wansheng

Introduction

Supervisor：Yukihiko Yamasita

devices consists of image acquisition, preprocessing,and classiﬁcation.

Human beings have been using a huge amount ofenergy such as oil and natural gas. Fossil fuel took 2.1 Image acquisition and preprocessing
about 200 million years to be accumulated by the enProcess of image acquisition and preprocessing is as
ergy of the sun. It will be spent in next 100 years or
200 years. Especially oil is said to be depleted in 40 follows.
years. Natural gas is said to be depleted in 60 years.
Furthermore, greenhouse gas emission from fossil fuel1. Take a picture of the electric energy meter. (Figure
use causes global warming. If greenhouse gas still con1)tinues to increase, averaged global temperature will rise◦from 2.4 to 6.4 C in 100 years, and sea level will risefrom 9 to 88cm. It casues various problems. Therefore,
we have to reduce the use of fossil fuels. Natural energy
such as solar power (renewable energy) is important tosolve the proplem.
To make solar power a stable source of energy ina region, not only to install solar panels but also tomaintain a stable power over time is important. Then
it is necessary to determine whether the generator has
Figure 1: Photos of energy meters
been normal. Kakegawa where people have installed solar panels, is developing a self-diagnosis photovoltanic
2. Cut out the numeric rengion of the image. (Figure
power generation system, and aims to create a solar2) .
network in the region. This self-diagnostic system measures the amount of solar radiation on equipment installed at City Hall and other representative points,
and the amount of electricity generated by photovoltaic
systems is estimated from the data. We can see it onweb, and know the amount of power generated in the
Figure 2: Part of the number
past period.
If we want to use this system, we must start a PC,
3. Obtain the threshold for binarization by Otsu’srecord the number of a electric energy meter installed
method .
outside the house, and input the number into the PC.
It is time consuming. If we can input the number more
4. Binarize the image by it. (Figure 3)
easily, the number of users of the system will increase,and the system will be more eﬀective. This is the reason
why I develop a system to input the value easily.

2

Recognition of number of electricenergy meter on Android devices

Figure 3: Binarized image
5. Label the objects in the image, and extract number
objects.

Android devices are suitable for the purpose since they
are very populer now. Smartphone shipments have already exceeded PC shipments.
The process of energy meter recognition by Android

6. Normalize an extracted number object to 16 × 16pixels image. (Figure 4) .
1
3

Figure 4: Extracted number

2.2Classﬁﬁcation

k-nearest neighbor classiﬁer is used to discriminate
the number. I explain this method. First we retrieve
the k training samples in the proximity of the input
pattern x from training sample set. That is, calculate Figure 5: Accurate
the distance between the input pattern x and train- identiﬁcation
ing samples, retrieving the k nearest training samples.
Count the number of training samples belonging to eachcategory. Classify the input pattern x to the category
which have the most training samples among the extracted k training samples. The input pattern x is a
256-dimensional vector.
x=

x1 x2 ........ x256.......................

Figure 6: Incorrect
identiﬁcation １(1)

Let xij be the j-th training sample of the i-th class.
The distance Dij between xij and x is given by


Dij =

(xij1 − x1 )2 + .... + (xij256−x256 )2 ....

Figure 7: Incorrect identiﬁcation ２(2)

The number of the extracted k nearest training sam- can recognized the number correctly. For future work,
ples belonging to category i is denoded by Ki . x is we have to derelop a system for noisy image.
classiﬁed into the category with the largest Ki .

References
3

Experiment4

Conclusion[1] Eiichi Furukawa, ”Android 2.1 programming
bible, ”Sosimu, 2010
I took pictures of energy meters by an Android device,performed pretreatment, and classiﬁed to 10 classes[2] Keiji Shotasu, ” Programming of Image Processing,from 0 to 9 by hand. They make up the training sam”SoftBank Creative, 2008
pleset. Training data consist of 200 16 × 16-pixels images. 23 photos are taken by Android decice and rec- [3] Richard O.Duda, Peter E.Hart, David G.Stork,
ognized for test. 19 photos were correctly recognized.
”Pattern identiﬁcation, ”Communications Tech,
Then accurate recognition rate is 82.6%.
2003
Figures 5 and 6 are the results of the same power meter, but photographs are taken from diﬀerent angles. [4] Jyunichirou Toriwaki, ”Recognition Engineering,”Corona Co, Ltd, 1993
Figure 6 is a result of misclassiﬁcation. To resolve thisproblem, we have to increase training samples, or the[5] Masashi Sugiyama, ”Statistical Machine Learning:
angle have to be set carefully when taking a photo. FigPattern Recognition Based on Enerative Model,
ure 8 is a result of misclassiﬁcation because of reﬂection”Ohmsha, Ltd, 2009of light by the glass on the energy meter.

I proposed a system to recognize the number of electricenergy meter. I conducted experiments to show it’s
perfomance. If the photo is taken clearly, the system24ᧂ೑↪ᶏ⮺⾗Ḯ߆ࠄߩࡃࠗࠝࠛ࠲ࡁ࡯࡞↢ᚑቇ☋⇟ภ㧦07-22101 ᳁ฬ㧦ࡅ࡚࠙ ࠙ࡦ࠹ࠠ ᜰዉᢎຬ㧦ਛፒ ᷡᒾߎߥߞߚ‫♧ޔ߅ߥޕ‬ൻᦼ㑆ਛߪဋ৻ߥ෻ᔕㅴⴕߩߚ
߼ߦ 24 ᤨ㑆Ფߦ࡝ࠕࠢ࠲ౝࠍࠬࡄ࡯࠹࡞ߦࠃࠅᷙ
ว࡮ᡬᜈߒߚ‫ޔߚ߹ޕ‬1 ࿁⋡‫ ߮ࠃ߅ޔ‬2 ࿁⋡ߩ♧ൻߢ↢ᚑߒߚࠣ࡞ࠦ࡯ࠬỚᐲࠍ HPLC ߦࠃࠅ᷹ቯߒߚ‫ޕ‬

1 ⋡⊛Concentration of glucose (g/L)

ㄭᐕ‫ޔ‬⍹ᴤઍᦧΆᢱߣߒߡࡃࠗࠝࠛ࠲ࡁ࡯࡞߇ᵈ⋡ߐࠇߡ߅ࠅ‫ޔ‬㘩♳ߣ┹วߔࠆߎߣߩߥ޿ࡃࠗࠝࡑ
ࠬ߆ࠄࠛ࠲ࡁ࡯࡞ࠍല₸ࠃߊ↢↥ߔࠆᣇᴺߩ㐿⊒߇ᦼᓙࠍ㓸߼ߡ޿ࠆ‫ࠬ࡯ࡠ࡞࠮ࡁࠣ࡝ޔ߼ߚߩߘޕ‬ᕈ 2.4 ㅪ⛯♧ൻߒߚᶏ⮺ߩ⊒㉂⾗Ḯ߆ࠄߩࠛ࠲ࡁ࡯࡞↢ᚑ߇ᄙߊ⹜ߺࠄࠇߡ޿ࠆ߇‫ ޔ‬ㅪ⛯♧ൻߒߚᶏ⮺ߦ㉂Უࠍ૞↪ߐߖ‫࡞࡯ࡁ࠲ࠛޔ‬ㅢᏱߩ࡝ࠣࡁ࠮࡞ࡠ࡯ࠬᕈ⾗Ḯߪ‫↢߇ ࡋ߿ࠬ࡯ࡠ࡞࠮ޔ‬ᚑน⢻߆⏕߆߼ߚ‫ޕ‬ㅪ⛯♧ൻᓟߩᶏ⮺ߦ yeastࡒ࠮࡞ࡠ࡯ࠬߩ♧ൻࠍ㒖ኂߔࠆ࡝ࠣ࠾ࡦࠍ㒰෰ߔࠆ extract ࠍ 3 g/L‫ޔ‬Polypepton ࠍ 5 g/L ߣߥࠆࠃ߁ߦᷝ
4178 ࠍ
ߚ߼ߩ೨ಣℂ߇ᔅⷐߢ޽ࠅ‫ ࠻ࠬࠦ↥↢࡞࡯ࡁ࠲ࠛޔ‬ട8 ߒ ‫ ޔ‬Saccharomyces cerevisiae IAMo߿↢↥ࠛࡀ࡞ࠡ࡯ࠍ਄᣹ߐߖࠆߎߣ߇໧㗴ߣߥߞߡ 10 CFU/mL ߣߥࠆࠃ߁ߦធ⒳ߒߡ 30 C ߢၭ㙃ߒߚ‫ޕ‬޿ࠆ‫⎇ᧄޕ‬ⓥߢߪ‫ࡦ࠾ࠣ࡝ޔ‬฽᦭㊂߇ૐ޿ࡃࠗࠝࡑ ߹ߚ‫ޔ‬ၭ㙃ߦߣ߽ߥߞߡᄌൻߔࠆࠣ࡞ࠦ࡯ࠬ‫࠲ࠛޔ‬ࠬߣߒߡᶏ⮺ߦ⌕⋡ߒ‫ޔ‬ᶏ⮺߆ࠄߩࠛ࠲ࡁ࡯࡞↢ᚑ ࡁ࡯࡞‫߮ࠃ߅ޔ‬೽↢ᚑ‛ߢ޽ࠆࠣ࡝࠮ࡠ࡯࡞Ớᐲࠍߦߟ޿ߡᬌ⸛ߒߚ‫ޔߚ߹ޕ‬ᶏ⮺ߪ৻ᣤ♧ൻߒߡࠛ࠲ HPLC ߦࠃࠅ᷹ቯߒߚ‫ޕ‬ࡁ࡯࡞ߦ⊒㉂ߔࠆߩߢ‫♧ޔ‬ൻߢ↢ᚑߔࠆ♧ࠍ㜞Ớᐲ 3 ⚿ᨐߣ⠨ኤൻߔࠆߎߣߦࠃࠅ‫ޔ‬ᒁ߈⛯ߊ⊒㉂ߢ↢ᚑߔࠆࠛ࠲ࡁ
3.1 ࡔࠗ࠮࡜࡯࠯ߩࠣ࡞ࠞࡦಽ⸃ᵴᕈ
࡯࡞ߩ㜞Ớᐲൻࠍ⹜ߺߚ‫ޕ‬ࡔࠗ࠮࡜࡯࠯ߦࠃࠆ࠮࡞ࡠ࡯ࠬࡄ࠙࠳࡯‫߲ࠎߢޔ‬2 ታ㛎ᣇᴺࠎ‫ޔ‬ȕ-1,3-ࠣ࡞ࠞࡦߩ♧ൻߦ߅ߌࠆࠣ࡞ࠦ࡯ࠬỚᐲߩ⚻ᤨᄌൻࠍ Fig. 1 ߦ␜ߔ‫޿↪ࠍࡦࠞ࡞ࠣߩࠇߕ޿ޕ‬2.1 ૶↪ߒߚᶏ⮺‫ޔ‬㉂⚛‫߮ࠃ߅ޔ‬㉂Უ
ߚ႐ว߽ࠣ࡞ࠦ࡯ࠬ߇↢ᚑߒ‫ޔ‬120 ᤨ㑆ᓟߦߪ࠮࡞ ᶏ⮺ߣߒߡ‫ߦ✁⮺ⶊ߮ࠃ߅ޔ✁⮺✛ޔ‬ዻߔࠆࠕ࠽
ࡠ࡯ࠬࡄ࠙࠳࡯‫ޔࠎ߲ࠎߢޔ‬ȕ-1,3-ࠣ࡞ࠞࡦ߆ࠄߘࠇ
ࠕࠝࠨ‫ޔߪߦࠨࠝࠕ࠽ࠕޕߚ޿↪ࠍ࠰ࠗࠟ࠴߮ࠃ߅ޔ‬ߙࠇ 14.2‫ޔ‬11.1‫ޔ‬8.93 g/L ߩࠣ࡞ࠦ࡯ࠬ߇↢ᚑߒߚ‫ޕ‬ࠣ࡞ࠦ࡯ࠬࠍ᭴ᚑ♧ߣߔࠆᄙ♧㘃ߢ޽ࠆࠣ࡞ࠞࡦ
ߎߩ⚿ᨐ߆ࠄ‫ࠍࡦࠞ࡞ࠣߩࠄࠇߎ߇࠯࡯࡜࠮ࠗࡔޔ‬ߣߒߡ‫߇ࠎ߲ࠎߢߣࠬ࡯ࡠ࡞࠮ޔ‬วࠊߖߡ 22.0%฽
ട᳓ಽ⸃น⢻ߢ޽ࠆߎߣ߇⏕߆߼ࠄࠇߚ‫ޕ‬߹ࠇߡ߅ࠅ‫࡯ࡠ࡞࠮ߡߒߣࡦࠞ࡞ࠣߪߦ࠰ࠗࠟ࠴ޔ‬ࠬߣ ȕ-1,3-ࠣ࡞ࠞࡦ߇วࠊߖߡ 24.5%฽߹ࠇߡ޿ࠆ
15
Celluloseߎߣࠍ⏕߆߼ߡ޿ࠆ‫ޕ‬
Starch ♧ൻ㉂⚛ߣߒߡߪ‫ޔ‬Trichoderma viride ↱᧪ߩE-1,3-Glucan
Meicelase㧔CEPB-5394, Meiji Seika Kaisya Ltd., Japan㧕
10ࠍ૶↪ߒ‫ޔ‬㉂Უߣߒߡߪ‫ޔ‬Saccharomyces cerevisiae
IAM 4178 ࠍ૶↪ߒߚ‫ޕ‬

2.2 ࡔࠗ࠮࡜࡯࠯ߩࠣ࡞ࠞࡦಽ⸃ᵴᕈ
ࠕ࠽ࠕࠝࠨߣ࠴ࠟࠗ࠰ߦ฽߹ࠇࠆࠣ࡞ࠞࡦߢ޽
ࠆ࠮࡞ࡠ࡯ࠬ‫ ߮ࠃ߅ޔࠎ߲ࠎߢޔ‬ȕ-1,3-ࠣ࡞ࠞࡦࠍ
ࡔࠗ࠮࡜࡯࠯߇ಽ⸃น⢻߆⏕߆߼ࠆߚ߼ߦ‫ߩ⮎⹜ޔ‬࠮࡞ࡠ࡯ࠬࡄ࠙࠳࡯㧔Avicel㧕‫ࠎ߲ࠎߢޔ‬㧔Starch,
Soluble㧕‫ޔ‬ȕ-1,3-ࠣ࡞ࠞࡦ㧔Curdlan㧕ߩ♧ൻࠍ⹜ߺߚ‫ޕ‬
0.1M ࠢࠛࡦ㉄✭ⴣᶧߦࡔࠗ࠮࡜࡯࠯ࠍ 1 g/L ߣߥࠆࠃ߁ߦṁ⸃ߒ‫ ࠍࡦࠞ࡞ࠣߩ⮎⹜ߩࠇߙࠇߘޔ‬20g/Lߣߥࠆࠃ߁ߦᷝടߒߡ෻ᔕߐߖߚ‫ޕ‬pH ࠍ 5.5‫ޔ‬᷷ᐲࠍ 50oC‫ޔ‬100 rpm ߩ᧦ઙߢ 120 ᤨ㑆♧ൻߒߚ‫ޔߚ߹ޕ‬♧ൻߢ↢ᚑߒߚࠣ࡞ࠦ࡯ࠬỚᐲࠍ HPLC ߦࠃࠅ᷹ቯ
ߒߚ‫ޕ‬

5

0
0

24

48

72

96

120

Time (h)Fig. 1 The courses of glucose concentration during
the enzymatic hydrolysis of various types of
glucans. Error bars for the concentrations of
glucose indicate a 95% confidence interval for the
averaged values (n = 3).

3.2 ᶏ⮺ߩㅪ⛯♧ൻ
2.3 ᶏ⮺ߩㅪ⛯♧ൻࠕ࠽ࠕࠝࠨߣ࠴ࠟࠗ࠰ߩㅪ⛯♧ൻߦ߅ߌࠆ♧ൻ 1ᶏ⮺ߦࡔࠗ࠮࡜࡯࠯ࠍ૞↪ߐߖߡ♧ൻߒ‫ޔ‬ᶏ⮺ਛ ࿁⋡‫ ߮ࠃ߅ޔ‬2 ࿁⋡ߩ 120 ᤨ㑆ᓟߩࠣ࡞ࠦ࡯ࠬỚᐲࠣ࡞ࠞࡦ߆ࠄࠣ࡞ࠦ࡯ࠬ߇↢ᚑน⢻߆⏕߆߼ߚ‫ޕ‬pH ࠍ Fig. 2 ߦ␜ߔ‫ޕ‬1 ࿁⋡ߩ♧ൻ 120 ᤨ㑆ᓟߦߪࠕ࠽
ࠍ 5.5 ߦ⺞ᢛߒߚ 0.1M ࠢࠛࡦ㉄✭ⴣᶧߦࡔࠗ࠮࡜࡯ ࠕࠝࠨ߆ࠄ 43.1 g/L‫ޔ‬࠴ࠟࠗ࠰߆ࠄ 67.7 g/L ߩࠣ࡞ࠦ࠯ࠍ 5 g/L‫ޔ‬ᶏ⮺☳ᧃࠍ 300 g/L ߣߥࠆࠃ߁ߦᷝടߒ‫↢߇ࠬ࡯ ޔ‬ᚑߒߚߎߣ߆ࠄ‫ࠝࠕ࠽ࠕ߇࠯࡯࡜࠮ࠗࡔޔ‬50oC ߢ 120 ᤨ㑆♧ൻߒߚ‫ޕ‬ࠨ߿࠴ࠟࠗ࠰ਛߩࠣ࡞ࠞࡦࠍಽ⸃น⢻ߢ޽ࠆߎߣ߇
ᒁ߈⛯޿ߡ‫♧ޔ‬ൻᶧࠍౣ߮ᰴߩ♧ൻߦ↪޿ࠆㅪ⛯ ⏕߆߼ࠄࠇߚ‫࠰ࠗࠟ࠴߮ࠃ߅ޔࠨࠝࠕ࠽ࠕޔ߅ߥޕ‬♧ൻߦࠃࠅ‫↢ޔ‬ᚑߔࠆࠣ࡞ࠦ࡯ࠬߩ㜞Ớᐲൻࠍ⹜ߺ ਛࠣ࡞ࠞࡦߩࠣ࡞ࠦ࡯ࠬ߳ߩᄌൻ₸ߪ‫ࠇߙࠇߘޔ‬ߚ‫♧ޕ‬ൻᓟߩᶏ⮺ࠍ㆙ᔃಽ㔌ߦࠃࠅ࿕ᶧಽ㔌ߒ‫ޔ‬਄ 58.8%‫ ߮ࠃ߅ޔ‬82.9%ߣߥߞߚ‫ޕ‬⣕࡝ࠣ࠾ࡦಣℂࠍᣉẴߺᶧࠍ♧ൻᶧߣߒߡ࿁෼ߒߚ‫ޔߦࠄߐޕ‬࿁෼ߒߚ ߒߡ޿ߥ޿ᧁ᧚☳ᧃߩ♧ൻߦ߅޿ߡߪ‫ࠬ࡯ࡠ࡞࠮ޔ‬♧ൻᶧߦࡔࠗ࠮࡜࡯࠯ࠍ 5 g/L‫ޔ‬ᣂߚߦ↪ᗧߒߚᶏ⮺ ߩᄌൻ₸߇ 13%ߣߥߞߡ޿ࠆߎߣ߆ࠄ[1]‫ޔ‬ᶏ⮺ਛߩ☳ᧃࠍ 300 g/L ߣߥࠆࠃ߁ߦᷝടߒߡౣᐲ♧ൻࠍ߅
5

140

140

120

120

100

100

80

80

60

60

40

40

20

20

0

Concs. of glucose, ethanol& glycerol (g/L)

100

60
40
20

0

6

12
Time (h)1824Fig. 3 The courses of the concentrations of glucose,ethanol, and glycerol during the fermentation ofsea lettuce hydrolysate prepared by successive
saccharification. Error bars for the concentrationsof glucose, ethanol, and glycerol indicate a 95%
confidence interval for the averaged values (n = 3).
140

0Primary Secondary
Sea lettuce

GlucoseEthanol
Glycerol800Primary Secondary
ChigaisoConcs. of glucose, ethanol& glycerol (g/L)

Concentration of glucose (g/L)ࠣ࡞ࠞࡦ߇㉂⚛ߦࠃࠅಽ⸃ߐࠇ߿ߔ޿ߎߣ߇ࠊ߆ߞ
ߚ‫ޔߪࠇߎޕ‬ᶏ⮺ਛߩ࡝ࠣ࠾ࡦ฽᦭㊂߇ૐ޿ߚ߼ߢ
޽ࠆߣ⠨߃ࠄࠇߚ‫ޕ‬
2 ࿁⋡ߩ♧ൻ 120 ᤨ㑆ᓟߦߪࠕ࠽ࠕࠝࠨ߆ࠄ
78.8 g/L‫ ࠄ߆࠰ࠗࠟ࠴ޔ‬123 g/L ߩࠣ࡞ࠦ࡯ࠬ߇↢ᚑߒ‫ ߽ࠄߜߤޔ‬1 ࿁ߩ♧ൻߢ↢ᚑߒߚࠣ࡞ࠦ࡯ࠬỚᐲ
ߩ 1.8 ୚ߣߥߞߚ‫ޔࠄ߆ߣߎߩߎޕ‬ㅪ⛯♧ൻߦࠃࠅࠣ࡞ࠦ࡯ࠬߩ㜞Ớᐲൻ߇น⢻ߢ޽ࠆߎߣ߇⏕߆߼ࠄࠇߚ‫ޕ‬Fig. 2 Comparison of glucose concentrations after
the successive saccharifications of sea lettuce andchigaiso. Error bars for the concentrations of
glucose indicate a 95% confidence interval for the
averaged values (n = 3).

120
100

GlucoseEthanol
Glycerol

80
60
40
20

3.3 ㅪ⛯♧ൻߒߚᶏ⮺ߩ⊒㉂
0ㅪ⛯♧ൻߒߚࠕ࠽ࠕࠝࠨ‫⊒ߩ࠰ࠗࠟ࠴߮ࠃ߅ޔ‬㉂
0
24
48
72
Time (h)ߦ߅ߌࠆࠣ࡞ࠦ࡯ࠬ‫࡞࡯ࡠ࠮࡝ࠣޔ࡞࡯ࡁ࠲ࠛޔ‬Ớᐲߩ⚻ᤨᄌൻࠍߘࠇߙࠇ Fig. 3‫ ߮ࠃ߅ޔ‬Fig. 4 ߦ␜ߔ‫ ޕ‬Fig. 4 The courses of the concentrations of glucose,ethanol, and glycerol during the fermentation ofࠕ࠽ࠕࠝࠨߢߪ‫⊒ޔ‬㉂ 24 ᤨ㑆ᓟߩࠛ࠲ࡁ࡯࡞‫ࠃ߅ޔ‬chigaiso hydrolysate prepared by successive߮ࠣ࡝࠮ࡠ࡯࡞Ớᐲ߇ߘࠇߙࠇ 30.0‫ޔ‬߅ࠃ߮ 6.91 g/L
saccharification. Error bars for the concentrationsߣߥࠅ‫࡞࡯ࡁ࠲ࠛߩࠄ߆ࠬ࡯ࠦ࡞ࠣޔ‬෼₸ߪℂ⺰෼
of glucose, ethanol, and glycerol indicate a 95%₸ߩ 73.9%ߣߥߞߚ‫⊒ޔߪߢ࠰ࠗࠟ࠴ޔߚ߹ޕ‬㉂ 72
confidence interval for the averaged values (n = 3).
ᤨ㑆ᓟߦ 12.4 g/L ߩࠣ࡞ࠦ࡯ࠬ߇ᱷሽߒ‫࡯ࡁ࠲ࠛޔ‬࡞‫࡞࡯ࡠ࠮࡝ࠣ߮ࠃ߅ޔ‬Ớᐲߪߘࠇߙࠇ 34.4‫ࠃ߅ޔ‬
߮ 17.2 g/L ߣߥߞߚߎߣ߆ࠄ‫࡞࡯ࡁ࠲ࠛޔ‬෼₸ߪ 4 ⚿⺰
59.8%ߣߥߞߚ‫ޔ߇޿ߥߐ␜ࠍ⚦⹦ߪߦߎߎޕ‬1 ࿁♧  ᧄ⎇ⓥߢߪ‫ࡦ࠾ࠣ࡝ޔ‬฽᦭㊂߇ૐ޿ᶏ⮺߆ࠄߩࡃൻߒߚࠕ࠽ࠕࠝࠨ‫⊒ߩ࠰ࠗࠟ࠴߮ࠃ߅ޔ‬㉂ߢߪ‫↢࡞࡯ࡁ࠲ࠛࠝࠗ ࠛޔ‬ᚑߣㅪ⛯♧ൻߦࠃࠆࠛ࠲ࡁ࡯࡞ߩ
࠲ࡁ࡯࡞෼₸ߪߘࠇߙࠇ 86.6‫ ߮ࠃ߅ޔ‬75.9%ߢ޽ߞ 㜞Ớᐲൻࠍ⹜ߺߚ‫ޔߕ߹ޕ‬ᶏ⮺ࠍࡔࠗ࠮࡜࡯࠯ߢ♧ߚߩߢ‫ޔ‬ㅪ⛯♧ൻߦࠃࠅ‫ߩߘޔ‬ᓟߩ⊒㉂ߦ߅ߌࠆࠛ ൻߔࠆߎߣߦࠃࠅࠣ࡞ࠦ࡯ࠬ߇↢ᚑน⢻ߢ޽ࠅ‫ޔ‬ㅪ࠲ࡁ࡯࡞෼₸߇ૐਅߔࠆߎߣ߇ࠊ߆ߞߚ‫♧⛯ ޔߪࠇߎޕ‬ൻߦࠃࠅࠕ࠽ࠕࠝࠨ߆ࠄ 78.8 g/L‫߆࠰ࠗࠟ࠴ޔ‬೽↢ᚑ‛ߢ޽ࠆࠣ࡝࠮ࡠ࡯࡞ߩ෼₸߇㜞ߊߥߞߚߚ ࠄ 123 g/L ߣ㜞Ớᐲߩࠣ࡞ࠦ࡯ࠬࠍ↢ᚑߔࠆߎߣ߇
߼ߢ޽ࠆߣ⠨߃ࠄࠇߚ‫ޔࠄ߆ߣߎߩߎޕߚ߈ߢ ࠣߩࠄ߆ࠬ࡯ࠦ࡞ࠣޔ߅ߥޕ‬2 ࿁⋡ߩ♧ൻߦ߅ߌࠆ㉂⚛
࡝࠮ࡠ࡯࡞෼₸ߪ‫ޔ‬1 ࿁♧ൻߒߚࠕ࠽ࠕࠝࠨ‫ߦ ࠃ߅ޔ‬ኻߔࠆ㒖ኂߪዊߐ޿ߎߣ߇⏕߆߼ࠄࠇߚ‫ޔߚ߹ޕ‬߮࠴ࠟࠗ࠰ߩ⊒㉂ߦ߅޿ߡߘࠇߙࠇ 0.06‫ ߮ࠃ߅ޔ‬ㅪ⛯♧ൻߦࠃࠅࠣ࡞ࠦ࡯ࠬࠍ㜞Ớᐲൻߔࠆߎߣߢ‫ޔ‬
0.10g-glycerol/g-glucose ߢ޽ߞߚ߇‫ޔ‬2 ࿁♧ൻߒߚᓟ ࠛ࠲ࡁ࡯࡞෼₸߇ૐਅߔࠆ߽ߩߩࠛ࠲ࡁ࡯࡞߽ ߩ ⊒ ㉂ ߢ ߪ ߘ ࠇ ߙ ࠇ 0.09 ‫ ߮ ࠃ ߅ ޔ‬30 g /L એ਄ߦ㜞Ớᐲൻߔࠆߎߣ߇ߢ߈ߚ‫ޕ‬
0.15g-glycerol/g-glucose ߣߥߞߚ‫ޕ‬ᶐㅘ࿶߇㜞޿᧦ઙ
ߢ㉂Უࠍၭ㙃ߔࠆߣࠛ࠲ࡁ࡯࡞෼₸߇ૐਅߒߡࠣ࡝ References࠮ࡠ࡯࡞෼₸߇਄᣹ߔࠆߎߣ߇ႎ๔ߐࠇߡ޿ࠆߎߣ [1] Yamashita, Y., et al.: Effective enzyme
߆ࠄ[2]‫ޔ‬ㅪ⛯♧ൻߦ߅޿ߡ♧ൻᶧߦṁ಴ߔࠆ‛⾰ߩ
saccharification and ethanol production fromỚᐲ߇㜞ߊߥߞߚߎߣߦࠃࠅ⊒㉂ᤨߩᶐㅘ࿶߇㜞ߊ
Japanese cedar using various pretreatment methods.
ߥࠅ‫࡞࡯ࡠ࠮࡝ࠣޔ‬෼₸߇㜞ߊߥߞߚߎߣ߇⠨߃ࠄ
J. Biosci. Bioeng., 110, 79-86 (2010)ࠇߚ‫ޕ‬[2] Modig, T., et al.: Anaerobic glycerol એ਄ߩ⚿ᨐ‫ޔ‬ㅪ⛯♧ൻߦࠃࠅࠛ࠲ࡁ࡯࡞෼₸߇ૐ
production by Saccharomyces cerevisiaeਅߔࠆ߽ߩߩ‫ߩ࡞࡯ࡁ࠲ࠛޔ‬㜞Ớᐲൻ߇น⢻ߢ޽ࠆ
strains under hyperosmotic stress. Appl.
ߎߣ߇⏕߆߼ࠄࠇߚ‫ޕ‬
Microbiol. Biotechnol., 75, 289-296 (2007)

6

Experimental study on grip-release mechanismof slanted multi-beam structure with Ti-Ni super elastic wireStudent number:04B04186 name: Kazuhito EMURA supervisor: Kunio TAKAHASHI
February 17, 2012
1.

Wide real contact area cause large adhesion force. If contact surface
has a roughness, contact area and adhesion force will be declined. But
beam structure enables to absorb surface roughness by deformation asshown Fig.3 and gets wide real contact area. Deformation causesinternal stress that disturbs adhesion force. So the structure is made up
of slanted and soft beams that is easy to absorb surface roughness andgenerates large adhesion force.

Introduction

Geckos can quickly move on wall and ceiling against gravity. It has
been said the ability depends on a lot of hairs with two levelnano-scaled structure on their foot surface, called seta-spatula [1]. By
this structure, it is possible to absorb surface roughness and to grip
various surfaces by Van der Waals force [2][3]. Large gripping force
causes difficulty in the release of surfaces. The geckos use tangential
force to control the gripping force. It is suggested that the beam and thecontact plane are required to mimic the function of the gecko fingers[4]. The effect of the beam curvature on the gripping force isinvestigated using the beam theory and the experiments with a single
beam structure [5][6]. A prototype multi-beam structure isdemonstrated by Wang [7]. However, the beams are vertical and too
hard to absorb the surface roughness.
In the present study, a multi-beam structure with soft and slanted
beams is made using Ni-Ti wire, the gripping function is investigated,and discussed with the beam theory.
2.

Fig 3 Absorb surface roughness
3.2 Process of making
Based on theory of multi-beam structure considered above, prototype
was made as slanted and soft beams. Beams were made of nitinol wirethat had super elasticity for repeatedly use.
The following illustration as shown Fig 4 is process of making.
1. Line the wires at equal space and put them between cupper andtin sheet and fix them by pressure welding.
2. Polish the tops of them and make same plane

Mechanism to control grip and release

It is assumed that all beams are equal to each other and the dynamics
of multi-beam structure are equivalent to superposition of one of single
beam and the criterion of adhesion crack is whether maximum normal
stress is larger than critical stress.
Assume that top of elastic beam contacts with flat rigid surface,
applying normal force ‫ܨ‬୬ to the structure cause stress distribution at
contact area as shown Fig.1. If tangential force ‫ ୲ܨ‬is applied with‫ܨ‬୬ ,the maximum stress decreases and the structure can grip the surface.
In this way, the beam structure can control grip and release. .

Fig 4 Process of making
3.3 Method of measurement of gripping area
Fig.5 shows schematic illustration of experiment system. Becausebalance can measure vertical force, adhesion force was measured as F
by vertical displacement in condition that structure contacted tilting
table. Experiment was carried out as following condition in Table.1 andfollowing process.
Number of beamͳͲAngle of beam to surface ߠͶͷሺሻ
Length of beam ݈ͳǤʹሺ ሻ
Diameter of beam ݀ͲǤͳͲሺሻ
Thickness of sheetͲǤͷሺሻ
Angle of tilting table ߮ͷ͸ͷሺሻ ͷሺሻ
Speed of displacementͳ ɊΤ 
Resolution of displacementͳɊ
Resolution of balance
1mgf

Fig 1 Grip and release mechanism
Correlation between ‫ ୲ܨ‬and ‫ܨ‬୬ about gripping area is expressed as
Fig.2.[8] In Fig.2, ݈ is length, ݀ is diameter and ߠ is slant of beam.
ܵ is area and Z is the section modulus of contact surface. F means
resultant of ‫ ୲ܨ‬and ‫ܨ‬୬ and ߮ is angle between vertical and resultant.

Table.1 Condition of experiment
Urethane sheet 0.5mm thick as adhesives was pasted on tilting table.
Parallel between tops of beams and contact surface was judged byvisual observation through microscope.
Vertical 1Ɋ displacement away from the tilting table at contact
point and 9 seconds wait were repeatedly applied to the structure atcontact point. Contact point was defined as the point when measured
value of valance was changed obviously by displacement approaching
to tilting table.
The value was declined by drawing up. Change of the value wasregarded as force that structure generated to contact surface. And theFig 2 Correlation between ࡲ‫ ܜ‬and ࡲ‫ܖ‬
3.

Making Prototype and Measurement of gripping area

3.1 Large adhesion force by absorbing surface roughness

7

point of the maximum absolute value was regarded as the detachment
point.

Consequence of Experiment

The experimental and theoretical consequences of gripping area
between ‫ ୲ܨ‬and ‫ܨ‬୬ are shown in Fig 6.ű is the detachment point inexperiment. Each points are calculated from measured ‫ ܨ‬and angle ߮.
And dash lines are theoretical consequence calculated from eachelements and average of ‫ܨ‬ఝୀସହ . The theoretical gripping area isnarrow as 1% of experimental consequence.

(mgf)
normal load Fn

50

40

30

20
number of beams=10
d=0.10(mm)l=1.2(cm)
T=45(deg)

10

0

10

20
30
tangential force Ft

Fig.6 Gripping area between
3.

40
(mgf)
୲

and

Thickness of urethane sheet
Angle of tilting table causes difference in displacement of
distance between tops of beams and contact surface. It meansthat contact condition is different each time.
And Watanabe’s model is based on beam theory. Thickness ofurethane sheet is beyond beam theory.

5.

Conclusion

In this study, experiment of grip-release mechanism using multi-beam
structure with super elastic wire was carried out. Based on this study,
we concluded that slanted multi-beam structure is useful for control
grip and release.
The gripping area between tangential force and normal load wasinvestigated the theoretical and experimental consequence. Comparedthese consequence, we conclude that another criterion of adhesion
crack have to be proposed.
Reference[1]Walker.G. Adhesion to smooth surface by insects a review, Int .
J.adhesion and adhesive 13: 3-7.1993.
[2] K.Autumn, W.P.Chang, R.Fearing, T.Hsieh, T.Kenny, L.Liang,
W.Zesch, R.J.Full. Adhesive force of a single gecko foot-hair.
Nature405:681-685.2000[3] K.Autumn, M.Sitti, A.M.Peattie, W.Hansen, S.Sponberg, Y.A.Liang,
T.Kenny, R.Fearing, J.N.Israelachvili, R.J.Full,. Evidence for van der
Waals adhesion in gecko setae. PNAS99(19):12252-12256.2002[4] K.Takahashi, J.O.L.Berengueres, K.J.Obata, S.Saito. Gecko’s foothair structure and move quickly. International journal of adhesion &Adhesive 26 (2006)639-64[5]Jun’ichi Watanabe, “Measurement of adhesion force of the structure
mimicking gecko foot hair for designing ceiling-walking robot”, Tokyo
Institute of Technology 2006
[6]Junichi Watanabe,”Effect of stiffness and shape on releasemechanism mimicking gecko foot-hair”, Tokyo Institute of Technology
2008.
[7]Miaomiao Wang,”Experiment on Prototype of Quick-release
Mechanism Using Multi-beam Structure”, Tokyo Institute ofTechnology 2009.
[8]Jun’ichi Watanabe, Kunio Takahashi “Release mechanism
Mimicking Gecko Foot-hair”,ͳͶ୲୦ symposium on Microjoining andAssembly Technology in Electronics, February 5-6 2008, Yokohama
369-372

Fig .5 System of experiment
4.

3.

50

୬

Discussion

As shown in Fig.6, it can be said that angle of resultant is near 45
degree might be good to grip. For example this structure can grip
20mgf weight applying tangential force between about 15mgf toabout 25mgf and it can release applying tangential force under about
15mgf or over about 25mgf. That is to say multi-beam structure cancontrol grip and release by applying tangential force.
It can be said that theoretical and experimental consequence have
similar tendency that this structure may be good to grip applying ‫ܨ‬inthe direction of beam axis. Theoretical gripping area is too narrow tocontrol, on the other hand experimental gripping area may be enoughto control. It is suspected that there is another criterion of adhesion
crack. Causes that affect the criterion are;
1. Shearing force
By Mieses yield criterion, criterion of adhesion crack may beaffected by not only normal stress but shearing stress.
2. Condition of contact8

Selective Catalytic Reduction of Nitrogen Oxides with Propene over (Ti,Zr)2O4
catalyst supported by Mo
Student Number: 06-08084

Name: DongIl KIM

1. Introduction

Supervisor: Hirofumi HINODE, Chris SALIM(Wako) was dissolved in 50 ml of deionized water andmixed with (Ti,Zr)2O4. Then the mixture was stirred atroom temperature for 12 h and dried at 80ଇ for 12 h.
The catalyst prepared by IM method was designated
Mo/(Ti,Zr)2O4.
In the MM method, Mo precursor used in IM
method was calcined first at 400Υ prior to mixing
with (Ti,Zr)2O4 prepared earlier. The mixing was
carried out by hand using a mortar and pestle with alittle amount of ethanol. All prepared catalysts were
calcined at 550ଇ in air for 4 h. Finally, the catalysts
were crushed and sieved to 0.71-1.00 mm [4]. The
catalyst prepared by MM method was designated
MoO3+(Ti,Zr)2O4
The SCR activity experiments were carried out in a
fixed-bed flow reactor. The reactant gas was composedof 1500 ppm NO, 10% O2, 1500 ppm C3H6 and He as abalance gas. 0.8~1.2 g catalyst was used with acorresponding space velocity of 13000 h-1 and a total
gas flow rate of about 4 mL/s. The temperature waschanged stepwise from 150ଇ to 550ଇ.
NO and NO2 concentration were analyzed by NOx
analyzer (Shimadzu, NOA-7000). N2O, CO2 and CO
were analyzed by gas chromatograph (GL Science,
GC-323w for N2O; GL Science, GC-390 for CO2 andCO). The catalysts were characterized by TG-DTA,
XRD, N2 adsorption (Autosorb 1MP/TSU, BET
analysis) and SEM-EDS.

Nitrogen oxides (NO, NO2 and N2O) are major
sources of atmospheric environmental problems suchas photochemical smog, acid rain, ozone depletion andgreenhouse effects. Furthermore, presence of nitrogenoxides in the atmosphere can cause problems to humanhealth. Most of the NOx comes from automobiles(48.5%) and power plants (46.2%) using fossil fuels[1].
Nowadays, three-way catalysts (TWC) are generally
used to reduce NOx in automobiles. This method can
reduce NOx with high efficiency at specific air to fuel(A/F) ratio. However, the development of lean-burnengines that have higher fuel economy and cleaner
emissions requires catalyst for NOx reduction that canfunction also at higher A/F ratio which cannot be
achieved using TWC only. One potential method to
reduce NOx in exhaust gas is selective catalytic
reduction of NO using hydrocarbon as reducing agent(HC-SCR of NO) [2]. Previous study showed that TiO2
supported Mo catalyst was effective in SCR of NO [3].
Another study reported that, TiO2-ZrO2 composite usedas support material exhibited higher catalytic activity
towards HC-SCR of NO when compared to bare TiO 2[4].
Therefore, in this study, the catalytic activities of(Ti,Zr)2O4 supported Mo catalysts prepared by two
different methods, impregnation (IM) and manual
mixing (MM), were investigated for HC-SCR of NOx
using propene as reducing agent.

3. Results and discussion
Figure 1 shows the conversion of NO to N2 over TiO2,
ZrO2, MoO3, (Ti,Zr)2O4 catalysts. (Ti,Zr)2O4 catalyst
shows the highest activity at 450ଇ among all the

2. Experimental(Ti,Zr)2O4
catalyst
waspreparedbyco-precipitation method using Zr(NO3)2㻚2H2O (Wako)and [(CH3)2CHO]4Ti (Wako) as precursors with thesame molar ratio for Ti and Zr [4]. First,Zr(NO3)2㻚2H2O was dissolved in 35 ml deionizedwater with continuous stirring and the pH wascontrolled by addition of HNO3 (pH=0.5-1.0). Then[(CH3)2CHO]4Ti was added to the solution and the pH
was changed by addition of NH3 solution (pH=9-11).
The resulting mixture was filtered, dried at 100ଇ for
8 h and calcined at 550ଇ in air for 4 h.
In the case of IM method, (NH4)6Mo7O24.4H2O

Fig.1.. Catalytic activity of TiO2, ZrO2, MoO3, (Ti,Zr)2O4 for the
reduction of NO to N2 using C3H6 as a reductant.

9

activity as compared to the bare (Ti,Zr)2O4 catalyst.
Figure 4 shows the conversion of NO to NO2 andthe conversion of C3H6 to CO2 using the (Ti,Zr)2O4 and
30% MM method MoO3+(Ti,Zr)2O4 catalysts. Additionof 30%Mo by MM method increased the conversion of
C3H6 to CO2 while decreasing the conversion of NO to
NO2. This suggested that addition of Mo improved theselectivity of NO reduction to N2 as shown in Figure 3.
It was reported by Ch. Fountzoula et al.[3] that
addition of MoO3 to bare catalysts improves its
mechanical strength through chemical and structural
promoters which enlarges the temperature window of
the SCR reaction [3]. The synergistic effects could also
be observed in the result of this study seen between
MoO3 and (Ti,Zr)2O4.

catalysts followed by ZrO2 and TiO2 while MoO3
showed the lowest conversion to N2.
Figure 2 shows the conversion of NO to N2 overMo/(Ti,Zr)2O4 catalyst by IM method with Mo loadinglevels from 0-30 wt.%. Metal loaded catalyst showed
lower catalytic activity than unloaded (Ti,Zr)2O4
catalyst. It is possible that the addition of Mo metal on
the (Ti,Zr)2O4 support by IM method reduced theexposed surface of (Ti,Zr)2O4 available for reaction
thus reducing its catalytic activity [3].

4. Conclusions
The
catalyst
preparedMM
methodMoO3+(Ti,Zr)2O4 catalyst showed catalytic activity
towards selective catalytic reduction of NO to N2 with
C3H6 as reducing agent. Compared to the IM method,
catalyst prepared by MM method exhibited higher
catalytic activity. The catalysts prepared by MM
method showed optimum ratio of Mo addition at 30%.
It is possible that MoO3 high oxidation ability
improved the catalytic activity of MoO3+(Ti,Zr)2O4 assuggested by previous study.

Fig.2.. Catalytic activity of Mo/(Ti,Zr)2O4 IM method catalyst for
the reduction of NO to N2 using C3H6 as a reductant.

References
Fig.3.. Catalytic activity of MoO3+(Ti,Zr)2O4 MM method catalyst
for the reduction of NO to N2 using C3H6 as a reductant.

[1] Norio Arai Techno-System Corp (1997)[2] R.Burch, T.C.Watling, Appl.Catal.B, 11, 207(1997)[3] Ch.Fountzoula et al. Appl, Catal. B, 35, 295(2002)[4] Yang Wang; Master Thesis, Tokyo Instituteof Technology (2010)

Fig.4.. Catalytic activity of (Ti,Zr)2O4, 30%MoO3+(Ti,Zr)2O4,
catalyst for the conversion of NO to NO2 and C3H6 to
CO2 using C3H6 as a reductant.

Figure 3 shows the conversion of NO to N2 over
MoO3+(Ti,Zr)2O4 catalysts by MM method with Mo
loading levels from 0-50 wt.%. Catalyst with 30%
MoO3 showed the highest catalytic conversion at
450ଇ followed by 10% and 20%. However, further
increase of Mo loading to 50% showed a decrease in

10

A study the decompositon of nitrous oxide over TiO2-CeO2 catalysts
Student Number: 06B20850 Name: Yutaro FUKUCHI Supervisor: Hirofumi HINODE

1. Introduction
Nitrous oxide (N2O) is one of the powerfulgreenhouse gases. Global warming potential
per molecule of N2O is about 300 times that ofcarbon dioxide (CO2) [1]. Moreover, it is very
stable and has a lifetime of more than 100
years in the atmosphere. N2O is also identified
as a contributor to the destruction of ozone
layer in the stratosphere. Although the major
sources of N2O are agricultural byproducts andbiomass burning [2], the industrial sources ofN2O such as production of adipic acid,combustion process and automobiles have been
increasing [2]. Therefore, the decomposition of
N2O at these industrial sources is importantfrom the environmental point of view.
In our laboratory, activity of the rutile
structure titania (TiO2) for the decompositionof N2O had been studied, and had shown better
activity than that of the anatase structure [3].
And previous study reported that the additionof CeO2 improved catalytic activity of Nb/TiO2
catalysts during selective catalytic reduction of
nitrogen oxide in the presence of hydrocarbon[4].
In this research, the decomposition of N2O
over manually mixed TiO2-CeO2 catalysts wasinvestigated.

Table 1 Characteristics of each TiO2
TiO2 No.

Surface area

Particle

Rutile[m2/g]

size [Ǵm]content [%]Toho-1

32.8

҇0.42

93.3

Toho-3

73.8

҇3.48

95.0

Table 2 Characteristics of each CeO2

CeO2 No.

Surface area

D50

Purity[m2/g][μm][%]

JRC-CEO-1

156.9

21.31

99.99

JRC-CEO-2

123.1

4.36

99.97

sample was calcined for 5 hours at 600 ͠
under air flow.
In order to reduce the pressure drop, thesample was finally pelletized, crushed andsieved into sizes between 0.71 mm to 1.00 mm.
The following nomenclatures for the catalyst
samples are used: TiO2 x-CeO2 y (a:b) where x
and y represent each reference oxide number,and (a:b) means TiO2:CeO2 molar ratio. (e.g.
TiO2 Ԙ -CeO2 Ԙ (1:2) means Toho-1 and JRC–CEO-1 were mixed under TiO2:CeO2=1:2
molar ratio).
2.2 Catalytic activity experiment
The catalytic reaction was carried out in a
fixed-bed flow reactor under atmosphericpressure. The reactant gas was prepared bymixing N2O, O2 and He as a balance gas. Thenthe mixed gas of 1000 ppm N2O, 0 or 10% O2
and He was fed to the catalyst at a flow rate
which corresponded to a space velocity of
16000h-1.
Gas chromatography (GC323w; GL Science
Co., with Porapak N, Porapak Q and Molecular
Sieve 13X column) was used to analyze N2O,
N2 and O2.

2. Experimental
2.1 Catalyst preparation
TiO2-CeO2 catalysts were prepared bymanual mixing method. Two types of TiO2
(Toho-1 and Toho-3, Toho Titanium Co.) andtwo types of CeO2 (JRC-CEO-1, Santoku Co.
and JRC-CEO-2, Daiichi Kigenso Co.) wereused. The characteristics of each TiO2 andCeO2 were shown in Tabel 1, 2.
During preparation, TiO2 was mixed with
CeO2 manually in a mortar using small
amount of ethanol for better mixing. Then the

2.3 Catalyst characterization

11

Characterization of catalyst was performedby X-ray Diffraction (MultiFlex), and ScanningElectron Microscope (JSM-5310LV)-Electronic
Data Systems (JED-2140).

3. Result and discussion
Figure 1 shows the catalytic activity resultsof all combinations of TiO2-CeO2 at same molar
ratio (1:1) for the decomposition of N2O to N2
without O2. Compared to bare TiO2 (Toho-1, 3)
which only decomposed 48% and 45% N2O at
700 ͠ , mixing of CeO2 improved catalytic
activity. And this result shows that
JRC-CEO-1 improved catalytic activity better
than JRC-CEO-2. Especially, TiO2 Ԛ -CeO2 Ԙ(1:1) catalyst decomposed N2O 100% at 650͠.
Fig. 2 The effect of molar ratio of TiO2Ԛ:CeO2Ԙ oncatalytic activity for the decomposition of N2O to N2
without O2.

4. Conclusions
Mixing of CeO2 into TiO2 improved catalytic
activity of bare TiO2. TiO2 Ԛ -CeO2 Ԙ (1:1)
showed the best catalytic activity with the N2O
decomposition achieving 100% at around650͠.
References[1]FAO, Livestock a major threat to environment(29 Nov.2006.)[2] U.S. Emissions Inventory 2010:
Inventory of Greenhouse Gas Emissions andSinks: 1990-2008[3]K.Yanagida, Master thesis, Tokyo Institute ofFig. 1 The effect of mixing of CeO2 on catalytic

Technology(2006)

activity for the decomposition of N2O to N2 without[4]M.Makii, Master thesis, Tokyo Institute ofO2.

Technology(2008)

Figure 2 shows the catalytic activity result ofdifferent molar ratio of TiO2Ԛ-CeO2Ԙ for thedecomposition of N2O to N2 without O2. The
catalytic activity of TiO2 Ԛ -CeO2 Ԙ (9:1) andTiO2 Ԛ-CeO2 Ԙ(1:9) were lower than that of
TiO2 Ԛ-CeO2 Ԙ(1:1). Particularly, for catalyst
with higher molar ratio of TiO2, the catalytic
activity decreased considerably.

12

Employment data analysis and Relationships of
employment support in Tokyo Institute of Technology
Student Number: 07-15526

1

Name: Akihito TAKANASHI

Introduction

3

In general, the employment situation of Tokyo Instituteof Technology (Tokyo Tech) is better than that of otheraverage public university. However, the rate of passing
the interview for Tokyo Tech students falls because of
the deterioration of the employment situation in Japan
called the secondary employment ice age in recent years.
In addition, students have many problems. Such as thestarting time to seek job gets early, they have too muchinformation, and the supports for job hunting are very
diﬀerent among faculties and departments.
In this paper, I clarify the current problems of job
hunting in Tokyo Tech, and propose suggestions to improve employment support in Tokyo Tech. This thesis
will submit to Department of Academic Aﬀairs where
various data are provided.

Supervisor: Yukihiko YAMASHITA

Actual conditions of employment
support

Purpose of this paper is proposing suggestions to employment support in Department of Academic Aﬀairs
to improve their job hunting supporting environment.
Therefore, in this section, I analyze the status of employment support in Japan and Tokyo Tech.
In general, employment support consists 1) Community support to meet people and companies, 2) Careerplan support, and 3) Support for anti-selection. There
are several community supports to meet workers andcompanies, such as society exchange meeting with theOB, visit to OB, corporate seminars, joint seminars, andso on. Career plan support consists of support for selfanalysis seminar, career counseling, and course way ofworking. Support for selection consists of literally, ES,
WEB test measures, group discussion (GD), and interview. This section introduces several typical companies
that support employment, and explain the trend of em2 The current state of job hunting
ployment support.
I explain organizational structure and support in the
In this section, we show the two axises of the current
university, the contents of each support. And I provide
status of job hunting, in Japan and in Tokyo Tech.
Ratio of job oﬀers to job seekers graduated in March the problems of employment support in Tokyo Tech.
2011 was 1.28. And students who had appointed job in
Feb. To the all in March 2011 is 0.774. Then, about 20
performance data
percent college students couldn’t get an oﬀer, and grad- 4 Employment
uated from college.
analysis in Tokyo Tech
In Tokyo Tech, 33 undergraduate students,125 mastercourse students, and 122 doctor course students gradu- At ﬁrst, I introduce analysis methods of statistics andated without a job oﬀer.Until twenty years ago, a recom- economics. Next, I show the result of analysis of
mendation from Tokyo Tech is equal to a job oﬀer. How- employment data and the current state of employment
ever, recently the recommendation is equal to pass of the support. Finally, I propose a strategy to conduct emﬁrst interview. With the change of human resources that ployment assistance of students for Academic Aﬀairs
companies seek, they change the interview points from in Tokyo Tech. I use the GPL software called weka
impression to conpetency .
for the analysis mainly. According to the weka, feaCharacteristics of competency interview is that they tures are extracted from the data. I verify the relationdemand to submit a self-PR statement, called the entry ship between the result of analysis and the problems
sheet (ES). And they judge the competency from inter- described in Section 3. In addition, organize new theview with his ES. The general ﬂow of the current re- state from Section 2 and Section 3 using an economic
cruitment is as follows, 1) Submission of ES and WEB analysis of the Strength weakness Opportunity Threat
TEST, 2) Group discussion, 3) Personnel interview, 4) (SWOT) analysis, the Customer Competitor Company
Director interview, and 5)Oﬃcer interview.
(3C) analysis, and Political Economic Social Techno1

13

logical (PEST) analysis, I suggest the direction of thejob hunting support for depertment of academic aﬀairs
which they should focus on. (table.1)

5

Conclusion

In the current employment support, there are diﬀerencesin the mechanism that connects to the OB and companies by departments. It has been strongly aﬀected to job
hunting.
I propose to construct an environment which makes
easy to seek jobs as a student in Tokyo Tech. Concretely
we have to produce seminars in Tokyo Tech not to go
to extra seminars to devote time to research, make cooperation stronger among job hunting support oﬃces ofdepartments, and among OB associations.
        

References[1] Career Support at Tokyo Tech, ”Research of employment situation, Tokyo Institute of Technology
Graduation , (2009, 2010, 2011)[2] Career Support at Tokyo Tech, Employment guidebook, Tokyo Institute of Technology , (2011, 2012,2013)[3] Junji TODA (2011) , College Graduates Job Opening Survey (Graduated 2012), Recruitment Trend,
http://bit.ly/9B2S1F[4] Ministry of Health, Labour and Welfare Press
release material, ”Survey of Employment appointed students, will graduate universities, etc.”
http://bit.ly/xoS6EK (17,Jan,2012)    !"

2

14

A Study on the effectiveness of Ecodriving Program on Fuel Economy
Student Number:

07_16282

Name: Ayuka Takeda

1. Introduction

Supervisor: Yasunori Muromachi

I asked nineteen drivers to take the ecodriving
warming,training program and equipped their cars with

reduction in emissions of carbon dioxide is one of

the calibrate GPS and FC gadgets before andthe major issues in several sectors. In Japan,

after the program. Both equipments collected

about twenty percent of the emissions are

instantaneous

produced by transport sector and about fifty

collection process is shown in Figure 1. I collected

percent of the emissions of the sector are

the FC and GPS data that came from the three

generated by passenger cars. So, how to reducedays of pretest or normal driving before thethe emissions from passenger cars is the mosttraining day, and another three days of posttest or

important. Ecodriving is one of the measures for

ecodriving after the training day. For collecting

reducing the emissions from passenger cars as

the driver’s trip data, each driver used his or her

well as trucks. Recently, the government andown car and route freely.

In

order

to

mitigate

global

data

per

0.10

second.

Datasome private organizations actively promoteecodriving program, because the effectiveness ofthe ecodriving program has often been confirmed
in preceding studies, and the program requires
less investment in infrastructure development.
However, while the preceding studies mostly
focused on the ecodriving conducted during the
program, the studies on the ecodriving in thereal-world have not been conducted much.
Figure 1 Data Collection Process

The objective of this study is to investigate
the effectiveness of the ecodriving program in the

By indicating GPS data on GIS roadreal-world by monitoring fuel consumption before

network map and by matching the location withand after the program and by analyzing whether

fuel consumption data, I built the dataset

the participants of the ecodriving program

containing the information on fuel consumption

actually practice ecodriving such as idling stop

during stopping and acceleration afterand gentle start in the real-word.

stopping, stopping time mostly due to intersection

thesignals, and acceleration patter after the stopping

2. Data Collection

for each driver. The data on the stopping is used
for investigating the practice of idling stop, while

In order to collect fuel consumption (FC)the data on the acceleration is used for studying

data and Global Positioning System(GPS) data,

15

Level =5(%)

Idling stop

Gentle-start

Improved

B,H,J,K,

B,H,J,K,Q,R,S

Worsened

N,P

Not significant

C,D,E,F,G,I,L,M,O,Q,R,S,T

C,D,E,F,G,I,L,M,N,O,P,T

Table 1 The Effectiveness of the Ecodriving Program (B-T indicate each driver’s ID)
that of gentle start.

3.

rate of fuel ec onomyThe

resultoftheanalysis

on

㻞㻞

the

㻝㻤

in Table 1. In term of idling stop, after the㻝㻢

ecodriving program, four out of the nineteen㻝㻠drivers㻝㻞

reductionin㼓㼑㼚㼠㼘㼑㻌㼟㼠㼍㼞㼠㻘
㻌㻞㻜㻚㻜

㻞㻜

effectiveness of the Ecodriving program is shown

demonstrated

㼕㼐㼘㼕㼚㼓㻌㼟㼠㼛㼜㻘
㻞㻟㻚㻞

㻞㻠

The Results of the Analysis

fuel

㻌㻝㻢㻚㻞
㻝㻡㻚㻜

㻝㻡㻚㻡

㻝㻡㻚㻝

la rg esm a ll㼙 㼑 㼐㼕㼡 㼙㻝㻜consumption significantly in the real-world. In
term of gentle start, seven drivers indicatedFigure 2 Fuel Economy during the Ecodriving

reduction in fuel consumption significantly. Most

Program and that in the real-world

driverswere

successful

inreducing

fuel4.

consumption after the program, however, thedifferences were not statistically significant.

Conclusions
In this study, I investigated the effectiveness

Next, I classified the drivers into the groupof the ecodriving program in the real-world by

with large difference, medium difference andmonitoring fuel consumption before and after thesmall difference. Each group consists of six toprogram. According to the results, four out of theeight drivers. Firstly, in the analysis of idling stop,

nineteen drivers practiced idling stop, while

the group with large difference tended to include

seven conducted gentle start. I also found thatmany female drivers and the group with small

female drivers practiced idling stop, while maledifference tended to include many male drivers.

drivers

In the analysis of gentle start, the tendency was

relationship between the fuel economy during thethe reverse. Therefore we believe that female

ecodriving program and that in the real-world

drivers practice idling stop, while male drivers

was not so clear.

practiced

gentle

start

more.

The

practice gentle start more easily.
Second, Figure 2 shows the relationship

Reference

between the fuel economy improvement during

1)

the ecodriving training program and the three

Bo,Ou.,2011.

groups. In the analysis of idling stop, the groupsystem with applications in vehicles with humanwith small difference indicated the largest

drivers

improvement, while in the analysis of gentle start,

Transportation Research Part D, 16,515-524

the group of medium difference the largest.

2) Kenji Mori and Hiroshi Makishita, 2010.

Therefore, the result of the ecodriving program is

Effectiveness of ecodriving at the time of

not necessarily related with the real-worldcongestion. Journal of civil planning, Vol.27 No.5,

practice of ecodriving.

935-940

16

Changxu,

Wu.,Guozhen.,
A Fuelandeconomyautonomous

Zhao.,

andoptimization

vehicles.

Effects of Temperature and Solution Type on Corrosion Behavior
at Acceleration Test of Paint-coated Steel Plate with DefectStudent Number: 07-29586 Name: Kenji Wada Supervisor: Nobuaki Otsuki
Specimens are arranged inside the container
as shown Fig. 2. As time passes, experiments insolution with different temperature are taken
as shown in Table 1.
Table 1 Measuring Setting

1. Introduction
Considering economic aspects, there is highdemand for methods of estimating life span ofreal structure. For this purpose, acceleration
test has been taken in the past studies.
In this study, offshore structure is focused on.
Offshore structures consist of paint-coated steel
material and corrosive deterioration is main
problem. Especially, macrocell corrosion due todefect of paint surface causes serious problem.

40Υ, 3% NaCl
Air 4L/min

There are two goals in this study.
1) To know about effects of environmental
factors (temperature and oxygen concentration)
on corrosion of paint-coated steel plate withpaint defect.
2) To build an electrical circuit model for
macrocell corrosion.

Fig. 2 Immersing
Setting

O2
Saturated

High
O2

Low
O2

40Ԩ

①

④

⑤

30Ԩ

②

―

―

20Ԩ

③

―

―

(2) To know the influence of oxygen,concentration of O2 at electrolyte (40 Υ , 3%
NaCl) is controlled by 3 stages. Specimens with
OA are exposed.

For goal (1), electrical values (such as current
density, resistance, and capacitance) are used toestimate corrosion rate. Furthermore, for goal(2), an electrical circuit model is considered byusing data from experiments.

2.3. Measurement Setting
Two kinds of electrical measurement are takenwith changing temperature and oxygen (Table1). It is because corrosion is chemical reaction
involving corrosion current flow.
(1) AC impedance method for microcell
corrosion current(2)Macrocell corrosion current

2. Experimental Procedure
2.1. Specimens
To focus on macrocell corrosion, paint-coatedsteel plates with defect at the center are used.
Two types of paintings are used, Oil Alkyd (OA,
paint not for marine environment, red, 150μm),
Tar epoxy (non-specific chemical substancetype) (NTE, paint for marine environment,
black, 150μm). A steel plate consists of six
divided parts which are connected each other bylead wires (Fig. 1).

Current Density (ȣȣ/cm2)

3. Result and Discussion(1) Influence of Temperature on Corrosion at
Defect Point
Macrocell and microcell current density
became higher at higher temperature.
DEFECT

1 day(a) Steel plate (back) (b) Paint coating (front)
Fig. 1 Specimen
2.2. Experiment Setting(1) Specimens are put into electrolyte (40Υ,
3% NaCl) with 4L/min air.

1 week2 week3 week

Fig. 3 NTE’s Macrocell Current Density at
Defect Point (Immersing Temp: 40Υ
/ Measuring Temp: 20, 30, 40Υ)

17

(5)Comparison of Experimental Result andAnalytical Solution
Fig. 6 shows the analytical model used in thisstudy. From Fig. 7, model and result can match
qualitatively.

Current Density (ȣȣ/cm2)(2) Influence of Paint Deterioration on
Macrocell Corrosion at Defect Point andComparison (before paint peeling)
As time passed, more current passed throughin both of paint type. Besides, decrease of paintresistance happened simultaneously.
Comparing the both result, OA’s corrosion rate
was much higher than NTE’s until OA’s paintwas broken, and OA’s paint resistance waslower than NTE’s correspondingly.
Blister

Anode(Corrosion)
Cathode (OH-)
Bessel’s equation
3 week2 week
1 week1 day(a) NTE,

Current Density (ȣȣ/cm2)

Anode(Corrosion),

Fig. 6 Analytical Model

Cathode (OH-)

3 week2 week
1 week
1 day

Paint Electrical resistivity: 㸫1.67103(Ȑm)
Potential: 㸫2.19V(b)OA
Fig. 4 Macrocell Current Density

Fig. 7 Comparison of OA’s experimental
Result with Analytical Solution

Current Density (ȣȣ/cm2)(3)Distribution of Paint Deterioration
Inside NTE’s specimen, at ring 2, a blister anda strong cathodic tendency were observed.
Comparing resistance of this part with ring 5
which kept relatively strong coating, ring2 hadlower resistance and was deteriorated harderthan ring5.
(4) Influence of Oxygen Concentration
As concentration got higher, corrosion rate also
ot higher,
hig
got
especially, about microcell corrosion.

4. Conclusion(1) Macrocell and microcell current density
became higher at higher temperature.
As paint deterioration progressed, morecurrent passed through in both of paint type.
Oxygen concentration makes corrosion rate
faster, especially, microcell corrosion.
(2) Model and result can match qualitatively.
(Reference)
CommitteeofStructuralEngineering:
Guidelines for Evaluation of Durability andLoad-Carrying Capacity for Steel Structures
under Marine Environment

Macrocell
Microcell

Oxygen Concentration (mg/L)

Fig. 5 Current density with different
O2 concentration18

A study on the operation and maintenance performance of wind power plants owned
by public sector in Japan
Student Number：08B06625

Name：Teppei KATATANISupervisor：Naoya ABE

日本の公共部門による風力発電施設の運営・保守管理体制
片谷 鉄平
1．Introduction

questionnaires were collected (collection rate: 55%.)
Among the 37 respondents, 34 were municipalities
operating 91 plants and the remaining 3 were theorganizations in the third sectors operating 30 plants.
Figure 1 and 2 show the parts of the summary of thesurvey. With respect to the frequency of scheduled
maintenance, 34 plants had it once per year while 54
plants had it twice annually and 23 plants had ad-hoc
basis. Distribution of capacity factor, which is the ratioof actual electricity generation divided by the potential
generation and is commonly used as an indicator toevaluate the performance of the plants, is shown in
Figure 2. Mean value of the capacity factors among the
subjects was 15.4%.

Wind power is one of the prominent renewable energy
resources. In Japan, there are 1,814 wind power plants
as of March, 2011.[1] However, there are frequent
failures and accidents, often resulting in expensive
maintenance cost.
About 20% of wind power plants in Japan are
operated by the public and third sector; the latter refers
to the organizations invested by both public sector andprivate sector. It is said that about 60% of the plants
owned by Japanese public sectors are in financial
deficits.[2]This study focuses on the public and the third
sectors which own plant in Japan, especially withemphasis on their performance in operation andmaintenance.

30%~, 2Ad-hocbasis, 23

2．Objectives
There are three objectives in this study; they are: (1) toclarify a variety of information related to operating
wind power plant in public sector, (2) to investigate theinfluence of the operation and maintenance scheme tothe performance of the wind power plant owned bypublic sector, and (3) to investigate the influence of theoperation and maintenance scheme to the frequency offailure and accidents in public sector.

~10%, 20
Once, 34

20~30%, 24

10~20%, 71

Twice, 50

Figure 1(Left)：The frequency of the scheduled maintenanceper year between April 2008 and March 2011
Figure 2(Right)：Distribution of capacity factor

3．Data & Method of Analysis
4−2．Survival analysis

Table 1 shows the category of the data collected andtheir sources and details. The sources of the data aremainly the reports by NEDO and by Hokkaido Sangyo
Hoan Kantoku-bu, and the questionnaire survey thatwas conducted by the author.
Two methodologies are used for the analysis:
survival analysis for the objective (2) and multiple
regression analysis with dummy variables for theobjective (3).

Firstly, the difference of the survival rates of the plants
between those operated by public sectors andnon-public sectors in Hokkaido was examined. Whensurvival rates of a certain sample group are lower thanthose of other groups, then the first group is understoodas suffering more accidents and troubles.
Figure 3 shows the result of the analysis, wherewind power plants owned by public sectors indicate low
survival rates almost consistently compared to those
rates owned by non-public sectors. The figure implies
that the performance of the plants owned by public
sector is inferior to the performance of the plants
owned by the non-public sectors.

Table 1：Data for analysis

Operation and maintenancescheme in public sector(Frequency of the scheduled
maintenance, etc.)

Sources & details
Questionnaire survey tomunicipalities and 3rd sectors
operating wind power plants andselling electricity.
(Sample number 67,
Respondents 36)

Survival rate
1.00

Category of data

The reports by NEDO andOther information

Owned by non-public sector

0.75(April 2004 ~March 2011),
and questionnaire survey(April 2008 ~ March 2011)(e.g. private company)

NEDO’s map of wind condition
anddatabase of wind power plants.

Owned by public sector

0.25(Wind speed, usage period of wind
power plant, etc.)

Hokkaido Sangyo Hoan Kantoku-bu

0.50

Information of failures andaccidents

4．Result
0.00

4−1．Summary of the questionnaire survey
A questionnaire survey was conducted from Nov, 30,
2011 to Jan, 27, 2012. 67 questionnaires were sent toall plant operators which are in either public or third
sectors selling electricity generation and 37

0

500

1000

1500

2000

2500 Day

Figure 3：The result of Survival analysis of the plants in
Hokkaido

19

Secondly, the influence of the frequency of thescheduled maintenance to the survival rate of the
public sector plants was considered. Figure 4 shows theresult and Table 2 shows some statistics of each groupclassified by the maintenance patterns. From Figure 4
and Table 2, low survival rates of plants which hadscheduled maintenance twice per year compared toothers that do not depend on the frequency of scheduled
maintenance. Some public sectors conducted it twice
assumedly because of frequent failures. In fact, averageannual operating time of the plants with scheduled
maintenance twice per year group is longer than thetimes of the other two groups by almost 700 to 1,100
hours. In addition, the capacity factor of the plants
with scheduled maintenance twice per year group ishigher than the values of the other two groups byalmost 5%.

‫ݔ‬௜ଶ ：‫ݐ݈݊ܽ݌ݎ݁ݓ݋݌݀݊݅ݓ݄݁ݐ݂݋݁ݏܾܽ݄݁ݐ݂݋݁݀ݑݐ݅ݐ݈ܣ‬ሾ݉ሿ‫ݔ‬௜ଷ ：݄ܶ݁݊‫݊݋݅ݐܽݎ݁݌݋݂݋ݏ݄ݐ݊݋݉݂݋ݎܾ݁݉ݑ‬‫ͳͳͲʹ݈݅ݎ݌ܣ݈݅ݐ݊ݑ‬ሾ݉‫ݏ݄ݐ݊݋‬ሿ‫ݔ‬௜ସ ：ܷ‫݉݁ݐݏݕݏ݃݊݅ݎ݋ݐ݅݊݋݉݁ݐ݋݉݁ݎ݃݊݅ݏ‬ሾͲͳሿ
‫ݔ‬௜ହ ：‫݁ܿ݊݋݁ܿ݊ܽ݊݁ݐ݊݅ܽ݉݈݀݁ݑ݄݀݁ܿݏݐܿݑ݀݊݋ܥ‬Ȁ‫ݎܽ݁ݕ‬ሾͲͳሿ
‫ݔ‬௜଺ ：‫݁ܿ݅ݓݐ݁ܿ݊ܽ݊݁ݐ݊݅ܽ݉݈݀݁ݑ݄݀݁ܿݏݐܿݑ݀݊݋ܥ‬Ȁ‫ݎܽ݁ݕ‬ሾͲͳሿߝ௜ ：‫݉ݎ݁ݐݎ݋ݎݎܧ‬ሺሻ

where ߚ଴ is constant and ߚ௞ ሺ݇ ൌ ͳǡ ʹǡ ͵ǡ Ͷǡ ͷǡ ͸ሻarecoefficients of the explanatory valuables. The option ofusing remote monitoring system and the frequency ofscheduled maintenance are expressed as dummy
valuables. If ‫ݔ‬௜ସ ൌ Ͳǡ it means remote monitoring
system is not used by the plantሺ݅ሻ. And if‫݅ݔ‬ͷ ൌ ‫݅ݔ‬͸ ൌ Ͳǡ itmeans scheduled maintenance was conducted ad-hoc
basis year by year.
The result of the regression analysis is the equation
below.

Table 2：Some statistics of each group(Through April 2008 to March 2011)‫ݕ‬ො௜ ൌ െͳͺǤ͵ͳ‫ כככ‬൅ ͷǤͳͻ‫ݔ כככ‬௜ଵ െ ͲǤͲͲ͸‫ݔ ככ‬௜ଶ െͲǤͲͳ͸‫ݔ‬௜ଷ൅ͶǤ͹Ͷ‫ݔ ככ‬௜ସ െ͵Ǥ͸͵‫ݔ ככ‬௜ହ ൅ ͵ǤͶͷ‫ݔ ככ‬௜଺

Frequency of annual scheduled
maintenance

Valuable

Once

Twice

Ad-hocbasis

Number of wind power
plants34

50

23

Total number of
failures and accidents

96

200

89

Average annual
operating time [hours]

6,567

7,237

6,336

sd(2,010)

sd(1,292)sd(2,174)

Average annual
capacity factor [%]

12.63

17.05

12.35

sd(4.64)sd(6.49)sd(6.32)ܴ ଶ ൌ ͲǤͶͳͷǡ  ǣ ‫Ͳͳ כ‬Ψǡ‫ ככ‬ͷΨǡ‫ͳ כככ‬ΨThe results show that capacity factor is explained bywind speed (positive effect), location (negative effectwhen an altitude increases), and usage period (negative
effect). Furthermore, if an operator would conduct thescheduled maintenance twice per year instead of ad-hoc
basis, the capacity factor of plants could have increasedby 3.45%. And if an operator would introduce remotemonitoring system, the capacity factor could haveincreased by 4.74%.

※Failures and accidents happened through April 2004 to March 2011.
※sd：standard deviation
Survival rate
1.00

5．Conclusion
Ad-hoc basis

From the results of analysis above, the following
conclusions are obtained.
In terms of survival rate, the performance of plants
owned by public and third sectors is inferior to theperformance of those owned by non-public sectors. In
addition, those plants with twice scheduled
maintenance per year constantly indicate lower rates
over time compared to those plants with once or ad-hoc
scheduled maintenance per year.
However, in terms of capacity factor, those plants
which had twice scheduled maintenance show superior
performance compared to those which had once or
ad-hoc scheduled maintenance.
Thus this study found that lower survival rate does
not necessarily mean poorer performance of capacity
factor or annual operating time. This study also
confirmed that the option of remote monitoring systeminfluences positively on the performance of wind power
plants.
As an area of the further study, a cost analysis
which focuses on the operation and maintenancescheme would be important to improve theperformance of wind power plants.

0.75

Once

0.00

0.25

0.50

Twice

0

‫ ܨ‬ൌ ͳ͵Ǥͳͺ‫כככ‬

500

1000

1500

2000

2500 Day

Figure 4：The result of survival analysis of the plants in public
sector classified by the maintenance patterns

4−3． Regression analysis
The following regression analysis was conducted byleast squares method where the dependent variable iscapacity factor (number of samples = 104).

‫ݕ‬௜ ൌ ߚ଴ ൅ ߚଵ ‫ݔ‬௜ଵ ൅ ߚଶ ‫ݔ‬௜ଶ ൅ ߚଷ ‫ݔ‬௜ଷ൅ߚସ ‫ݔ‬௜ସ ൅ ߚହ ‫ݔ‬௜ହ ൅ ߚ଺ ‫ݔ‬௜଺ ൅ ߝ௜

Reference[1]NEDO, database “Situation of wind power in Japan”[2]NHK, News (2010. Nov. 18)‫ݕ‬：‫ݐ݈݊ܽ݌ݎ݁ݓ݋݌݀݊݅ݓ݂݋ݎ݋ݐ݂ܿܽݕݐ݅ܿܽ݌ܽܥ‬ሾΨሿ‫ݔ‬௜ଵ ：‫݉Ͳ͵ݐܽ݀݁݁݌ݏ݀݊݅ݓ݁݃ܽݎ݁ݒܽ݈ܽݑ݊݊ܣ‬ሾ݉Ȁ‫ݏ‬ሿ

20

Acid-catalyzed biodiesel production from triolein
Supervisor: Ryuichi EGASHIRA, Hiroaki HABAKI

Table 2.1 Compositions of the jatropha oil

Introduction

Fossil fuels are limited and finite, and the alternative fuelresources have been developed. Biodiesel is attracting an
increasing attention and strongly recommended as analternative diesel fuel because it is renewable. Biodiesel can beproduced from renewable biological sources such as vegetableoils and animal fats. Especially inedible vegetable oils such as
jatropha oils are recently becoming more attractive because of
their environmental benefits. The main content of vegetableoils is triglyceride (TG) and biodiesel is produced bytransesterification of the triglyceride. Transesterification is achemical reaction by triglycerides and alcohol in the presenceof a catalyst to form fatty acid methyl esters (FAME) andglycerol. The reaction consists of three consecutive reversible
steps, such as the conversion of TG to diglyceride (DG), that of
DG to monoglyceride (MG), and that of MG to glycerol. For
this reaction a catalyst is usually used for improving reaction
rate and shortening reaction time. In the ordinarytransesterification process the alkaline catalyst, such as NaOH,
is used and some disadvantages of this catalyst have beenreported. The vegetable oils originally contain free fatty acid
(FFA), causing low productivity of biodiesel and thesaponification of the glycerides is also serious. On the other
hand an acid catalyst, such as H2SO4, is not affected by thepresence of FFA and does not saponify FFA and methanol canreact to form FAME with the acid catalyst and FFA can beconverted to FAME at the transesterification as well.
Accordingly it is considered that the pretreatment to remove
FFA in the ordinary transesterification process can be reduced
by using acid catalyst. However the disadvantage of an acidcatalyst was reported to show a slower reaction rate thanalkaline catalyst[4]. Therefore this dissertation aims to study
the effects of the operational parameters on the acid-catalyzed
biodiesel production.

2

Myrstic acid
Palmitic acid
Stearic acid
Oleic acid
Linoleic acid
Free fatty acidmass fraction
0
0.103
0.138
0.427
0.263
0.110

(C14:0)(C16:0)(C18:0)(C18:1)(C18:2)

6 flat blade paddle

Baffle

water

H

d
B

b

1

Name: Xiao HAN

D

CStudent Number: 08-07783

water

Width of baffle: B=0.008 [m]Height of impeller: b=0.01 [m]Set height of impeller: C=0.0285 [m]Diameter of reactor: D=0.057 [m]Diameter of impeller: d=0.03 [m]Height of reactor: H=0.057 [m]Volume of reactor: V=130u10-6 [m3]Figure 2.1 Experimental apparatus
Table 2.2 Experimental conditions
Feed
Temperature
Methanol/oil molar ratio
Catalyst
Catalyst/oil mass ratio
Agitating speed
Reaction time3

Triolein40, 6012, 24, 30
H2SO40.01, 0.03, 0.05
600
0~10

[䉝]
T
n MeOH /n Feed [-]M cat. /M Feed [-]
N
[rpm]
t
[h]

Results and Discussion

3.1
Effects of experimental conditions on yield
3.1.1 Effect of temperature
Figure 3.1 shows the effects of temperature on yield of
FAME. Y increased with the operating temperature from 40Υ
to 60Υ. However Y decreased from 60Υ to 70Υ because
70 Υ was higher than the boiling point of methanol andmethanol should have been vaporized. Actually the volume ofthe solution in the stirring vessel was significantly reduced.
Thus the methanol concentration could not be kept enough highto make Y lower.

Experimental

2.1

Feed oil
The compositions of the jatropha oil are summarized inTable 2.1[4]. Oleic acid is the major component in this oil andtriolein was selected as a model feed oil in this study.

0.25

2.2

Transesterification
Figure 2.1 and Table 2.2 show the experimental apparatus
and conditions in the transesterification. The stirring vessel was
employed as a reactor. The specified amounts of triolein,
methanol and H2SO4 were put in the vessel and the time to start
the agitation was defined as the starting time of the reaction.
Samples of 0.9ml were taken every 1 hour and then mixed with
1ml of distilled water to stop the reaction. Then the solution
was separated into the biodiesel and water phases. TG, DG, MG
and FAME contents in the oil phase were determined by using
a liquid chromatography. The experimental parameters of theoperating temperature, methanol/feed molar ratio, andcatalyst/feed mass ratio were changed to study thetransesterification reaction in this system.
Biodiesel yield Y was defined by Eq.(1).
Yi

T [Υ]

YFAME [-]

0.2

40

60

0.15

70
0.1

0.05
0
㻜

㻞

㻠
t [h]

㻢

㻤

㻝㻜

Figure 3.1 Effect of temperature on Yield
3.1.2 Effect of methanol/feed molar ratio
Figure 3.2 shows the effect of nMeOH/nFeed on Y. Y attained
0.205 when nMeOH/nFeed was 30. Higher molar ratio helped
FAME to increase in this measurement. However, if too muchmethanol is used, the concentration of feed will be reduced,
which might lead to low reaction rate. Thus, a favorable valueof methanol/feed molar ratio must exist and the value should beMi(1)
M Feed21

of course affected by the other parameters.

0.25
nMeOH/nFeed [-]

0.25

0.2

12
nMeOH/nFeed [-]Y䠢䠝䠩䠡 [-]

12
0.15

24

0.15

Y䠠䠣[-]

0.2

24

30
0.1

30

0.1

0.05

0.05

0
㻜

㻞

㻠

㻢

㻤

㻝㻜

㻤

㻝㻜

t[h]

0
㻜

㻞

㻠
t[h]

㻢

㻤

㻝㻜

Figure 3.5 Yield of DG

Figure 3.2 Effect of nMeOH/nFeed on Yield

0.3

Y䠩䠣[-]

3.1.3 Effect of catalyst/feed mass ratio
Figure 3.3 shows the effect of Mcat./MFeed on FAME’s yield.
Almost no FAME was obtained when Mcat./MFeed was 0.01. By
increasing Mcat./MFeed from 0.01 to 0.03, the yield became muchlarger. However there was little difference in the FAME’s yield
when Mcat./MFeed changed from 0.03 to 0.05. Thus excess
amount of catalyst don’t lead to high yield but a lack of catalyst
resulted in a low yield of FAME.

0.25

nMeOH/nFeed [-]

0.2

12
24

0.15

30

0.1
0.05
0

㻜

0.25

Y䠢䠝䠩䠡 [-]

㻠

㻢
t[h]

Figure 3.6 Yield of MG

Mcat./MFeed [-]

0.2
0.15

0.01

4

0.03

FAME can be produced by acid catalyst in a slower reaction
rate than alkali catalyst. In the case of acid catalyst, reaction
temperature, methanol/oil molar ratio and catalyst/oil mass
ratio can affect the yield of FAME.
Higher Y can be obtained by using higher temperature,
however, a temperature higher than the boiling point ofmethanol will cause low Y of FAME due to the evaporation of
methanol. Higher molar ratio leads to higher Y effectively.
However, the concentration of feed will be reduced and mightlead to low reaction rate due to excess amount of methanol.
Catalyst mass ratio higher than 0.03 also leads to higher Y but
not very obviously.At last, it can be observed that nMeOH/nFeed
could have a large influence on the conversion from MG toglycerol.
Experiments using jatropha oils will be needed to confirm
the result.

0.05
0.1
0.05
0
㻜

㻞

㻠

㻢

㻤

㻝㻜

t [h]

Figure 3.3 Effect of Mcat./MFeed on Yield
3.2
Yield variation of TG, DG, and MG
In the results mentioned above, the effects of nMeOH/nFeed
were large and the profiles of TG, DG and MG would be
discussed here. Figures 3.4~ 3.6 show the yield variation of TG,
DG, and MG when nMeOH/nFeed was changed. Y of TG is almost
same in all three cases. Y of TG and DG didn’t changed a lot
when nMeOH/nFeed was changed, and Y of DG is smaller than MG.
It should be noted that the nMeOH/nFeed could have a large
influence on the conversion from MG to glycerol and higher
nMeOH/nFeed should cause higher Y of FAME.

Conclusion

Nomenclature
M
n
N
P
t
T
Y

1

䠵䠰䠣[-]

㻞

=
=
=
=
=
=
=

0.9

<Subscript>

0.8

cat.
Feed
i
MeOH

nMeOH/nFeed [-]

0.7

=
=
=
=

Mass
Molar
Agitating speed
Purity of productTime
Temperature
Yield

[-]
[-]
[rpm]
[-]
[h]
[䉝]
[-]

Catalyst
Feed
Component i
Methanol

12
0.6

Reference24[1] Hayashi Tomoki, “Effect of Operating Conditions in Biodiesel Fuel
Production from Low-valued Feed”, Bachelor Thesis, Tokyo Instituteof Technology, 2008[2]Kotte Sagar Rao· Pradosh Prasad Chakrabarti· B. V. S. K. Rao· R. B.
N. Prasad, “Phospholipid Composition of Jatropha curcus Seed Lipids”,
J Am Oil Chem Soc, 86,197-200(2009)[3]May Ying Koh, Tinia Idaty Mohd. Ghazi*; “A review of biodiesel
production from Jatropha curcas L.oil”, Renewable and Sustainable
Engery Reviews, 12,2240-2251(2011)[4] Sinthupinyo Patima, “Comparative Study of Biodiesel Production
by Alkaline Transesterification from Low- valued Feed”, Master Thesis,
Tokyo Institute of Technology, 2007

30
0.5
㻜

㻞

㻠

㻢

㻤

㻝㻜

t[h]

Figure 3.4 Yield of TG
With these considerations the experimental conditions were
fixed as T=60Υ, nMeOH/nFeed=30 and Mcat./MFeed=0.05 to expect
high Y of FAME and Y of 0.385 was obtained. It can be
speculated that changing the reaction conditions, such asincreasing the pressure, reaction time, agitating speed, a higher
yield could be obtained.

22

Crushing sound analysis of snail fossils under compressive loading using audible frequency
measurement
Student Number: 08-08073 Name: Shuhei KITAKATA Supervisor: Thirapong PIPATPONGSA
undertaken by Electricity Generating Authority ofThailand (EGAT). The preserved area of snail fossil
with layers of up to 12 meters deep in the mining area
has been set aside; however, there is a growing
concern about influence of mining activities in thevicinity area when excavating to a deeper depth.
Therefore, the detailed studies of characteristics ofsnail fossil are really required in order to preserve thisgeological heritage while developing the energy
resources.

1

Introduction
Many failure mechanisms in brittle materials can
produce sound waves due to the crack development
and particle crushing. Acoustic emission (AE) has
emerged as in-situ techniques to evaluate rock slope
stability; however, successful results to predict slope
failure are quite few partially because an installednumber of AE sensors are not sufficient in themonitoring area. In order to increase number of
installment by cost reduction, feasible study on analternative method using audible frequency
measurement is introduced as part of warning system.
Normally, the predominant frequency of soundwaves in soil is ranged between 0-40kHz while anaudible range is 30Hz-20kHz with best at frequencies
between 1-5 kHz for ear’s sensitivity. In acoustics,
decibel is commonly used to define sound pressurelevel to a reference value 20 μPa which represents 0
dB for the quietest sound of human hearing thresholdin air at 1 kHz. Sound loudness in air can be measured
using a microphone and in water using hydrophone.
This research aims to carry out the crushing soundanalysis of crushable materials under compressive
loading using commercial microphone. Practicalapplications are planned to carry out at an overseas
coal mine. There is a perfect site of coal mine in thenorth of Thailand where the biggest lignite in theSoutheast Asia is deposited. Moreover, a huge bulk ofsnail fossils were found, therefore the conflict andconstraint between energy resource development andgeological heritage protection has been arisen. It is
expected that a preliminary study of this techniquecould be useful for alarming damage to fossils.

3

Experiments on snail fossil
The site investigation and experiments for thisstudy was carried out at Mae Moh mine during
September 13-28, 2011. The disturbed sample of thesnail fossil, where located at latitude 18o19c07cc N andlongitude 99o41c86cc E, was taken from a subsurface ofa location deposited outside the preservation area.
Full body of snail fossils were collected andwashed by water. Thirteen sets of oedometer test wereconducted on air-dried samples loosely arranged incircular container with diameter 61.8 mm. A wholeexperimental program with details is given in Table 1
Table 1 Experimental program
Loading/unloading steps (kPa)
33,98,196,391,815,1630
33,98,196,391→196,98,33
33,98,196,391,815,1630
33,98,196,391,815,1630,3260→1630,815,391
4♪
,196,98,33
33,98,196,391,815,1630,3260→1630,815,391
5,196,98,33
6♪
33
7♪
33,98
8♪
33,98,196
9♪
33,98,196,391
10♪ 33,98,196,391,815
11♪ 33,98,196,391,815,1630
12♪ 33,98,196,391,815,1630,3260
13
33,98,196,391,815,1630,3260→391,33
Note: ♪ = with crushing sound recording
№
1
2
3♪

20-25mmFig. 1 Deposit of snail fossils at Mae Moh open-pit
lignite mine in Northern part of Thailand

10-15mm2
Site investigation
The large deposit of snail fossil aging around 12-13
million years has been discovered at Mae Moh
open-pit lignite mine in Northern part of Thailand as
shown in Figure 1. This snail deposit dominantly
composes of freshwater mud snails, named Bellamya,
and rare tiny spire snails, Bithynia. Lignite mining at
this largest coal mine in the Southeast Asian region is10-15mmFig. 2 Specimen of snail fossil in oedometer tests

23

6
Sample 1

Oedometer

5

Audio interface

Sample 2

microphone

Void Ratio

Sample 3

notebook PC

stethoscope

4

Sample 4
Sample 53

Sample 6
Sample 72

Sample 8
Sample 91sound recording software

microphone
connection

Sample 10
Sample 110

Sample 12

10

100
1000
Presser (kPa)

Fig. 3 Sound recording apparatus

10000

Sample 13

Fig. 6 Consolidation characteristics
As shown Fig.2, oedometer tests were run whilecrushing sound of fossils was recorded with apparatus
shown in Fig.3. Stethoscope used in this study(NURSE SCOPE) with tube I4.4mm×570mm helps
transmit the sound to a microphone. Link between
tube and microphone is tightly connected to prevent
pressure leak. Microphone used in this study (AT9903
Audio-technica) with size I6.4mm×18mm can detectsound frequency between 30Hz-18kHz withsensitivity to sound pressure level -42dB. Four
recording channels are linked to audio interface(OCTA-CAPTURE (Roland)), monitored andcontrolled by sound recording software (Cakewalk
Production Plus Pack) thru a notebook computer.

100

Percent finer by weight

90
80
70

33kPa
98kPa
196kPa391kPa815kPa1630kPa
3260kPa

60
50
40
30
20
10
0

0.01

0.1

1
Grain Size(mm)

10

100

Fig. 7 Grain size distribution
160
140

Cumulative Count

4
Data analysis
Sound wave data in time space were filtered byhigh-pass filter (HPF) at 3kHz to remove noise as
shown Fig. 4. Sound wave pulses with amplitude
greater than the defined lowest level were counted
over a specific period of time as shown Fig. 5 wherecrushing sounds were detected as triangular shape
Volume change characteristic in terms of void ratio
versus applied vertical pressure is shown in Fig.6.
Sieve analysis is conducted with result given in Fig.7.

sample 12
sample 6
sample 7
sample 8
sample 9
sample 10
sample 3
sample 11
sample 4

120
100
80
60
40
20
0
-0.2

0

0.2

0.4

0.6

0.8

1

Cumulative Settlement (cm)Fig. 8 Cumulative settlement vs cumulative
counts of crushing soundRecordedsoundwave data

5

ResultAs shown in Figs.6-7, a large volume change isobserved for pressure between 196-391kPa. Also, thisrange of crushing pressure is observable by rapidchange of cumulative counts of crushing sound.
Moreover, the relationship between the cumulative
counts and volume change in terms of cumulative
settlement can be confirmed as analyzed in Fig.8.

Noiseremoval by
high-passfilter 䠄3kHz䠅

Real wave

Spectrogram

6 Conclusion
This study confirmed that audible frequency
measurement of crushing sound of snail fossils can
indicate the similar characteristics as observed inoedometer test; therefore, it could be developed to awarning system using microphone installed to a site.

Fig. 4 Sound signal processing (3 second fromloading step 3260 kPa in oedoemeter test no.12)
1
Lowest level

2

3 4
5

6

7

8

7

References>@ᯇᒸ ඖ㸸ᅵ㉁ຊᏛ᳃໭ฟ∧>@ᆅ┙ᕤᏛ఍ࠕᅵ㉁ヨ㦂ࡢ᪉ἲ࡜ゎㄝࠖ
>@኱ὠ ᨻᗣ㸸࢔ࢥ࣮ࢫࢸ࢕ࢵࢡ࣭࢚࣑ࢵࢩࣙࣥࡢ≉ᛶ࡜⌮ㄽ㸦➨㸰∧㸧᳃໭ฟ∧

Shape

Fig. 5 Wave pulse of crushing sound24Fast Visual Object Categorization by Clusteringwith Hamming DistanceStudents Number: 08 08392 Name: Joowon Kim Supervisor: Yukihiko Yamasita

I

Introductionand evaluation stage of bag-of-keypoints algorithm. In
the learning stage, we build a three layer hierarchicalmodel of the training images. The ﬁrst layer consistsof the original data. The second layer consists of therepresentative images extracted from each category byusing k-means clustering method and the representative
images are clustered again. The third layer consists of
the representative images of such clusters. Also we binarize the bag-of-keypoints of the representative images.
A bitwise operation is used for comparisons and calculations. Typically, bitwise operations are substantially fasterthan division, several times faster than multiplication, andsometimes signiﬁcantly faster than addition. By using
Hamming distance, the speed of retrieving should be
improved. We evaluate the test images based on clusteredtraining images.

In recent years, the digital imaging revolution in theWorld Wide Web facilitates the generation and storageof large collections of images. These images of various
scenes include an important information. This exponential
growth of the digital image data has created a compellingneed for an innovative method for retrieving, recognizing and visualizing images from large collections to use
training and education, artiﬁcial intelligence, and robotics
ﬁelds.
Recent advances in computer vision and pattern recognition techniques have given rise to extract such robust andinvariant features from images. Usually a large number,perhaps hundreds or thousands, of features are obtained
from each image. A vector descriptor, such as the scale
invariant feature transform (SIFT) is associated with eachfeatures, computed from the intensity pattern [1]. The
calculated descriptors are vector quantized by k-meansclustering and an image is represented by a bag of these
keypoints so that images are searchable in a similar way
to bag-of-words in text retrieval.
The idea of bag-of-keypoints has already been applied
to generic visual categorization with promising results [2].
In the bag-of-keypoints model, each keypoint is considered
independent of all the other keypoints besides the lossof all ordering structure. However, this method does notwork well in some cases. In the cases, there exist strongly
correlated keypoints in individual images as well as ina collection as a whole. Therefore, it is necessary to
exploit the correlation or similarity patterns among the
keypoints to improve the retrieval effectiveness. Also,
there are a lot of objects in the world and we have morethan 100 thousands categories. It requires an intensive
learning/training stage for parametric image categorization(e.g. Naive Beyes, SVM classiﬁer). However, it takes toomuch time to classify using supervised learning methods.
To overcome the limitation, this paper presents a fastvisual image categorization method with a new bag-ofkeypoints. In this method, Hamming distance is applied
to clustering of training data.

II

Fig. 1.

The proposed method.

The learning algorithm is as follows:
1) Detecting and making descriptors of covariant regions or interest points.
2) Generating visual words by applying vector quantization or clustering based on the descriptors in the
training set.
3) Assigning the descriptors to a set of predeterminedclusters or visual words in the codebook.
4) Constructing a resulting histogram on individual
image, which counts the number discriptors assigned
to each cluster and treating the histogram as thefeature vector.
5) Clustering the images in each category and extracting some images as the representatives of the eachcategory.

Overview of Proposed Method

The proposed method uses k-means clustering algorithm
for building hierarchical training database. The effectiveness of evaluating all images has been considerably low
if there are a lot of categories and photos [3]. In general,
each category has a similar pattern in bag-of-keypoints sothat it is not necessary to evaluate all the training images
at the beginning. In this paper, I modify the learning

25

TABLE I
R ESULT
Train

15

30

cluster
300
400
500
1000
2000
300
400
500
1000
2000

Recog.
Rate
17.11%
17.72%
18.07%
18.51%
19.56%
17.46%
22.46%
22.72%
23.16%
22.37%

Time(ms)
2711.486133.847362.2113527.4
24288.3
5456.8812126.614840.926844.1
48250.3

Bag of Keypoints Method
Good
Recog.
Category
Rate
Soccerball86.7%
StopSign
80.0%
windsorchair
73.3%
DollarBill
66.6%
Motorbikes
60.0%
SoccerBall
93.3%
Faces
86.7%
WindsorChair
80.0%
DollarBill
73.3%
Motorbikes
73.3%

Bad
Categories
16
14
10
8
6
11
19
10
8
3

Recog.
Rate
0.0%
6.7%
13.3%
20.0%
26.7%
0.0%
6.7%
13.3%
20.0%
26.7%

Time(ms)
1073.06
1085.23
1888.773545.694359.682292.712767.04
3332.765416.39861.84

Proposed Bag of Keypoints Method
Good
Recog.
Bad
Category
Rate
Categories
DollarBill
86.7%
19
SoccerBall
80.0%
11
WindsorChair
73.3%
8
StopSign
66.7%
10Motorbikes
60.0%
4
Motorbikes
86.7%
16
euphonium
80.0%
9SocceerBall
80.0%
10
StopSign
80.0%
4
WindsorChair
73.3%
4

Recog.
Rate
0.0%
6.7%
13.3%
20.0%
26.7%
0.0%
6.7%
13.3%
20.0%
26.7%

by performing proposed method with Hamming distanceand the original method to show the actual improvement in
retrieval time of the proposed methods. We obtained morethan 5 times faster retrieval speed compare to the original
method and also its recognition rates is better than thoseof the original method. Those results show that it is notnecessary to evaluate all images. We can speed up the
retrieval process by evaluating between query image andselected images. By analyzing Table 1, we can observethat the performance of the proposed method is muchbetter when there are a lot of training images. The better
performances are expected as keypoints are more localizedin nature and invariant to viewpoint and illumination
changes. We can also observe that the category suchas soccer ball or stop sign that do not have irrelevant
keypoints performed better than other categories that have
many irrelevant keypoints. Overall, the improved result
indicates that the retrieval speed by using the proposed
method is not negligible and the proposed method can be
exploited effectively in large databases.

6) Transforming the histogram of each representatives
resulting image to a binary vector (visual words not
0 is to 1) in the second layer.
7) Building hierarchical database by re-clustering therepresentative images.
The evaluation algorithm is as follows:
1) Evaluating the similarities between the query andrepresentative images in the third layer. A binary
vector is utilized with Hamming distance and thethree best clusters are chosen. Next, the similarities
between the query and representative images in thethree best clusters are evaluated at the second layer
and the best 3 clusters are chosen again.
2) Evaluating the similarities between the query andimages in the three best clusters. Not a binary vector
but a histogram is utilized in an Euclidean distancemeasure function.

III

Recog.
Rate
18.16%
19.21%
22.37%
19.30%
19.65%
24.56%
25.26%
26.67%
23.95%
22.63%

Experimental Results

IV Conclusions

The image collection for experiment comprises of 8677
images of 101 manually assigned global categories. It is alarger collection to used for object categorization or image
retrieval task in Caltech 101 [4]. To build the visual words
based on the k-means clustering, a training set of images
is selected before the learning process. The training set
used for this purpose consists of 15 and 30 images foreach category. To compare the recognition rates between
different numbers of visual words, the descriptors are
clustered to generate visual words of ﬁve different sizes
as 300, 400 500, 1000, 2000 units. After the construction
process, all the images in the collection are encoded
and represented as bag-of-keypoints. For a quantitativeevaluation of the categorization results, we selected 15
images for each category in the collection as query images
and used the nearest neighbor method for classiﬁcation.
A retrieval is correct if it belongs to the same category
as the query image out of the 101 categories as shownin recognition rate. The retrieval time is used as the basic
evaluation measure of retrieval performances. The average
is calculated over all the queries to generate the table inﬁve different settings.
Table 1 shows the recognition rate on ﬁve different
codebook sizes. It is clear from Table 1 that the best
recognition rates are different depending on the numbersof visual words and clusters. The recognition rates in 30
training images per category are degraded when the sizes
are decreased or further increased. Also, Table 1 shows theretrieval speed of the keypoints-based image representation

We have investigated fast visual object categorization
approach based the bag-of-keypoints that is inspired by
the ideas of the text retrieval. In this approach, an interest
point is detected and described by the SIFT descriptor at
ﬁrst. Based on the construction of visual words, images are
represented as a vector of keypoints. The proposed method
evaluates the similarities/correlations between images by
the histograms and classiﬁes by the nearest neighbormethod using Hamming distance. Experimental results
showed that the proposed method improved retrieval
speed. However, the precision of the retrieval results is notenough. My method cannot improve the precision compare
to Naive Beyes or SVM clasiﬁer.
As future works, we plan to study on a better clustering
method for training data to speed up the retrieval time. We
need to investigate how to remove irrelevant keypoints intraining data.

References[1] D.Lowe, Distinctive image features from scale-invariant keypoints.,
IJCV, 60(2), 2004.
[2] G. Csurka, C. Bray, C. Dance, and L. Fan, Visual categorization
with bags of keypoints, ECCV Workshop on Statistical Learning in
Computer Vision, pp. 1-22, 2004.
[3] Oren Boiman, Eli Schechtman, and Michal Irani, In defense ofnearest-neighbor based image classiﬁcation, CVPR08, 2008.
[4] L. Fei-Fei, R. Fergus, and P. Perona, Learning generative visualmodels from few training examples: an incremental Bayesian approach tested on 101 object categories., CVPR 2004, Workshop on
Generative-Model Based Vision, 2004.

26

A Study on Renewable Energy Funds Initiated by NPOs in Japan
Student Number: 08_08676
Name: Keisuke KUSUNOKI Supervisor: Naoya ABE
NPO ୺ᑟ࡟ࡼࡿ෌⏕ྍ⬟࢚ࢿࣝࢠ࣮ࣇ࢓ࣥࢻ࡟㛵ࡍࡿ◊✲ᴋ ᜨ㍜
the subjects into some groups based on the similarityof their characteristics. Hearing surveys to two NPOs
which are involved and maintain renewable energy
plants built by the funds supplemented the analysis.

1. Introduction
In recent years, importance of renewable energy has
been increasing. Effects of introduction of renewableenergy, such as the creation of employment
opportunities, new markets and diversification ofenergy sources, can be expected. [1] Utilizing theseeffects, there are some activities trying to contributeto the community using renewable energy by NPOs.
These NPOs have built some community wind-mills or
installed solar panels with citizens’ investment(renewable energy citizens’ fund) and subsidy fromthe government. The method of citizens’ fund has
advantage of environmental contribution of thecitizens, however, it has disadvantage for NPO ofmuch labor cost or risk of fund shortage.

䢠100 million

䠂

80

16

70

14

60

12

50

10
8

40

6

30

4

20

2

10
0

0
4

14

6

9

8

7

5

3

subsidy
citizens investment

1

10 15

2

12 13 11 16

other money% of citizens investment

Figure.1 Composition of funds

2. Objective

Fig.1 shows the proportion of the investors ofeach fund. The majority of these funds are made up of
citizens’ investment and subsidies from local and/or
national government. For example, total amount ofFund #1 is about 16 million Yen, consisting of about
54.4 % from citizens’ investment, 45% from subsidy,
and 0.6 % from other sources.

The objective of this study is goal is to investigate therelationship between the characteristics of renewableenergy funds and the characteristics of the power
plants built by the funds, focusing on the variables
such as the amount of the citizens’ investment andoutput capacity and amount (kW, kWh) of each plant.

3. Data & Analysis

4. Results

This study focused on the renewable energy funds
owned by operated by NPOs or local government inJapan, which are for wind power, solar (photovoltaic)
power, and small hydro power generation projects.
Based on various materials, 19 funds were identified inJapan as of February, 2012. The data and its sourcesof this study are shown in Table 1.

4.1 Result of analysis(a) As the result of cluster analysis, it can be divided
funds into 6 groups. In Fig.1, horizontal axis andvertical axis means investment yield and the amountmoney to be raised. Group 1 and Group 2 are specialgroups because one or another value is an outlier. In
Group 3~6, each group are different in investment
yield, the amount money to be raised or amount ofinvestment unit. It seems that each fund have beendevised for collecting money.

Table 1 . Data sources
Data category
Funds’ data: the number of investor;
the amount of money to be raised;
Amount of investment unit; total
amount of investment; investment
yield
Power plants’ data: total operating
expenses; rated output; electricity
generation in a year
Details of two NPOs

Data sources
Web page of relatedorganizations,
relevant literature,
inquiry by e-mail or
FAX to relatedorganizations(b) Fig.3 shows the result of PCA. Vectors are
eigenvectors of each variable and numbers meaneach fund and power plant. The value of cumulative
proportion up to second principal component is 75.3,
so three-quarter of the entire information has been
aggregated into this chart. The meanings of theprincipal components are inferred from vectors of

Hearing survey

Two analytical methods were adopted to clarify thegoal of this study. Cluster analysis and Principal
Component Analysis (PCA) are adequate for dividing

27

Table 2. Comparison of the rankings of output
capacity basis and actually generated amount basis
# A B
C
D E
F
G H
I J
3 w 1500 2543 19 46.9 3.95 3 669 2 1
4 w 1980 3576 21 18.5 3.70 4 668 3 1
6 p 300 379 14 36.2 1.67 6 211 5 1
1 w 7000 13300 22 54.4 4.43 1 842 1 0
7 p 732 864 13 41.2 1.43 7 169 7 0
8h20
49 28 39.2 0.46 8 112 8 0
9h1956 34 37.9 0.30 9
90 9 05 p 248 293 13 45.2 1.77 5 209 6 -1
2 w 1980 2230 13 56.0 3.96 2 446 4 -2

each variable. First principal component (horizontalaxis) means “power generation potential” and second
principal component (vertical axis) means “the depthof the relationship of citizens to the fund” of power
plants. These indexes become larger and larger in thenegative direction of the axes.
100million
yen
8.000

1

wind
PV

6.000

A: type (w: wind, p: photovoltaic, h: small hydro)
B: rated output [KW] C: electricity generation in 2010 [MWh]D: operation ratio [%] G: ranking of F
E: citizens’ investment per total operating expenses [%]F: B*D per citizens’ investment [KW/㻃100,000]
H: C*D per citizens’ investment [kWh/㻃100,000]
I: ranking of H
J: F-H

hydro

3

4.000

5

2.000

interest
yield
%

2

4

4.2 Result of hearing survey

0.000
0

1

2

3

4

56

6

7

8

Two NPOs’ motivation to utilize the scheme of citizens’
fund is based on the fact that they cannot get loan
finance from financial institutions in common. Then,
there is also an advice from the NPO that had built
first windmill with a citizens’ fund and eventually they
had chosen the scheme of citizens’ fund. One of the
two NPOs is willing continue to utilize this fundscheme for advantage that it can directly involve
citizens in activities of diffusion of renewable energy.
However, the other NPO indicated reluctance to use
the fund scheme again due to the disadvantages; itcosts much labor or does not meet the characteristics
of a renewable energy project in their views.

Figure 2. Result of cluster analysis

5. Conclusion

The depth of therelationship ofcitizens to the fundAccording to the results of the analysis above and thehearing survey, there are some relationships of tradeoff. In fund scheme, relationship between “participation of the citizens” and “labor or cost” is tradeoff. The relationship between “the depth of therelationship of citizens to the fund” and “the amountof generation per investment” is also trade-off. As afurther study, it is considered that more various funds
will appear in the future, so the study should increasesamples and analyze them again. The investigation ofrenewable energy funds owned by NPOs in foreigncountries would be also important.

Power generation potential

Figure 3. Result of PCA(c) Finding the remainder between ranking of rated
output per citizens’ investment and ranking ofelectricity generation per citizens’ investment, it
showed “J” in Table.2. These values mean that, if “J”>0,
power plants at the base of electricity are superior toat the base of rated output. In other words, when
judging which power plants generate more electricity,
there are differences between judging rated output
and actual electricity generation. Operation ratios ofsamples which ranking have fallen are lower than thatof samples of same type.

References[1]Ministry of the Environment, 2010, Basic
Energy Plan

28

A study on the perceptions of drinking water quality and risk in Shenyang, China
Student Number: 08B09368 Name: Lu GAO Supervisor: Naoya ABE୰ᅜ℘㝧ᕷ࡟࠾ࡅࡿỈ㐨Ỉ฼⏝⪅ࡢỈ㉁࡜ࣜࢫࢡ࡟ᑐࡍࡿㄆ㆑
ࢥ࢘ ࣟ
1 Introduction
associated with drinking tap water at home,
In China, the average water resource capacity
41% of the respondents indicated that there are
per person is only 1/4 of the world and 65% ofindeed the risks, 40% respondents replied theygroundwater and 50% of surface water isdidn’t know, and just 18% respondents disagree
polluted respectively.
with the statement (see fig1.) Those two results
In the beginning of 1980s, the bottled
imply that more people concern about the risk
water entered China market and theof tap water than the quality of tap water in the
consumption of bottled water is increasingcity.
every year. At present, the consumption ofbottled water in China ranks third in the worldstrongly
100%
and it is increasing at 30% per year. This trend
4%
8%
agree
12%
may represent the change of people’s
10%
80%
agree
perception about the quality of drinking waterfrom the tap.
40%
60%
53%

2 Objective
The objective of this study is to understand theperception of tap water quality and risk in
Shenyang, China. A previous study (Doria,
2005) conducted a study about the tap waterrisk and quality perception in Portugal .He
found the factors such as flavour which can
impact the perception.

40%
20%
0%

16%

24%

14%

17%

Quality

Risk

don’t
know
disagreestrongly
disagreeFig 1 Perception of tap water quality and risk

3 Data and Analysis
An interview survey was performed in
Shenyang, China, from January 11st to
February 1, 2012. Shenyang is one of the 10
largest cities (like Beijing and Shanghai) in
China and the water resource capacity perperson is the lowest in those cities. In the past,as the biggest heavy industrial city inNortheast
China,
therearesevereenvironmental problems such as watercontamination of rivers in Shenyang. At
present, although the water quality becomes
better than before, some citizen of Shenyang
still worry about it.
In this study, 349 questionnaires sheets
were collected and the effective responses are
270 (effective answer rate: 77.4 %.) The sample
is composed of 41.5% (112) male and 58.5%(158) female and the mean age is 44.5 years old(youngest: 18, eldest to 73 years.)
One of the questions was about the
respondents’ opinions about the quality of tap
water, which is about the taste, colour, odour of
tap water. The survey found that 30% of the
respondents replied they don’t satisfy with tap
water quality, 53% replied that they do not
know, and 13% replied that they satisfied withtap water. As for the question of whether
respondents think there are health risks

As for the analytical method, theStructural
Equation
Modeling(a.k.a.
Covariance Structure Analysis) was adopted.
This approach is used to analysis structural
relation between variables, some of which may
be latent. A simple example is shown in Fig2,
which is about perceptional structure of people
ability of reading and writing based on thelevels of intellectual skills and sense of humor.
In this figure, in part 1, “Intelligence” is alatent variable that is not directly measured
but rather assessed indirectly using “Reading”
and “Writing”, two measured variable and thispart use the factor analysis which can show
some measured variable by common factor.
An equation for part 1 was defined asfollow.
Reading=aIntelligence+e2
Writing=bIntelligence+e1“a” and “b” are factor loadings, whichshow the relationship between Intelligence andReading (Writing). e1 and e2 are error terms.
In part 2, “sense of humor” is a result of“Intelligence”. An equation for part 2 wasdefined as follow.
29

Portugal and its fitness indexes are:non-normed fit index (NNFI) =0.964, thecomparative fit (CFI) =0.963, the root meansquare error of approximation (RMSEA) =0.046.
The result of applying the same model
framework to the Shenyang’ data provided thefit indexes such as NNFI=-0.045, CFI=0.751,
RMSEA=0.236 respectively. It means the
Chinese data is not explained well by thismodel.
For this reason, a Modified model wasconstructed (Fig4). Two variables which are“impurity” and “pump” were added in thismodel. Results show that, both perceived tap
water quality and risk are largely influenced byflavour. Those fit indexes are NNFI=0.868,
CFI=0.952,
RMSEA=0.094.
This
model
becomes better than before.

Sense of humor=cIntelligence+e3“c” is the path coefficients which show thecausal relationship between “Intelligence” and“sense of humor”. If the c is big, it means “senseof humor” is large impact by “Intelligence”. And
the one arrows stand for regression weights
and the two arrows stand for correlations
among predictors.

Fig 2 example model
4 Results
The structural equation model that integrates
the main variable of this study is presented on
Figure 3. Results show that, in Lisbon,
Portugal, perceived tap water quality is largely
influenced by “flavour”, but in Shenyang, China,“risk” has a very important role for perceptionof tap water quality. On the other hand, in
Lisbon, Portugal, perceived tap water risk islargely influenced by “colour”, but in Shenyang,
China, “flavour” has a very important role forperception of tap water risk.

Fig4 Modification model

Fig.3 A structural equation model

5 Conclusion
In Portugal, according to Doris (2005), people
take variable “flavour” as the medium toperceive the tap water quality. But in China,
people consider a variable “risk”, which is moregeneral notion, as the medium to perceive thetap water quality. It may mean Chinese people
pay more attention to the safety of tap water.
In the modification model which is fit
Chinese data, “Vision” is used as the medium toperceive the tap water quality and risk. It may
mean Chinese people used the factor which canbe visible to perceive the quality and risk of tap
water. If the vision was being improved, maybe
the perception of tap water quality and risk will
become better.
References(Portugal and China)

Doria, Pidgeon,Hunter, 2005 䠈 Perception of
tap water risks and quality: a structural

This model is referred from the previous
study (Doria, 2005), which investigated theperceptional model about drinking water inequation

model

approach,

Water&Technology, Vol 52, No8, PP143-149

30

ScienceDevelopment of time domain channel sounding system of BAN
and evaluation of SIMO diversity
Student Number: 08B10012
1

Name: Sho Kobayashi

Receivertransmitter

Introduction

Triggersignalgenerator

Wireless body area network(BAN) has seen very rapid
development in recent years. BAN involves communication between communicating nodes located in, on, or
around the human body.In the future Wireless BAN systems are expected to ﬁnd wide application in medical care,sports,entertainment, or many other ﬁelds[4]. BAN Channels are those surrounding the human body, so the affect ofhuman body and human body motion must be consideredwhile studying BAN channels.
In the IEEE 802.15.6 standard different frequency
bands are allocated for WBAN applications.Ultra-wideband (UWB)technology is also considered in this standard
for usage in WBAN. It constitute communication over very
wide bandwidth, enabling high data rate transmissions atvery low power. In UWB transmission the signals are usually of very short duration, like a pulse, and we can easily
differentiate the signal from its delayed versions. HenceUWB communication has the advantage of being very resilient to multi path fading.
While considering human body motion each body parts
moves differently and because of this,to understand the
WBAN channel we need to make dynamic multi portchannel measurements as opposed to static measurements.
To study the UWB WBAN channel,a UWB measurement
system was developed and veriﬁed to be working properly. Then we conducted an experiment to make on-body
WBAN SIMO channel measurement using one transmitting and two receiving antennas. We assumed that the antenna diversity can be evaluated by measuring the correlation between each channel.

2Supervisor: Jun-ichi Takada, Minseok Kim

PG

BPF

LNA

BPF

Oscilloscope

Synchronization

Figure 1: Time domain method

3

Data process

The received signals were observed using the digital sampling oscilloscope. The oscilloscope was controlled using
PC. Because of the very low transmitted power, SNR (signal to noise rate)at the receiver is also very low. To beable to ﬁnd the pulse correctly, SNR has to be improveby averaging[2]. If averaging is done N times, SNR is improved by 10 log10 N dB.
Receiving signal includes the channel transfer function
and the system response of the measurement system. We
have to therefore remove the system response from the received signal to get the channel transfer function. In thissystem, the power of PG couldn’t not be changed, so we removed it after measurement. Received signal can be mathematically represented as
Y (f ) = X(f )Gt (f )H(f )Gr (f )(1)

X(f ), Y (f ) are spectra of the transmitted and received signals, and Gt (f ), Gr (f ) system response of transmitter andreceiver. H(f ) is transfer function of channel including
antennas. On the other hand, when transmitter and receiverare connected through the attenuatorHatt (t) as the calibration measurement, the received signal is

Configuration of the measurement system

Y  (f ) = X(f )Gt (f )Hatt (f )Gr (f )UWB signal measurements, are always done either intime domain or frequency domain[1]. In the case of dynamic channel such as BAN, the signal is always changing and therefore the time domain method of measurement which uses digital sampling oscilloscope with highsampling rate is better because of short measurement timecompared to the frequency domain method. The conﬁguration of the measurement system is shown in Figure 1. The
transmitter is constructed with a pulse generator (PG) and aband pass ﬁlter (BPF). For UWB, the 3.4-4.8GHz and 7.2510.25GHz bands are allowed to be used in Japan. The receiver is composed of a BPF, a Low noise ampliﬁer(LNA),
and an oscilloscope. Due to very low transmitted signal
power, we had to amplify the pulse at the receiver to beable to view the signal. The PG and the Oscilloscope weresynchronized using trigger signal from signal generator so
as to be able to observe the delay time of pulse.

(2)

we can then get channel transfer function by following.
H(f ) =Y (f )Y (f )Hatt (f )=X(f )Gt (f )Gr (f )Y  (f )(3)

Channel impulse responseh(t) can be gotten from channeltransfer function by inverse Fourier transform.
 ∞1
H(f )ej2πf t dt
(4)h(t) =
2π −∞4Verification

4.1

Direct connection

The receiver has 4 port in this system. To conﬁrm the same
signal can be received at each port, we compare the signal
by connecting the transmitter and receiver directly through

31

0.03

0.03

0.02

0.02

0.01

0.01

−35

0
−0.01
−0.02

0
−0.01
−0.02

−0.03
0

5

10
Time [ns]

15

20

−0.03
0

Figure 2: receiver 1
4−45
Path Gain[dB]

Voltage [V]Voltage [V]

−40

x 10

5

10
Time [ns]

15

−60

−3

4

stillch1
stillch2
walkch1
walkch2
updownch1
updownch2−55

20

Figure 3: receiver 2

−3

−50

−65
0

x 10

2

4

6

8

10

Time [s]2

Figure 7: Path gain(dx =15cm,dy =10cm)

voltage[V]voltage[V]

2

0

0

−2

−4
0

Table 2: Correlation coefﬁcient(dy =10cm)

−2

5

10
Time [ns]

15

−4
0

20

5

Figure 4: Oscilloscope

10
Time [ns]15

20

Figure 5: VNA
Receiver

Cable(3m)

R2

dx

Tx

25Gsample/s
2portsPC

still
0.90580.84070.69820.60330.8051

Triggerdx =1cmdx =5cmdx =10cm
dx =15cm
dx =20cm

33120a(50Hz)
Synchronization
Cable(3m)

Trigger
MG3641A(50MHz)

Transmitter

Figure 6: Caption for ﬁgures at the bottom
Table 1: parameter
Triggering Signal Frequency
500Hz
sampling frequency
25GHz
Measurement timeabout 8.6sec
dx , dy(1,5,10,15,20cm),(10,18cm)

byR(x, y) = nupdown0.89660.08580.89790.58180.5354ni=1 (xi − x)(yi − y)n2i=1 (yi − y)(5)where xi and yi represent measurement data, and x and ydenote average of it[4]. Table 2 and 3 shows the results.
Though for higher antenna diversity low correlation between the channels are preferred, from the results we observe high correlation even when the distance between
the antennas are long.We therefore need to consider othermethods such as polarization diversity in front of body.

Free space

Next, I measured the signal in free space and compare thedata from VNA which can measure channel transfer function. Figure4 and 5 show each channel impulse response
for a distance of 30cm. Almost same voltage level can beobserved.

5walk
0.77120.55480.7056
0.61290.65562i=1 (xi − x)

an attenuator. Figure 2 and 3 are the two calibrated signal
which were generated by T-branch at the same time at two
port. We conﬁrmed that the signals can be received at thesame time and their power are the same.
4.2

updown0.8536
0.93130.93660.85750.1724

Table 3: Correlation coefﬁcient(dy =18cm)

Oscilloscope
Cable(3m)walk
0.90800.91580.8856
0.89680.7388

Cable(1.5m)

Cable(1.5m)

R1

dy

still
0.94820.98250.97840.9846
0.9613dx =1cmdx =5cmdx =10cm
dx =15cm
dx =20cm

6

Conclusion

In this research, a UWB time domain channel sounding
system for BAN was developed and its accuracy evaluated.
Also, We measured the on-body channels and calculated
their correlation.

SIMO channel measurement

References

In the experiment, we attached the transmitting antennaon the navel(ﬁxed), and two receiving antennas on the center of chest. We then measured the signal while standing
still, walking,and standing up and sitting down. Next, wechanged the distance dx and dy , and repeated the measurements for the above motion scenarios.
Table 1 is the parameter of this experiment.
From the maxpeak of obtained data, we found out thepath gain about each path by subtracting the power. Figure
5 is the case for dx =1cm anddy =10cm.
The similar ﬂuctuation can be observed in channel 2. I
obtained correlation coefﬁcient of Path gain between channel 1 and channel 2 in each case. The correlation is deﬁned[1] Pascal Pagani, Friedman TchoffoTalom, Patrice Pajusco, andBernard Uguen, Ultra-Wideband Radio Propagation Channels, John
Wiley and Sons, Inc, Jan 2010[2] Huynh Thi Thanh Trieu, Junichi Takada, Katsuyuki Haneda, Kenichi Takizawa Validating the effectiveness of UWB transmission
simulation using a stored channel, papers of Technical Meetingon Instrumentation and Measurement, IEE Japan, IM-06-16-23 Jul
2006,[3] Julien Keignart, Chadi Abou-Rjeily, Christophe Delaveaud, and Norbert Daniele UWB SIMO Channel Measurements and Simulations, Microwave Theory and Techniques, IEEE Transactions, 54
Issue:4, pp.1812-1819, June 2006
[4] Norihiko Katayama, et.al Channel Model on Various Frequency
Bands for Wearable Body Area Network, IEICE Trans. Comm
Vol.E92-B No.2 pp.418-424, Feb. 200932

TiO2 ගゐ፹࡟ࡼࡿỈ୰ࡢࢸࢺࣛࢡ࢚ࣟࣟࢳࣞࣥ PCE ࡢගศゎᏛ⡠␒ྕ㸸08B10443 Ặྡ㸸ᕥ ⓡᣦᑟᩍဨ㸸᪥㔝ฟ ὒᩥ ࢧ࣒ࣜ ࢡࣜࢫ ⥴ゝ ᆅୗỈởᰁ≀㉁ࡢ୰࡛ࠊ᭷ᶵሷ⣲໬ྜ≀ࡣ௦⾲ⓗ࡞౛࡛࠶ࡿࠋ᭷ᶵሷ⣲໬ྜ≀ࡣࠊࢺࣜࢡ࢚ࣟࣟ
ࢳࣞࣥ(TCE)ࠊࢸࢺࣛࢡ࢚ࣟࣟࢳࣞࣥ(PCE)࡞࡝ࠊ
ሷ⣲ࢆྵࡴ᭷ᶵ໬ྜ≀ࡢ⥲⛠࡛ࠊ࡯࡜ࢇ࡝ࡀேᕤ
ⓗ࡟ྜᡂࡋࡓ໬ྜ≀࡛ࠊ୺࡟㎰⸆ࡸ⁐๣࡜ࡋ࡚฼
⏝ࡉࢀ࡚࠸ࡿࠋ⎔ቃ୰࡛㠀ᖖ࡟Ᏻᐃ࡛࠶ࡾࠊ⏕≀ࡢ⬡⫫ෆࡢ⵳✚ᛶࡀ㧗ࡃࠊ୍⯡࡟ẘᛶࡀᙉࡃࠊⓎ࢞ࣥᛶࡢ␲࠸ࡶ࠶ࡿ᭷ᐖ࡞ࡶࡢ࡛࠶ࡿࠋ⏕≀ⓗ࡟ศゎࡉࢀ࡟ࡃ࠸ࡓࡵࠊ୍ᗘởᰁࡉࢀࡿ⎔ቃࡀඖࡢ≧ែ࡟ᅇ᚟ࡍࡿ࡟ࡣ㛗᫬㛫ࡀᚲせ࡛࠶ࡿࠋࡑࡢୖࠊ
ప⃰ᗘࡢࡶࡢࡀ኱㔞࡟ᆅୗỈ୰࡟ᗈࡀࡗ࡚࠸ࡿ
ࡓࡵࠊᅇ཰ཬࡧ㝖ཤࡀ㠀ᖖ࡟ᅔ㞴࡛Ⳙ኱࡞ࢥࢫࢺ
࡜ປຊࡀᚲせ࡜࡞ࡗ࡚࠸ࡿࠋ ᆅୗỈởᰁゎỴ⟇࡜ࡋ࡚ࠊάᛶⅣ࡞࡝ࡢ྾╔ᮦࢆ⏝࠸࡚ࠊởᰁ≀㉁ࢆ㝖ཤࡍࡿ྾╔ἲࡸᅵ╔ᚤ⏕≀࡟㓟⣲ࡸᰤ㣴※ࢆ୚࠼ࠊࡑࡢᚤ⏕≀࡟ࡼࡾᆅୗ
Ỉࢆί໬ࡉࡏࡿᚤ⏕≀ί໬ཬࡧ㓟໬๣࡜Ỉࡢ཯ᛂ࡟ࡼࡾࣄࢻࣟ࢟ࢩࣝࣛࢪ࢝ࣝࢆከࡃⓎ⏕ࡉࡏࠊ㓟໬ศゎ཯ᛂࡢಁ㐍ࢆ┠ⓗ࡜ࡋࡓಁ㐍㓟໬ἲ࡞࡝ࡢ᪉ἲࡀ࠶ࡿࠋάᛶⅣ࡟ࡼࡿ྾╔ἲࡣ▷࠸ฎ⌮᫬㛫ࡀ࠿࠿ࡿࡀࠊ౑⏝ࡉࢀࡓάᛶⅣࡢ஧ḟฎ⌮ࡀ⪃࠼࡞ࡅࢀࡤ࡞ࡽ࡞࠸ࠋᚤ⏕≀ί໬ἲࡣ᭱ึ㈝⏝ࡀప࠸ࡀࠊ㛗࠸ฎ⌮᫬㛫ࡀ࠿࠿ࡿࠋಁ㐍㓟໬ἲࡣ▷࠸ฎ⌮᫬㛫ࡀ࠿࠿ࡿࡋࠊởᰁ≀ࡢ஧ḟฎ⌮ࡀ࠸ࡽ࡞࠸ࠋᮏ◊✲࡛ࡣࠊಁ㐍㓟໬ἲࡢ୍✀࡜ࡋ࡚ࠊ
UV TiO2 ࢩࢫࢸ࣒ࡀ౑ࢃࢀ࡚࠸ࡿࠋ
᭱㏆ࠊගࡔࡅ࡛ởᰁ≀㉁ࢆศゎ࡛ࡁࡿ㓟໬ࢳࢱ
ࣥගゐ፹ࡀὀ┠ࡉࢀ࡚࠸ࡿࠋගゐ፹࡜ࡋ࡚ࡢ㓟໬ࢳࢱࣥࡣ௨ୗࡢ≉ᚩࢆᣢࡗ࡚࠸ࡿࠋ
ķᙉ࠸㓟໬ຊ࡜㉸ぶỈᛶࢆᣢࡘࠋ
ĸ⣸እ⥺↷ᑕ࡛᭷ᶵởᰁ≀ࡀศゎ࡛ࡁࡿࠋ
Ĺ㈨※ⓗ࡟㇏ᐩ࡛࠶ࡿࠋ
ĺᏳᐃࡋࡓ≀㉁࡛ࠊỈ࡟ධࢀ࡚ࡶศゎࡋ࡞࠸ࠋĻ஧ḟởᰁࡢᚰ㓄ࡀ࡞࠸ࠋ ௨ୖࢆ㋃ࡲ࠼ࠊᮏ◊✲࡛ࡣ࢚ࢿࣝࢠ࣮
ࣇ࣮ࣜࠊ⎔ቃ㈇Ⲵࡀపࡃࠊ⡆᫆࡞ฎ⌮ἲࢆᐇ⌧ࡋ࠺ࡿ㓟໬ࢳࢱࣥගゐ፹ࢆࢰࣝࢤࣝἲ࡛ㄪ〇ࡋࠊ᭷ᶵሷ⣲໬ྜ≀ࢆ㝖ཤࡍࡿࡇ࡜ࢆ┠ⓗ࡜ࡋ࡚࠸ࡿࠋ

33

2. ᐇ㦂
2.1 ගゐ፹ࡢྜᡂ ගゐ፹ࡣࠊࢰࣝࢤࣝἲ࡛ྜᡂࡋࡓࠋྜᡂࡢᡭ㡰ࡣ௨ୗࡢࡼ࠺࡟♧ࡍࠋ
C12H28O4Ti

CH3COOH㸮Υࠊ1 ᫬㛫࡛ᨩᢾࡋ
࡞ࡀࡽ⁲ୗ

H2O
⁐ᾮ A
㉸㡢Ἴฎ⌮ 30 ศࡋࠊ
5 ᫬㛫⥆ࡅ࡚ᨩᢾ

㏱᫂࡞ TiO2 ⁐ᾮ
70Υ࡛ 12 ᫬㛫⇍ᡂࢤࣝ100Υ࡛஝⇱ࡋࠊ○ࡃࣃ࢘ࢲ࣮
500Υ࡛ 5 ᫬㛫↝ᡂTiO2
2.2 ගゐ፹ࡢ࢟ࣕࣛࢡࢱࣜࢮ࣮ࢩࣙࣥ ྜᡂࡋࡓගゐ፹࡟ࡘ࠸࡚ࠊTG-DTAࠊXRDࠊ
SEM-EDSࠊTEM ཬࡧ BET ẚ⾲㠃✚ ᐃ࡟ࡼࡿ࢟ࣕࣛࢡࢱࣜࢮ࣮ࢩࣙࣥࢆ⾜ࡗࡓࠋ⾲ 1 ࡟ྜᡂࡋࡓྛගゐ፹ࡢẚ⾲㠃✚ཬࡧ⢏Ꮚᚄࢆ♧ࡍࠋ
⾲ 1 ྛගゐ፹ࡢẚ⾲㠃✚ཬࡧ⢏Ꮚᚄ๓㥑య:⁐፹:Ỉ
(ࣔࣝẚ)

ẚ⾲㠃✚
(੍/g)

୍ḟ⢏Ꮚᚄ
(nm)

1:10:250
1:10:300
1:10:350
1:10:400
Degussa P-25

94
102
110
98
47

16.7
14.5
13.4
14.1
19.6

ẚ㍑ࡢࡓࡵ࡟ࠊᕷ㈍ࡢ㓟໬ࢳࢱࣥࡢ࢟ࣕࣛࢡࢱ
ࣜࢮ࣮ࢩࣙࣥࡶ⾜ࡗࡓࠋ⾲ 1 ࡟♧ࡋࡓࡼ࠺࡟ࠊ๓
㥑య㸸⁐፹㸸Ỉࡢࣔࣝ⋡ࢆㄪᩚࡋ࡞ࡀࡽࠊㄪ〇ࡋࡓ㓟໬ࢳࢱࣥࡣ␗࡞ࡿࢧ࢖ࢬࡢ⢏Ꮚᚄࡀ࡛ࡁࠊᕷ㈍ࡢ㓟໬ࢳࢱࣥࡼࡾᑠࡉ࠸ࠋẚ⾲㠃✚ࡀ 2 ಸ௨ୖ኱ࡁ࠸ࠋࡋࡓࡀࡗ࡚ࠊㄪ〇ࡋࡓ㓟໬ࢳࢱࣥࡣᕷ㈍ࡢ㓟໬ࢳࢱࣥගゐ፹ࡼࡾ㧗࠸ගゐ፹άᛶࢆ♧ࡍࡇ࡜ࢆண ࡋ࡚࠸ࡿࠋ

ᅗ㸰 PCE ࡢศゎᅗ㸰࡟♧ࡋࡓࡼ࠺࡟ࠊ๓㥑య㸸⁐፹㸸Ỉ(ࣔࣝẚ)
㸻1:10:350 ࡟࠾ࡅࡿྜᡂࡋࡓ㓟໬ࢳࢱࣥࡣ୍␒㧗࠸ගゐ፹άᛶࢆ♧ࡋࡓࠋࡇࡢ᫬ࠊPCE ࡢศゎ⋡
ࡣ 85%࡛࠶ࡿࠋᕷ㈍ࡢ㓟໬ࢳࢱࣥගゐ፹ࡣ୍␒ప
࠸ศゎ⋡ 51%ࢆᚓࡓࠋ
3. ⤖ㄽ ࢰࣝࢤࣝἲ࡛ࢼࣀࢧ࢖ࢬࡢ㓟໬ࢳࢱࣥගゐ፹ࡢྜᡂࡀ࡛ࡁࠊ୍ḟ⢏Ꮚᚄࡣ 13.4~16.7nm ࡛࠶ࡿࠋPCE ࡢගศゎࡢᐇ㦂࡟࠾࠸࡚ࠊ๓㥑య㸸⁐፹㸸Ỉࡢࣔࣝ⋡ࢆㄪᩚࡋ࡞ࡀࡽࠊྜᡂࡋࡓ㓟໬ࢳࢱࣥගゐ፹ࡣ␗࡞ࡿගゐ፹ࡢάᛶࢆ♧ࡋࡓࠋ๓㥑య㸸⁐፹㸸Ỉ㸻1:10:350 ࡢ᫬ࠊྜᡂࡋࡓ㓟໬ࢳࢱࣥࡢ
⢏Ꮚᚄࡀ୍␒ᑠࡉࡃࠊẚ⾲㠃✚ࡀ୍␒኱ࡁࡃࠊ
PCE ࡟ᑐࡍࡿศゎ⋡ࡀ୍␒㧗ࡃࠊࡼࡾ㧗࠸ගゐ፹άᛶࢆ♧ࡋࡓࠋ

ᅗ 1 XRD ࡢࣃࢱ࣮ࣥᅗ㸯࠿ࡽぢࡿ࡜ࠊㄪ〇ࡋࡓ㓟໬ࢳࢱࣥࡢ࢔ࢼ
ࢱ࣮ࢮᆺࡢࡳࡀ☜ㄆ࡛ࡁࠊᕷ㈍ࡢ㓟໬ࢳࢱࣥගゐ፹ࡣ࢔ࢼࢱ࣮ࢮᆺ௨እ࡟ࣝࢳࣝᆺࡶほ ࡉࢀࡓࠋ࢔ࢼࢱ࣮ࢮᆺࡢ᪉ࡣࣂࣥࢻࢠࣕࢵࣉࡀ኱ࡁࡃࠊග
ゐ፹࡜ࡋ࡚ࡢάᛶࡀ㧗࠸ࡢ࡛ࠊ࢔ࢼࢱ࣮ࢮᆺࡢࡳほ ࡉࢀࡓ㓟໬ࢳࢱࣥࡢගゐ፹άᛶࡣࡶࡗ࡜㧗࠸ࠋ
2.3 PCE ࡢගศゎᐇ㦂 UV ※࡜ࡋ࡚ࠊ100WࠊἼ㛗ࡣ 300~600nm ࡢ㧗ᅽỈ㖟ࣛࣥࣉࢆ⏝࠸࡚ PCE ࡢගศゎᐇ㦂ࢆ⾜ࡗࡓࠋPCE ࡢ᥹Ⓨࢆᢚไࡍࡿࡓࡵ࡟ᐇ㦂࡛ࡣ㛢㙐⣔
ࢆಖࡕࠊUV ↷ᑕ࡟ࡼࡿගศゎᐇ㦂ࡢ๓࡟ PCE ⁐ᾮ࡜㓟໬ࢳࢱࣥගゐ፹ࢆ྾╔ᖹ⾮࡟⮳ࡽࡏ࡚࠿ࡽࠊ୍ᗘྍど࣭⣸እ⥺ศගἲ࡛ึ྾ගᗘࢆ ᐃࡋࠊࡑࢀ࠿ࡽග↷ᑕࢆ⾜࠺ࠋ
PCE ࡢึ⃰ᗘࡣ 150 ppm
࡛ࠊPCE Ỉ⁐ᾮࡢ㔞ࡣ 700 mlࠊゐ፹ᢞධ㔞ࡣ 0.14
g ࡛࠶ࡿࠋPCE ࡣ 201 nm ࡢἼ㛗࡛྾ගᗘࡀ୍␒
㧗࠸ࡢ࡛ࠊࡇࡢἼ㛗࡟࠾ࡅࡿ྾ගᗘࢆ ᐃࡋࡓࠋỈࡢࣔࣝ⋡ࢆㄪᩚࡋ࡞ࡀࡽࠊㄪ〇ࡋࡓ㓟໬ࢳࢱࣥ
ཬࡧᕷ㈍㓟໬ࢳࢱࣥගゐ፹࡟ࡼࡿ PCE ࡢศゎ⋡ࡣᅗ㸰࡟♧ࡍࠋ

ཧ⪃>@19HQNDWDFKDODP0DWHULDOV&KHPLVWU\DQG
3K\VLFV  
>@*R,VKLGDWKHGLVVHUWDWLRQIRUPDVWHUGHJUHHRIHQJLQHHULQJ7RN\R,QVWLWXWHRI7HFKQRORJ\
>@=KLZHL:XWKHGLVVHUWDWLRQIRUPDVWHUGHJUHHRIHQJLQHHULQJ7RN\R,QVWLWXWHRI7HFKQRORJ\

34

ࢱࢣ࠿ࡽࡢࣂ࢖࢚࢜ࢱࣀ࣮ࣝ⏕ᡂ࡟࠾ࡅࡿᚤ⣽໬࡜࢔ࣝ࢝ࣜฎ⌮ࡢຠᯝᏛ⡠␒ྕ㸸08-12287 Ặྡ㸸ᚎ ⱁⴌ ᣦᑟᩍဨ㸸୰ᓮ Ύᙪ

1 ┠ⓗ2.2 ࢱࢣࡢ㓟࣭࢔ࣝ࢝ࣜฎ⌮ࢱࢣ࡟ྵࡲࢀࡿࣜࢢࢽࣥࢆ㝖ཤࡋࠊࢭ࣮ࣝࣟࢫࡢ
ศゎ⬟ࢆྥୖࡉࡏࡿ┠ⓗ࡛ࢱࢣ⢊ᮎࢆ㓟࣭࢔ࣝ࢝ࣜ
⁐ᾮ࡛ฎ⌮ࡋࡓࠋ㓟࣭࢔ࣝ࢝ࣜฎ⌮ࡣ 4%H2SO4ࠊ
1%NaOHࠊ
1%Ca(OH)2 Ỉ⁐ᾮ࡟ 50 Pm ࢱࢣ⢊ᮎࢆ 100
g/L ࡜࡞ࡿࡼ࠺࡟ῧຍࡋࠊ࣮࢜ࢺࢡ࣮ࣞࣈ࡛ 135Υࠊ
90 ศ㛫ຍ⇕ࡍࡿࡇ࡜࡟ࡼࡾ࠾ࡇ࡞ࡗࡓࠋᘬࡁ⥆ࡁࠊ
ℐ㐣࡟ࡼࡾᅛᾮศ㞳ࡋࠊᅛయヨᩱࢆ⵨␃Ỉ࡛Ὑίࡋࡓᚋ஝⇱ࡉࡏࡓࠋࡓࡔࡋࠊ1%Ca(OH)2 ฎ⌮࡛ࡣࠊℐ
㐣࡟ࡼࡿᅛᾮศ㞳ࡢ๓࡟ங㓟࡟ࡼࡿ୰࿴ࢆ࠾ࡇ࡞࠸ࠊCa(OH)2 ࢆྍ⁐໬ࡉࡏ࡚ྲྀࡾ㝖࠸ࡓࠋConcentration of glucose(g/L)ࢱࣀ࣮ࣝ 5 mL ࡜⵨␃Ỉ 5 mL ࡢΰྜ⁐ᾮ࡟ࣇࣟࣟࢢ
㏆ᖺࠊ▼Ἔ௦᭰⇞ᩱ࡜ࡋ࡚ࠊ㣗⣊࡜➇ྜࡍࡿࡇ࡜ ࣝࢩࣀ࣮ࣝ 38 mg ࢆຍ࠼࡚⁐ゎࡋࠊሷ㓟 5 mL ࢆຍࡢ࡞࠸ࢭ࣮ࣝࣟࢫᛶ㈨※࠿ࡽࣂ࢖࢚࢜ࢱࣀ࣮ࣝࢆຠ ࠼࡚ᰁⰍ⁐ᾮ࡜ࡋࡓࠋᮍฎ⌮ࡢ 50 Pm ࢱࢣ⢊ᮎࠊ࠾⋡ࡼࡃ⏕⏘ࡍࡿ᪉ἲࡢ㛤Ⓨࡀᮇᚅࢆ㞟ࡵ࡚࠸ࡿ[1]ࠋ ࡼࡧࠊ࢔ࣝ࢝ࣜฎ⌮ࡋࡓ 50 Pm ࢱࢣ⢊ᮎ 1 mg ࡟ᰁ୍᪉ࠊࢱࢣࡣᡂ㛗ࡀ㏿ࡃࠊᯘᆅࡸ⏿࡬ࡢ౵㣗ࡀၥ㢟 Ⰽ⁐ᾮࢆࡑࢀࡒࢀ 40 PL ⁲ୗࡋࡓࠋ⣙ 1 ศ㛫㟼⨨ࡋ࡜࡞ࡗ࡚࠸ࡿࡇ࡜࠿ࡽࠊ➉ᯘࡢᩚഛ࡜ఆ᥇ࡋࡓࢱࢣ ࡓᚋࠊࢱࢣ⢊ᮎࢆ㢧ᚤ㙾࡛ほᐹࡋࡓࠋࡢ᭷ຠ฼⏝ࡢࡓࡵࡢᢏ⾡㛤Ⓨࡀồࡵࡽࢀ࡚࠸ࡿࠋᮏ
2.5 NaOH ฎ⌮ࢱࢣ⢊ᮎࡢᅛయᇵ㣴 SSF◊✲࡛ࡣࠊࢱࢣ࡟ᚤ⣽໬ฎ⌮࡜࢔ࣝ࢝ࣜฎ⌮ࢆ᪋ࡍ
1%NaOH ฎ⌮ࡋࡓ 50 Pm ࢱࢣ⢊ᮎࡢᅛయᇵ㣴 SSF
ࡇ࡜࡟ࡼࡾࢭ࣮ࣝࣟࢫࡢศゎᛶࢆྥୖࡉࡏࠊྠ᫬⢾ࢆ࠾ࡇ࡞࠸ࠊ㧗⃰ᗘࡢ࢚ࢱࣀ࣮ࣝ⏕ᡂࢆヨࡳࡓࠋ໬Ⓨ㓝 (SSF) ࡟ࡼࡿࢱࢣ࠿ࡽࡢࣂ࢖࢚࢜ࢱࣀ࣮ࣝ
Yeast extractࠊPolypeptonࠊ࣓࢖ࢭ࣮ࣛࢮࢆ⁐ゎࡋࡓ⏕ᡂࢆヨࡳࡓࠋ
0.1 M ࢡ࢚ࣥ㓟⦆⾪ᾮ࡟ 1%NaOH ฎ⌮ࢱࢣᚤ⣽⢊ᮎࢆῧຍࡋࠊBaker's yeast ࢆ᥋✀ࡋ࡚ ᗘ᮲௳ 35Υ࡛
2 ᐇ㦂᪉ἲ
ᇵ㣴ࢆ㛤ጞࡋࡓࠋ࡞࠾ࠊYeast extract ࡣ 3 g/Lࠊ
2.1 ౑⏝ࡋࡓࢱࢣࠊ㓝⣲࠾ࡼࡧ㓝ẕPolypepton
ࡣ 5 g/Lࠊ1%NaOH ฎ⌮ࢱࢣᚤ⣽⢊ᮎࡣ ࢱࢣࡣ㟼ᒸ┴὾ᯇᕷ࡛᥇ྲྀࡋࡓᏗ᐀➉ࢆ➉⢊○
300
g/L࡜࡞ࡿࡼ࠺࡟ῧຍࡋࡓࠋ
ࡲࡓࠊ࣓࢖ࢭ࣮ࣛࢮᶵ (ࢳࢵࣃ࣮ࣜࢼࠊ୸኱㕲ᕤᰴᘧ఍♫)࡟ࡼࡾ⢊○ࡋࠊࡢ⃰ᗘࡣ
5ࠊ10ࠊ15g/L࡛࠾ࡇ࡞ࡗࡓࠋ96᫬㛫ᇵ㣴ࢳࢵࣉ࡟ࡋࡓࡶࡢࢆ౑⏝ࡋࡓࠋࡲࡓࠊࢱࢣࡢ⢾໬࡟ࡋࠊࢢࣝࢥ࣮ࢫ⃰ᗘ࡜࢚ࢱࣀ࣮ࣝ⃰ᗘࢆ
HPLC
࡟ࡼ࠾࠸࡚ࠊࢱࢣ࡟ྵࡲࢀࡿࢭ࣮ࣝࣟࢫ࡜࣓࢖ࢭ࣮ࣛࢮ
ࡾᐃࡋࡓࠋࡢ཯ᛂᛶࢆྥୖࡉࡏࡿࡓࡵࠊࢱࢣࢳࢵࣉࢆᚤ⣽໬ฎ⌮ࡋࡓࠋᖖ ⏕➉ᚤ⢊〇㐀ᶵ(PA-Zࠊ୸኱㕲ᕤᰴᘧ఍ 3 ⤖ᯝ࡜⪃ᐹ♫)࡟ࡼࡾࢱࢣࢳࢵࣉࢆ⢏ᚄ 300㹼500 Pmࠊ࠾ࡼࡧࠊ
3.1 ࢱࢣࡢ⢾໬࡟࠾ࡅࡿᚤ⣽໬ࡢຠᯝ
50 Pm ௨ୗࡢ⢊ᮎ࡟࡞ࡿࡼ࠺ࠊࡑࢀࡒࢀᚤ⣽໬ࡋ࡚ ࢱࢣࢳࢵࣉࠊ
300㹼500 Pm ࢱࢣ⢊ᮎࠊ
࠾ࡼࡧࠊ
50 Pm
౑⏝ࡋࡓࠋ㓝⣲࡜ࡋ࡚ࡣࠊ᫂἞〇Ⳬ࡟ᥦ౪ࢆཷࡅࡓ
ࢱࢣ⢊ᮎࡢ⢾໬࡟࠾ࡅࡿ 96 ᫬㛫ᚋࡢࢢࣝࢥ࣮ࢫ⃰Trichoderma viride ⏤᮶ࡢ࣓࢖ࢭ࣮ࣛࢮ(Meicelaseᗘࢆ Fig. 1 ࡟♧ࡍࠋࢢࣝࢥ࣮ࢫ⃰ᗘࡣࠊ50 Pm ࢱࢣ
CEPB-5394ࠊ᫂἞〇Ⳬᰴᘧ఍♫)ࢆ౑⏝ࡋࡓࠋ㓝ẕ࡟⢊ᮎ࡛᭱኱ࡢ 2.09 g/L ࡜࡞ࡾࠊࢱࢣࡢ⢏ᚄࡀᑠࡉ࠸ࡣ ࣃ ࣥ 㓝 ẕ ࡛ ࠶ ࡿ Baker’s yeast (Saccharomyces࡯࡝⏕ᡂࡍࡿࢢࣝࢥ࣮ࢫ⃰ᗘࡀ㧗ࡃ࡞ࡿࡇ࡜ࡀ☜cerevisiae)ࢆ⏝࠸ࡓࠋ㓝ẕࡣ 30Υ࡛ 3 ᪥㛫ࠊYM ᐮኳ࠿ࡵࡽࢀࡓࠋ௨ୖࡢ⤖ᯝࠊࢱࢣࢆᚤ⣽໬ࡍࡿࡇ࡜࡟ᇵᆅ(glucose,10g㸹Polypepton,5g㸹Yeast extract,3g㸹
ࡼࡗ࡚ࢭ࣮ࣝࣟࢫ࡜㓝⣲ࡢ཯ᛂᛶࢆ㧗ࡵࡿࡇ࡜ࡀ
Malt extract,3g㸹agar,20g㸹distilled water,1L㸹pH 6.2)
࡛ࡁࡓࠋୖ࡛ᇵ㣴ᚋ 4Υࡢ෭ⶶᗜ࡛ಖᏑࡋࡓࠋ
3

2

1

0
chip

powder300~500 Pm

fine powder
50 Pm

2.3 ࢱࢣࡢᾮయ⢾໬
2.1 ࠾ࡼࡧ 2.2 ࡛ᚤ⣽໬ฎ⌮ࠊ࠾ࡼࡧࠊ㓟࣭࢔ࣝ࢝
Fig. 1 ࢱࢣࢳࢵࣉࠊ300㹼500 Pm ࢱࢣ⢊ᮎࠊ࠾ࣜฎ⌮ࡋࡓࢱࢣࡢ⢾໬ࢆ࠾ࡇ࡞ࡗࡓࠋ0.1 M ࢡ࢚ࣥ
ࡼࡧࠊ50 Pm ࢱࢣ⢊ᮎࡢᾮయ⢾໬࡟࠾ࡅࡿ 96᫬㛫ᚋࡢࢢࣝࢥ࣮ࢫ⃰ᗘ (n=3)
㓟⦆⾪ᾮ࡟ࢭ࣮ࣝࣛࢮࢆ 1.0 g/Lࠊㄪ〇ࡋࡓࢱࢣࢆ 20
g/L ࡜࡞ࡿࡼ࠺࡟ῧຍࡋࠊ ᗘ᮲௳ 35Υ࡛⢾໬ࢆ㛤ጞࡋࡓࠋࡲࡓࠊ⢾໬࡛⏕ᡂࡋࡓࢢࣝࢥ࣮ࢫࡢ⃰ᗘࢆ 3.2 ࢱࢣ⢊ᮎࡢ⢾໬࡟࠾ࡅࡿ㓟࣭࢔ࣝ࢝ࣜฎ⌮ࡢ
HPLC ࡟ࡼࡾ ᐃࡋࡓࠋຠᯝ
2.4 ࣇࣟࣟࢢࣝࢩࣀ࣮ࣝᰁⰍ࡟ࡼࡿࢱࢣ୰ࣜࢢࢽ  㓟࣭࢔ࣝ࢝ࣜฎ⌮ࡋࡓ 50Pm ࢱࢣ⢊ᮎࡢ⢾໬࡟࠾ࡅࡿࢢࣝࢥ࣮ࢫ⃰ᗘࡢ⤒᫬ኚ໬ࢆ๓ฎ⌮࡞ࡋࡢ⤖ᯝࣥࡢྍど໬ࢱࢣ࡟ྵࡲࢀࡿࣜࢢࢽࣥࢆࣇࣟࣟࢢࣝࢩࣀ࣮ࣝ ࡜ẚ㍑ࡋ࡚ Fig. 2 ࡟♧ࡍࠋ4%H2SO4ࠊ1%NaOHࠊ
ᰁⰍ࡟ࡼࡾྍど໬ࡋࠊNaOH ฎ⌮࡟ࡼࡗ࡚ࢱࢣ࡟ྵ 1%Ca(OH)2 ࡢ࠸ࡎࢀࡢ๓ฎ⌮࡟࠾࠸࡚ࡶࠊࢢࣝࢥ࣮
ࡲࢀࡿࣜࢢࢽࣥࡀ㝖ཤࡉࢀ࡚࠸ࡿ࠿ࢆ☜ㄆࡋࡓࠋ࣓ ࢫࡀ๓ฎ⌮࡞ࡋ࡟ẚ࡭࡚㧗⃰ᗘ࡟⏕ᡂࡋࡓࡇ࡜࠿ࡽࠊ
1
35

Concentration of glucose (g/L)

㓟࣭࢔ࣝ࢝ࣜࡢ๓ฎ⌮ࡣࢭ࣮ࣝࣟࢫࡢ⢾໬ࢆಁ㐍ࡍࡿຠᯝࡀ࠶ࡿࡇ࡜ࡀ☜࠿ࡵࡽࢀࡓࠋ୰࡛ࡶ 1%NaOH
ฎ⌮ࡣ≉࡟ຠᯝࡀ㧗ࡃࠊࢱࢣᚤ⣽⢊ᮎ୰ࢭ࣮ࣝࣟࢫ
ࡢศゎ⋡ࡣ 77.6%࡜࡞ࡾࠊ๓ฎ⌮࡞ࡋࡢ 13.0%࠿ࡽ
኱ᖜ࡟ቑຍࡋࡓࠋ

(A)

(B)

10
๓ฎ⌮䛺䛧
4% H2SO4
1% NaOH
1% Ca(OH)2

8

(C)

6
4
2

(A) ᮍฎ⌮(B) 1%NaOH ฎ⌮(C) 5%NaOH ฎ⌮

0
0

24

48

72

Time (h)Fig. 4 ࣇࣟࣟࢢࣝࢩࣀ࣮ࣝᰁⰍ࡟ࡼࡿ 50 Pm
ࢱࢣ⢊ᮎ୰ࣜࢢࢽࣥࡢྍど໬ (ಸ⋡ 200 ಸ)

Fig. 2 㓟࣭࢔ࣝ࢝ࣜฎ⌮ࡋࡓࢱࢣᚤ⣽⢊ᮎࡢᾮయ⢾໬࡟࠾ࡅࡿࢢࣝࢥ࣮ࢫ⃰ᗘࡢ⤒᫬ኚ໬ (n = 3)

Concentration of glucose(g/L)

Concs. of glucose & ethanol (g/L)

3.5 NaOH ฎ⌮ࢱࢣ⢊ᮎࡢᅛయᇵ㣴 SSF
3.3 ࢱࢣࡢᾮయ⢾໬࡟࠾ࡅࡿ NaOH ⃰ᗘࡢᙳ㡪
1%NaOH ฎ⌮ࢱࢣ⢊ᮎࢆ⏝࠸ࡓᅛయᇵ㣴 SSF ࡟
1%NaOH ࠾ࡼࡧ 5%NaOH ฎ⌮ࢆ᪋ࡋࡓࢱࢣࢳࢵ ࠾ࡅࡿᇵ㣴 96 ᫬㛫ᚋࡢࢢࣝࢥ࣮ࢫ⃰ᗘ࡜࢚ࢱࣀ࣮
ࣉࠊ300㹼500 Pm ࢱࢣ⢊ᮎࠊ࠾ࡼࡧࠊ50 Pm ࢱࢣ⢊ ࣝ⃰ᗘࢆ Fig. 5 ࡟♧ࡍࠋ⏕ᡂࡋࡓ࢚ࢱࣀ࣮ࣝࡢ⃰ᗘᮎࡢᾮయ⢾໬࡟࠾ࡅࡿ 96 ᫬㛫ᚋࡢࢢࣝࢥ࣮ࢫ⃰ᗘ ࡣ㓝⣲⃰ᗘ 5ࠊ10ࠊ15 g/L ࡛ࡑࢀࡒࢀ 30.7ࠊ40.1ࠊ45.4
ࢆ Fig. 3 ࡟♧ࡍࠋ࠸ࡎࢀࡶ NaOH ⃰ᗘࢆ㧗ࡃࡍࡿࡇ g/L ࡜࡞ࡗࡓࠋ௨ୖࡢ⤖ᯝࠊ1%NaOH ฎ⌮ࡋࡓ 50 Pm࡜࡛ࢢࣝࢥ࣮ࢫ⃰ᗘࡣ㧗ࡃ࡞ࡗࡓࡀࠊࢱࢣࢳࢵࣉ࡜ ࢱࢣ⢊ᮎࡢᅛయᇵ㣴 SSF ࡟࠾࠸࡚ࠊ㓝⣲⃰ᗘࢆቑຍ
300㹼500 Pm ࢱࢣ⢊ᮎ࡛ࢢࣝࢥ࣮ࢫ⃰ᗘࡀ 2 ಸ࡟࡞ ࡉࡏࡿࡇ࡜࡛࢚ࢱࣀ࣮ࣝࡣ 45 g/L ௨ୖࡢ㧗⃰ᗘ໬ࡀࡗࡓࡢ࡟ᑐࡋࠊ50 Pm ࢱࢣ⢊ᮎ࡛ࡣ 1.1 ಸ࡜ୖ᪼ᖜ ྍ⬟࡛࠶ࡿࡇ࡜ࡀ☜࠿ࡵࡽࢀࡓࠋ
ࡀᑠࡉࡃࠊ1%NaOH ฎ⌮࡛ࡶ㧗࠸ࢢࣝࢥ࣮ࢫ⃰ᗘࢆ
50ᚓࡽࢀࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋ
GlucoseFig. 4 ࡟ࣇࣟࣟࢢࣝࢩࣀ࣮ࣝᰁⰍ࡟ࡼࡾࣜࢢࢽ
Ethanol
40ࣥࢆྍど໬ࡋࡓ 50 Pm ࢱࢣ⢊ᮎࡢ㢧ᚤ㙾෗┿ࢆ♧ࡍࠋᮍฎ⌮ࡢ 50 Pm ࢱࢣ⢊ᮎ࡛ࡣ㉥Ⰽ࡟ᰁⰍࡉࢀ
30ࡓ㒊ศࡀከࡃࠊࣜࢢࢽࣥࡀከࡃྵࡲࢀ࡚࠸ࡿࡇ࡜
ࡀ☜࠿ࡵࡽࢀࡓࠋ୍᪉ࠊ 1%NaOHࠊ࠾ࡼࡧࠊ
20
5%NaOH ฎ⌮ࡋࡓ 50 Pm ࢱࢣ⢊ᮎࡣᰁⰍࡉࢀࡎࠊ
ࣜࢢࢽࣥࡀ㝖ཤࡉࢀ࡚࠸ࡿࡇ࡜ࡀ☜࠿ࡵࡽࢀࡓࠋ
10ࡇࢀࡼࡾࠊ50 Pm ࢱࢣ⢊ᮎ࡟࠾࠸࡚ࡣࠊNaOH ฎ⌮࡟࠾ࡅࡿ NaOH ࡢ⃰ᗘࡀ 1%࠶ࢀࡤࠊ༑ศ࡟ࣜ
0
5
10
15
ࢢࢽࣥࢆ㝖ཤࡍࡿຠᯝࡀ࠶ࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋ௨
Concentration of cellulase (g/L)ୖࡢ⤖ᯝࠊࢱࢣࢆ 50 Pm ࡟ᚤ⣽໬ࡍࡿࡇ࡜࡟ࡼࡗ࡚ࠊNaOH ฎ⌮ࡢຠ⋡ࢆ㧗ࡵࠊప࠸ NaOH ⃰ᗘ࡛
Fig. 5 1%NaOH ฎ⌮ࢱࢣᚤ⣽⢊ᮎࡢᅛయᇵ㣴ࣜࢢࢽࣥࡀ㝖ཤྍ⬟࡛࠶ࡿࡇ࡜ࡀ☜࠿ࡵࡽࢀࡓࠋ
SSF ࡟࠾ࡅࡿᇵ㣴 96 ᫬㛫ᚋࡢࢢࣝࢥ࣮ࢫ⃰ᗘ࠾ࡼࡧ࢚ࢱࣀ࣮ࣝ⃰ᗘ (n=3)

151%NaOH
5%NaOH4 ⤖ㄽ ᮏ◊✲࡛ࡣࠊࢱࢣࢆ50 Pm࡟ᚤ⣽໬ࡍࡿࡇ࡜࡟ࡼ

10

ࡗ࡚ࠊNaOHฎ⌮ࡢຠ⋡ࢆ㧗ࡵࠊప࠸NaOH⃰ᗘ࡛ࣜࢢࢽࣥࡀ㝖ཤྍ⬟࡛࠶ࡿࡇ࡜ࢆ☜࠿ࡵࡓࠋࡲࡓࠊ
1%NaOHฎ⌮ࡋࡓ50 Pmࢱࢣ⢊ᮎࢆᅛయᇵ㣴SSFࡍ
ࡿࡇ࡜࡟ࡼࡾࠊ⣙45 g/Lࡢ㧗⃰ᗘ࢚ࢱࣀ࣮ࣝࡀ⏕ᡂྍ⬟࡛࠶ࡿࡇ࡜ࢆ᫂ࡽ࠿࡟ࡋࡓࠋ

50Referenceschip

powder300~500 Pm

fine powder
50 Pm>@ ᳃ᕝ ᗣࠊ➨9ᅇ 㓝⣲ᛂ⏝ࢩ࣏ࣥࢪ࣒࢘ࠊࢭࣝ
࣮ࣟࢫ⣔ࣂ࢖࣐࢜ࢫࡢ㓝⣲⢾໬ࡢ⌧≧࡜ᑗ᮶ࠊ
http://www.amano-enzyme.co.jp/jp/company/pdf/9/theme-7.pdf

Fig. 3 1%NaOHࠊ5%NaOH ฎ⌮ࡋࡓࢱࢣࡢᾮయ
⢾໬࡟࠾ࡅࡿ 96 ᫬㛫ᚋࡢࢢࣝࢥ࣮ࢫ⃰ᗘ (n=3)

236Investigation of carbonation depth of real structuresin The Philippines and Laos
Student number: 08_13743

Name: Satoshi Takaki

1. Introduction
Nowadays, carbonation of concrete is serious problem. After losing alkalinity of concrete, passive statesof the surface on the steel bar could be broken, andbecome to be corrosion. To prevent deterioration,
maintenance is very important against carbonation of
concrete. And it is necessary to predict deterioration
by carbonation for maintenance.
However, carbonation of existing structures has notbeen investigated yet, especially in Southeast Asia
wherein it is a high temperature and heavy rain.
Therefore, this study aims, (1) To evaluate the carbonation depth in the Philippines and Laos existing
structure. (2) To evaluate effect of temperature andenvironments in carbonation speed coefficient.

Supervisor: Nobuaki Otsuki

thickness.
Formula (2.5) is what formula (2.3) is transformedinto.
‫ ܣ‬ൌ ܺΤඥ‫ ݐ‬െ ܺሺ‫ܪ‬Τ‫ܥ‬ሻሺ ݀Τ‫ܦ‬௙ ሻ 㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃(2.5)
From this formula, coefficient “A” is determined bysubstituting value of ̶‫ܦ‬௙ ”, “C”, “H”, “d”.
3. Sight Investigation and results
3.2 Investigation method
Carbonation depth was measured according to
NDIS 3419, “Method of test for neutralization depthof concrete in structures with drilling powder”[5](figure.1). More than three specimens of eachpart were prepared (figure.1). During drilling, thepowder were exposed in phenolphthalein solution, thecarbonation depth was measured when the powderchanged red-purple.

2. Carbonation speed
Carbonation is a chemical reaction as equation (2.1)
between calcium hydrate in concrete and CO2 in atmosphere.
Ca(OH)2 +CO2 →CaCO3 +H2 O㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃(2.1)
From the previous research[1], carbonation depth was

Fig.1 Method of test for neutralization depth

calculated the following formula (2.2).
ܺ ൌ ‫ܣ‬ξ‫ݐ‬㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃(2.2)“X” represents carbonation depth, “t” material age
and “A” is called carbonation coefficient which is affected by environments and concretes. Coefficient “A”
also depend on diffusion speed of carbon dioxide inconcrete. Therefore carbon dioxide is difficult to penetrate when concrete is wet, because of full of water infine pores. From previous research [3] carbonation of
concrete without roof is more slowly about twice thanwith roof.
Additionally, there are many painted concrete as investigation objects in this study, from that reason, weapply formula (2.3)[4] to carbonation depth of painted
concrete.
ܺ ൌ ‫ܣ‬ሺξ‫ ݐ‬൅ ܴ ଶ െ ܴሻ㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃㺃(2.3)( ‫ ܣ‬ൌ ඥʹ‫ܦ‬௖ ‫ܥ‬Τ‫ ܪ‬, ܴ ൌ ‫ܦ‬௖ Τ‫ܣ‬ሺ‫ܦ‬௙ Τ݀ሻ )This formula is based on Fick’s 1st Law. “X” and “A”
is same as formula (2.2) . “R” is value of resistance of
paint against carbonation. “‫ܦ‬௖ ̶̶‫ܦ‬௙ ̶ is carbon
dioxide diffusion in concrete and paint. “C” is CO2
concentration of surface on concrete. “H” amount ofcalcium hydrate by a unit volume. And “d” is paint

3.3 Observation site(1) In the Philippines
8 structures in near metro Manila were investigated.
There are 6 education facilities: the age of 14, 15, 16,
25, 30, 35years, and one house: the age of 40years,
one church: the age of 60years.
(2) In Laos
5 structures in Vientiane were investigated. There
are 5 educational facilities: age of 46(3) In Japan
To compare carbonation speed between countries
we used carbonation data in Japan[6]. There are 2 educational facilities: age of 18, 49years, 2 medical facilities: age of 13, 29years, 2 office: age of 13, 23years, 4houses: age of 21, 23, 24, 26years, and 1 hotel: age of
24.
4. Result and Discussion
4.1 Present condition
Figure.2 shows carbonation depth in each building.
As you can see, carbonation depth is big in Japan
compare with in Philippine and Laos. Furthermore,37

carbonation depth is big in Laos compare with in The
Philippines. It was consider that it is much
4.2 Effect of precipitation and paintFigure.3 shows carbonation speed coefficient incase of not painted concrete and painted concrete. As
you can see, it is obvious that carbonation depth issmall in case of painted concrete in all countries. Especially in the DLSU building, painted carbonation
depth become 1/4.24 times compare to no painted
carbonation depth. It was consider that, in the DLSU
building, repaint was conducted every five year,
therefore, the paint was kept in good condition.
Figure.4 shows effect of precipitation. And Figure.5
shows effect of temperature. From these figure effectof precipitation is larger than temperature.

࠙Referenceࠚ
[1]Nobuaki Otsuki and Shinichi Miyazati (2003) .ࢥ
ࣥࢡ࣮ࣜࢺᮦᩱ. Asakura Publishing.
[2]Kenichi Hisaka (1996). ≀⌮໬Ꮫ. Asakura Publishing[3]Takeru Tsuich,Michihiko Abe, Tadatsugu Kage,
Takuya Hasegawa ; “ᒇእᭀ㟢࡟ࡼࡿ୰ᛶ໬࡟ཬࡰ
ࡍྛ✀⎔ቃ᮲௳ࡢᙳ㡪”, Architectural Institute ofJapan[4]Masanori Kono, Keisuke Masuda, Takanori
Okihashi Yusuke Nakamura; “Study on the Predictionof Carbonation Progress of Concrete Considering theAging of Coating Materials for Textured Finishe on
Diffusion Theory”, Cement Science and Concrete
Technology[5]NDIS 3419, “Method of test for neutralization
depth of concrete in structures with drilling powder”,
The Japanese Society for Non-Destructive Inspection[6]Kasami Hideo; “Carbonation of Concrete and of
Rainforcing Bars in Exiting Structures up to 55 Years
Old”, Architectural Institute of Japannot painted in Japan
painted in The Philippine
not painted in The Philippine
painted in Laos
not painted in Laos30
Carbonation depth (mm)5. Conclusion
The conclusions of this study are as follows:(1) Carbonation speed in the Philippines and Laos issmall compare with in Japan.
(2) There is effect of paint and precipitation.

25
20
y = 4.3856x

15

y = 0.9425x

105y = 0.2209x

0
3

5

7

9

√year
Fig.2 carbonation depth each countries

Carbonation depth (mm)3.5
painted concrete3

not painted concrete

2.5
2
1.5
1
0.5
0
14 years
16 years
Philippine DLSU,Philippine

11yearJapan(mm/√year)

Caarbonation coefficientFig.3 Paint resistance by comparing same concrete
6

6

4

4

2

2

0

[7]

Philippine
Laos
Japan

0

1400

1900

2400

Precipitation (mm)

14

19

24

29

Temperature (˚C)

Figure.4 Effect of

Figure.5 Effect ofprecipitation

temperature38ᮾ࢔ࢪ࢔-໭⡿⯟㊰ࡢࢥࣥࢸࢼⲴືࡁ㔞ணࣔࢹࣝᏛ⡠␒ྕ㸸08-15498  Ặྡ㸸ࢱࢽ ࢘ࣥ ᣦᑟᩍဨ㸸ⰼᒸఙஓࡣࠊ༢఩᰿᳨ᐃ࡟ࡼࡗ࡚☜ㄆ࡛ࡁࡿࠋ
ᮏ◊✲ࡣࠊADF Augmented Dickey-Fuller)
᳨ᐃࢆ EViews ࡟ࡼࡗ࡚⾜࠺ࠋADF ᳨ᐃࡢ⪃࠼᪉ࡣ㸦2㸫1㸧ᘧࢆ᥎ィࡋ࡚α㸻㸯࠿࡝࠺࠿ࢆㄪ࡭ࡿࠋα㸻㸯࡛࠶ࢀࡤ༢఩᰿ࡀᏑᅾࡍࡿࡇ࡜
࡟࡞ࡿࠋ㹲ࡣࢱ࢖࣒ࢺࣞࣥࢻ࡛࠶ࡾࠊ᥎ィᮇ㛫ࡢጞࡲࡾࢆ㸯࡜ࡋ࡚ࠊ1 ࡎࡘቑ࠼࡚࠸ࡃኚᩘ࡛
࠶ࡿࠋ㸯㸬ࡣࡌࡵ࡟ ୡ⏺ᐃᮇࢥࣥࢸࢼ⯟㊰࡟࠾࠸࡚ࠊ࢔ࢪ࢔㸫໭⡿ࠊ࢔ࢪ࢔㸫Ḣᕞࠊ࢔ࢪ࢔ᇦෆࡢⲴືࡁ㔞ࡀከࡃࠊ㔜せ࡞⯟㊰࡜ㄆ㆑ࡉࢀ࡚࠸ࡿࠋ ᖺ ᭶ࡢ࢔ࢪ࢔  ᅜ࡜ᆅᇦ ᪥ᮏࠊ㡑ᅜࠊྎ‴ࠊ୰ᅜࠊ㤶 ࠊ࣐࢝࢜ࠊࢩ࣏࣮ࣥ࢞ࣝࠊࣇ࢕ࣜࣆࣥࠊ
࣐࣮ࣞࢩ࢔ࠊ࢖ࣥࢻࢿࢩ࢔ࠊࢱ࢖ࠊ࢝ࣥ࣎ࢪ࢔ࠊ
࣋ࢺࢼ࣒ࠊࢩ࣐࣮ࣕࣥࠊ࢖ࣥࢻࠊࣃ࢟ࢫࢱࣥࠊ
ࢫࣜࣛࣥ࢝ࠊࣂࣥࢢࣛࢹࢩࣗ Ⓨࡢ໭⡿⯟㊰࡛ࡣࠊ୰ᅜ㸦㸧ࠊ㡑ᅜ  ࠊ᪥ᮏ㸦㸧ࠊ
ྎ‴  ࡢᮾ࢔ࢪ࢔ࡀୖ఩㸲ࣨᅜࢆ⊂༨ࡋ࡚࠾ࡾࠊ඲యࡢ⣙㸶๭ࡢⲴືࡁ㔞ࢆ༨ࡵࡿࠋ ໭⡿ ⯟ࡢⲴືࡁ㔞ࡣࠕⲴືࡁࡢఙࡧ⋡ࡣᐇ㉁⤒῭ᡂ㛗⋡ࡢ㸱᥃ࡅࠖ࡜࠸࠺ࡇ࡜ࡀ▱ࡽࢀ࡚࠸ࡿࠋ⡿ᅜࡢᐇ㉁ *'3 ࡜໭⡿ ⯟Ⲵືࡁ㔞ࡢ┦㛵ಀᩘࡣ  ࡜ᙉ࠸ṇࡢ┦㛵ࡀ࠶ࡿࠋࡇࡢࡼ
࠺࡟ࠊ*'3 ࢆ฼⏝ࡍࢀࡤⲴືࡁ㔞࡜ࡢ㛵ಀ࠿ࡽᑗ᮶ࡢ኱ࡲ࠿࡞ண ࢆ⾜࠺ࡇ࡜ࡀฟ᮶ࡿࠋࡋ࠿
ࡋࠊ*'3 ࡣࢹ࣮ࢱࡢ㢖ᗘࡀᅄ༙ᮇࡈ࡜࡜ࡢࡓࡵࠊ
᭶ḟࣞ࣋ࣝࡢண ࢆ⾜࠺ࡇ࡜ࡀ࡛ࡁ࡞࠸ࠋ᭶ḟࣞ࣋ࣝࡢண ࡀྍ⬟࡟࡞ࡿ࡜ࠊ⯪♫ࡢ౪⤥㔞ண࡟ά⏝࡛ࡁࠊࡼࡾᐇ⏝ⓗ࡛࠶ࡿࠋ
ᮏ◊✲࡛ࡣࠊ᭶ḟࣞ࣋ࣝࡢ⤒῭ᣦᶆࢹ࣮ࢱࢆ
⏝࠸࡚ࠊᮾ࢔ࢪ࢔  ࣨᅜ㸦୰ᅜࠊ㡑ᅜࠊ᪥ᮏࠊྎ‴㸧✚ࡳ⡿ᅜᥭࡆࡢ໭⡿⯟㊰ࡢⲴືࡁ㔞ணࣔࢹࣝࢆᵓ⠏ࡍࡿࠋᵓ⠏ࡋࡓࣔࢹࣝ࠿ࡽⲴືࡁ
㔞࡬ࡢἼཬ⤒㊰ࢆ᫂ࡽ࠿࡟ࡋࠊⲴືࡁ㔞࡟᭱ࡶᙳ㡪ࡍࡿኚᩘࢆ≉ᐃࡍࡿࠋࡉࡽ࡟ࠊࡑࡢ⤒῭ᣦᶆࡣఱᮇᚋ࡟Ⲵືࡁ㔞࡟ᙳ㡪ࢆ୚࠼ࡿ࠿ゎ᫂ࡍࡿࠋ᭱ᚋ࡟ࠊ୰ᅜࠊ㡑ᅜࠊ᪥ᮏࠊྎ‴Ⓨ໭⡿╔ࡢ  ⯟㊰ࡢࣃ࣓࣮ࣛࢱࢆẚ㍑ࡍࡿࠋ㹲 㸻ߙܺ‫ݐ‬െͳ ൅ ෍ ߚ݅ οܺ‫ݐ‬െ݅ ൅ ߤ ൅ ߜ‫ ݐ‬൅ ‫ ݐݑ‬ሺʹ െ ͳሻ݅ ༢఩᰿᳨ᐃࡢ⤖ᯝࠊࢹ࣮ࢱࡀᐃᖖࡢሙྜࡣࡑ
ࡢࡲࡲ VAR ࣔࢹࣝࢆồࡵ࡚ࡼ࠸ࡀࠊ୙ᐃᖖࡢ
ሙྜࡣᐃᖖ࡟ࡍࡿࠋ⤒῭ኚᩘࡢᅇᖐศᯒ࡛ࡣࠊ
㠀ᐃᖖ࡞᫬⣔ิࡣ㝵ᕪࡸᡂ㛗⋡㸦ᑐᩘ㝵ᕪ㸧ࢆ࡜ࡿࡇ࡜࡟ࡼࡾࠊᐃᖖ࡟࡞ࡿࡇ࡜ࡀከ࠸ࠋ 㝵ᕪ㸸ο‫ ݕ‬ൌ  െ െͳ ᡂ㛗⋡㸸 ൌ  െ ͳ ൌ  െ െͳ  െͳ

ࢹ࣮ࢱࢆᐃᖖ࡟ࡋࡓᚋࠊVAR ࣔࢹࣝࡢࣃࣛ
࣓࣮ࢱ᥎ᐃࢆ⾜࠺ࠋ

2.2 ฼⏝ࢹ࣮ࢱᮾ࢔ࢪ࢔㸫໭⡿⯟㊰ࡢⲴືࡁ㔞࡟ᙳ㡪ࡍࡿ࡜⪃࠼ࡽࢀࡿ⤒῭ᣦᶆࡣࠊձⲴືࡁ㔞㸦᪥ᮏᾏ஦ࢭࣥࢱ࣮㸧ࠊղᮾ࢔ࢪ࢔ྛᅜࡢᑐ⡿ࢻࣝⅭ᭰
࣮ࣞࢺ㸦ࢽ࣮࣮ࣗࣚࢡ㐃㑥‽ഛ㖟⾜㸧ࠊճ⡿ᅜࡢ㖔ᕤᴗ⏕⏘ᣦᩘ㸦FRB㸸㐃㑥‽ഛไᗘ⌮஦఍㸧ࠊմ࢔ࢪ࢔㸫໭⡿ࡢࢥࣥࢸࢼ㐠㈤㸦୰ᅜᅜ㝿ᾏ㐠⥙㸧ࠊ௨ୖࡢᅄࡘ࡛࠶ࡿࠋ࡞࠾ࠊᣓᘼෆࡣࢹ࣮
ࢱࡢฟ඾࡛࠶ࡿࠋⲴືࡁ㔞ࡢ༢఩ࡣ TEU ࡛ࠊ௚ࡢࢹ࣮ࢱࡣᣦᩘ࡛࠶ࡿࠋ

㸱㸬⤖ᯝ࡜ศᯒ
3.1 ⤖ᯝ

㸰㸬◊✲ࡢ᪉ἲ ᡭἲ

ADF ᳨ᐃࡢ⤖ᯝࠊ୙ᐃᖖ࡜ุᐃࡉࢀࡓࢹ࣮
ࢱࢆᑐᩘࡸᑐᩘ㝵ᕪࢆྲྀࡿࡇ࡜࡟ࡼࡾᐃᖖ໬
ࡋࡓࠋ⾲㸯ࡣࢹ࣮ࢱࡢᐃᖖ໬ᚋࠊ෌ᗘ ADF ᳨ᐃࢆ⾜ࡗࡓ⤖ᯝࢆ♧ࡋ࡚࠸ࡿࠋ ᫬⣔ิࢹ࣮ࢱࢆ⏝࠸࡚ࠊఱᮇ๓ࡢኚᩘࡀⲴື
ࡁ㔞࡟ᙳ㡪ࢆ୚࠼ࡿ࠿ࢆ⪃ᐹࡍࡿࠋ⌧ᅾࡢⲴືࡁ㔞ࢆ㐣ཤࡢ್࡛ㄝ᫂ࡍࡿ࣋ࢡࢺࣝ⮬ᕫᅇᖐ
㸦9$5㸧ࣔࢹࣝࢆ⏝࠸ࡿࠋ
9$5 ࣔࢹ࡛ࣝ⏝࠸ࡿ᫬⣔ิࢹ࣮ࢱࡣᐃᖖ࡛࡞
ࡅࢀࡤ࡞ࡽ࡞࠸ࠋᒣᮏ  ࡣᐃᖖࡢ୕ࡘࡢ᮲
௳࡜ࡋ࡚ࠊձࢹ࣮ࢱࡢᖹᆒࡀ᫬㛫ࢆ㏻ࡌ୍࡚ᐃࠊ
ղࢹ࣮ࢱࡢศᩓࡀ᫬㛫ࢆ㏻ࡌ୍࡚ᐃࠊճࢹ࣮ࢱ
ࡢ⮬ᕫඹศᩓࡀ᫬㛫ࢆ㏻ࡌ୍࡚ᐃࠊࢆᣲࡆ࡚࠸
ࡿࠋࢺࣞࣥࢻࢆᣢࡗࡓኚᩘࠊࡘࡲࡾ୙ᐃᖖ࡞ኚ
ᩘྠኈࢆ᥎ィࡍࡿ࡜ࠊᮏ᮶ࡑࢀ࡯࡝┦㛵ࡣ㧗ࡃ࡞ࡃ࡚ࡶᙜ࡚ࡣࡲࡾࡀࡼࡃ࡞ࡿࠋ୧ኚᩘࡢࢺࣞࣥࢻせᅉࢆྲྀࡾ㝖࠸࡚᥎ᐃࡍࡿ࡯࠺ࡀࡼࡾṇ☜࡞᥎ィࡀฟ᮶ࡿࡓࡵࠊࢹ࣮ࢱࡢᐃᖖᛶࢆ஦๓
࡟☜ㄆࡍࡿᚲせࡀ࠶ࡿࠋࢹ࣮ࢱࡢᐃᖖᛶ࡟

 ⾲ 1 ᐃᖖ໬ᚋࡢ ADF ᳨ᐃࡢ⤖ᯝ(୰ᅜ⯟㊰)㻌 㻌 ኚᩘ㔞㻌㖔ᕤᴗ Ⅽ ᭰ 䝺 䝁 䞁 䝔 Ⲵ ື 䛝௦ධ㻌⏕⏘ᣦ 䞊䝖㻌
䝘㐠㈤㻌 㔞㻌
ᩘ್㻌
ᩘ㻌
-2.43
-1.22
-2.48
-3.49
ཎᩘ್㻌 t
0.02
0.08
0.15
ˎ 0.38
t
-1.05
-2.30
-6.71
-2.45
ᑐᩘ㻌
ˎ
0.02
0.07
0.77
0.38

-1.64
-8.35
-8.75
-6.71
ᑐᩘ㝵 t
ˎ
0.47
0.51
0.53
0.77
ᕪ㻌

 ྛኚᩘ࡟࠾࠸࡚ࠊ࢔ࣥࢲ࣮ࣛ࢖ࣥࢆ௜ࡅࡓᩘ

39

(R2=0.266)
ܹ‫ ݐ‬ൌ െͲǤͲʹͳܺ‫ݐ‬െͳ െ ͲǤͲ͵͵ܺ‫ݐ‬െʹ ൅ ͲǤͲ͸Ͷܼ‫ݐ‬െͶ  ൅ ͳǤͲͳ͵െͳ െ ͲǤʹͳͺܹ‫ݐ‬െͶ ൅ ͲǤͲͳͻܻ‫ݐ‬െͳ  ൅ ͲǤͲ͵ͻܿ (R2=0.989)
ྎ‴㸸ܻ‫ ݐ‬ൌ െͲǤ͸ͳͳܻ‫ݐ‬െͳ െ ͲǤʹͻʹܻ‫ݐ‬െʹ െ ͲǤʹͷͲܻ‫ݐ‬െ͵  െ ͲǤʹͳ͵ܻ‫ݐ‬െͶ െ ʹǤʹͳ͹ܼ‫ݐ‬െͶ െ ͲǤͷͺ͸ܿ
(R2=0.394)ܺ‫ ݐ‬ൌ െͲǤͳͻͻܺ‫ݐ‬െͳ െ ͲǤʹʹͺܺ‫ݐ‬െʹ ൅ ͳǤͻͺʹܹ‫ݐ‬െͶ  െ ͲǤʹͳ͹ܿ (R2=0.200)
ܼ‫ ݐ‬ൌ ͲǤ͹ͳ͵ܼ‫ݐ‬െͳ െ ͲǤͶͺͳܼ‫ݐ‬െʹ ൅ ͲǤͶͷͻܼ‫ݐ‬െ͵ െͲǤͲͶͺܺ‫ݐ‬െʹ ൅ ͲǤͲͷͳ (R2=0.453)
ܹ‫ ݐ‬ൌ െͲǤͲʹͳܺ‫ݐ‬െͳ െ ͲǤͲʹͻܺ‫ݐ‬െʹ ൅ ͲǤͲͳͻܺ‫ݐ‬െ͵ ൅ͲǤͲͳ͸ܺ‫ݐ‬െͶ ൅ ͲǤͳͺʹܼ‫ݐ‬െͶ ൅ ͳǤͳͳͷܹ‫ݐ‬െͳ       െ ͲǤͳ͸Ͷܹ‫ݐ‬െͶ ൅ ͲǤͲͶͻܿ (R2=0.988)

್ࡣࠊࡍ࡭࡚ࡢ௦ධᩘ್ࡢ୰࡛ẚ㍑ⓗ㹲್࡜Ỵᐃಀᩘࡀ㧗࠸ࠋࡋࡓࡀࡗ࡚ࠊࡑࢀࡽࡢᩘ್࡟ࡣ
༢఩᰿ࡀᏑᅾࡋ࡞࠸ྍ⬟ᛶࡀẚ㍑ⓗ㧗࠸࡜♧၀ࡉࢀࠊ9$5 ࣔࢹࣝࡢࣃ࣓࣮ࣛࢱ᥎ᐃ࡟⏝࠸ࡿࠋࡇࢀࡼࡾࠊ୰ᅜ⯟㊰ࡣ㖔ᕤᴗ⏕⏘ᣦᩘࡢᑐᩘ್ࠊ
Ⅽ࣮᭰ࣞࢺࠊࢥࣥࢸࢼ㐠㈤ࠊⲴືࡁ㔞ࡢᑐᩘࡢ㝵ᕪࢆ⏝࠸࡚᥎ᐃࡍࡿ㸬 ᪥ᮏࠊ㡑ᅜࠊྎ‴⯟㊰࡛ࡣࠊⅭ࣮᭰ࣞࢺ࡜Ⲵ
ືࡁ㔞ࡢࢹ࣮ࢱ࡟ࡘ࠸࡚ࠊ඲࡚ᑐᩘ㝵ᕪᑐᛂࢆྲྀࡗ࡚ᐃᖖ໬ࡋࡓࢹ࣮ࢱࢆ⏝࠸ࡿࠋ 9$5 ࣔࢹࣝࢆ᥎ィࡍࡿ࡜ࡁࠊఱᮇ๓ࡲ࡛ࣛࢢ
ࢆྲྀࡿ࠿Ỵᐃࡍࡿᚲせࡀ࠶ࡿࠋᮏ◊✲࡛ࡣࠊ୍⯡ⓗ࡟౑ࢃࢀ࡚࠸ࡿ㉥ụ᝟ሗ㔞ᇶ‽㸦$,&㸧ࢆ⏝࠸࡚Ỵᐃࡍࡿࠋศᯒࡢ⤖ᯝࠊྛᅜࡢ᭱㐺ࣛࢢᩘࡢ⤖ᯝࡣࠊ୰ᅜ࡜㡑ᅜࡀ㸰ࠊ᪥ᮏࡀ㸴ࠊྎ‴ࡀ㸳࡜࡞ࡗࡓࠋࣛࢢᩘࡀᑠࡉ㐣ࡂࡿ࡜ㄗᕪ㡯ࡢస⏝ࡀ኱ࡁࡃ࡞ࡿࡇ࡜࡜ࠊྛᆅᇦࡢࣔࢹࣝࢆẚ㍑ࡍࡿࡓࡵ࡟ࠊࣛࢢᩘࡣ㸲࡟ྜࢃࡏࡓࠋ ௨ୖࡢࡼ࠺࡟ࡋ࡚ࠊ$') ᳨ᐃ࡟ࡼࡾࢹ࣮ࢱࢆ
ᐃᖖ໬ࡋࠊ$,& ࡟ࡼࡾࣛࢢᩘࢆỴᐃࡋࡓ࠺࠼࡛ࠊ
9$5 ࣔࢹࣝࢆᵓ⠏ࡋࡓࠋ㹲್࡞࡝ࡢ⤫ィ㔞࠿ࡽࠊ⤫ィⓗ࡟᭷ព࡛࡞࠸ࣛࢢኚᩘࢆ㝖ࡁࠊⲴືࡁ㔞ࢆ <ࠊࢥࣥࢸࢼ㐠㈤ࢆ ;ࠊⅭ࣮᭰ࣞࢺࢆ =ࠊ㖔ᕤᴗ⏕⏘ᣦᩘࢆ :ࠊF ࢆᐃᩘ㡯࡜ࡍࡿ࡜୰ᅜࠊ㡑ᅜࠊ᪥ᮏࠊྎ‴ࡑࢀࡒࢀࡢ 9$5 ࣔࢹࣝࡣ௨ୗࡢ
ࡼ࠺࡟࡞ࡿࠋ୰ᅜ㸸ܻ‫ ݐ‬ൌ െͲǤʹͲͻܻ‫ݐ‬െͳ െ ͲǤͳ͸͹ܻ‫ݐ‬െʹ ൅ ͲǤʹͶͳܻ‫ݐ‬െ͵൅ͳǤ͸ͺͺܼ‫ݐ‬െͳ െ ʹǤͺͶͶܹ‫ݐ‬െͶ െ ͲǤͳͻͳܿ
(R2=0.249)
ܺ‫ ݐ‬ൌ െͲǤͳͺͲܺ‫ݐ‬െͳ െ ͲǤʹ͵͸ܺ‫ݐ‬െʹ ൅ ͳǤ͹ͺͻെͶെͲǤͳ͹͵ (R2=0.155)
ܼ‫ ݐ‬ൌ ͲǤͲͳ͸ܻ‫ݐ‬െͳ െ ͲǤʹͶͶܼ‫ݐ‬െʹ െ ͲǤ͵͸ͳܼ‫ݐ‬െ͵  ൅ ͲǤʹͻͳܹ‫ݐ‬െͳ െ ͲǤͳͳͶܿ (R2=0.307)ܹ‫ ݐ‬ൌ െͲǤͲͳͺܺ‫ݐ‬െͳ െ ͲǤͲʹ͸ܺ‫ݐ‬െʹ ൅ ͲǤͲʹͷܺ‫ݐ‬െ͵൅ͳǤͳͲͷܹ‫ݐ‬െͳ െ ͲǤʹͷͲܹ‫ݐ‬െͶ ൅ ͲǤͲͷͺܿ
(R2=0.986)᪥ᮏ㸸ܻ‫ ݐ‬ൌ െͲǤ͹͸Ͳܻ‫ݐ‬െͳ െ ͲǤͺ͸Ͷܻ‫ݐ‬െʹ െ ͲǤͷͶͻܻ‫ݐ‬െ͵െͲǤʹ͸ͻܻ‫ݐ‬െͶ ൅ ͲǤͻ͵͵ܺ‫ݐ‬െͶ െ ͶǤͶ͹Ͷܹ‫ݐ‬െͳ൅͵ǤͲͶͲܹ‫ݐ‬െͶ െ ͲǤ͸͵͸ܿ (R2=0.562)
ܺ‫ ݐ‬ൌ െͲǤͳͻʹܺ‫ݐ‬െͳ െ ͲǤʹʹͻܺ‫ݐ‬െʹ ൅ ͲǤ͵ͲͶܻ‫ݐ‬െʹ൅ͲǤʹ͵͹െʹ െ ͲǤ͸ͳͶܼ‫ݐ‬െʹ െ ͲǤͲͻͻ
(R2=0.269)
ܼ‫ ݐ‬ൌ ͲǤͳ͹ͺܼ‫ݐ‬െͳ െ ͲǤʹʹʹܼ‫ݐ‬െ͵ ൅ ͲǤ͸ͻͲܹ‫ݐ‬െͳ  െ ͲǤ͹͸Ͳܹ‫ݐ‬െʹ ൅ ͲǤͷʹ͹ܹ‫ݐ‬െͶ െ ͲǤͲʹͳܿ
(R2=0.187)
ܹ‫ ݐ‬ൌ െͲǤͲʹʹܺ‫ݐ‬െͳ െ ͲǤͲ͵Ͳܺ‫ݐ‬െʹ ൅ ͲǤͲʹ͵ܺ‫ݐ‬െ͵൅ͲǤͲʹͲܻ‫ݐ‬െͶ ൅ ͳǤͳͶʹܹ‫ݐ‬െͳ െ ͲǤʹ͸ͳܹ‫ݐ‬െͶ൅ͲǤͲͷ͵ܿ (R2=0.987)㡑ᅜ㸸ܻ‫ ݐ‬ൌ െͲǤͳ͵͵ܻ‫ݐ‬െʹ െ ͲǤʹͲ͵ܻ‫ݐ‬െͶ െ ͲǤ͵͹ʹܼ‫ݐ‬െʹ  െ ͵ǤͳͲʹܹ‫ݐ‬െͳ െ ͲǤʹ͸Ͳܿ (R2=0.178)
ܺ‫ ݐ‬ൌ െͲǤͳͻ͹ܺ‫ݐ‬െͳ െ ͲǤ͵ͳ͸ܺ‫ݐ‬െʹ ൅ ͲǤͳͷ͵ܻ‫ݐ‬െͳ  ൅ ʹǤͲͳʹെͶ െ ͲǤ͵ͳͳܿ (R2=0.237)
ܼ‫ ݐ‬ൌ െͲǤʹͲͻܼ‫ݐ‬െͳ െ ʹǤͲͷͲܹ‫ݐ‬െʹ ൅ ͲǤ͵Ͷ͵ܿ

3.2 ⤖ᯝࡢẚ㍑࡜⪃ᐹ ௨ୖࡢ⤖ᯝ࠿ࡽࠊྛኚᩘࡢ㛵ಀ࠾ࡼࡧⲴືࡁ
㔞࡬ࡢἼཬࢆ⪃ᐹࡍࡿࠋྛ⯟㊰࡛ࡣࠊ᭱ࡶ௒ࡢ
Ⲵືࡁ㔞࡟ᙳ㡪ࡀ࠶ࡿࡢࡣ 1 ᮇ๓࠿ࡽ 4 ᮇ๓ࡲ࡛ࡢⲴືࡁ㔞࡛ࠊ㖔ᕤᴗ⏕⏘ᣦᩘ࡟ࡶᙳ㡪ࢆཷࡅ࡚࠸ࡿࠋࡑࡢ୰࡛ࡶࠊ⯟㊰࡟ࡼࡗ࡚␗࡞ࡿ⤖ᯝࡀฟࡓࠋ୰ᅜ⯟㊰࡛ࡣࠊ4 ᮇ๓ࡢ W ࡜ 1 ᮇ๓
ࡢ Z ࡶ௒ᮇࡢⲴືࡁ㔞࡟ᙳ㡪ࡀ࠶ࡿࠋ㡑ᅜ⯟㊰࡛ࡣࠊ2 ᮇ๓ࡢ W ࡜ 2 ᮇ๓ࡢ Z ࡀⲴືࡁ㔞࡟స⏝ࡍࡿࠋ᪥ᮏ⯟㊰࡛ࡣࠊ1 ᮇ࡜ 4 ᮇ๓ࡢ W ࡜ 4
ᮇ๓ࡢ X ࠿ࡽ௒ᮇࡢⲴືࡁ㔞ࢆண ࡍࡿࡇ࡜ࡀ࡛ࡁࡿࠋྎ‴⯟㊰ࡣࠊ4 ᮇ๓ࡢ Z ࡀⲴືࡁ㔞࡟㛵ಀࡋ࡚࠸ࡿࠋ
㸲⯟㊰࡟࠾࠸࡚ࠊⅭ࣮᭰ࣞࢺࡣ௚ࡢᣦᩘ࡟࠶
ࡲࡾᙳ㡪ࢆཷࡅ࡚࠸࡞࠸ࡇ࡜ࡀࢃ࠿ࡿࠋ㖔ᕤᴗ⏕⏘ᣦᩘࡣ௒ᮇࡢ್ࡀ㸯ᮇ๓࡜㸲ᮇ๓ࡢᣦᩘ
࡟ᑐࡋ࡚ᙉ࠸⮬ᕫ┦㛵ᛶࢆᣢࡗ࡚࠸ࡿࡇ࡜ࢆ
♧ࡋ࡚࠸ࡿࠋ௒ᮇࡢࢥࣥࢸࢼ㐠㈤ࡣ 1 ᮇ๓ࡢⲴືࡁ㔞࡜ 4 ᮇ๓ࡢ㖔ᕤᴗ⏕⏘ᣦᩘ࡟స⏝ࢆ࠺ࡅ
࡚࠸ࡿࠋ
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 ᮾ࢔ࢪ࢔㸫໭⡿⯟㊰࡟ࡘ࠸࡚ࠊ୰ᅜࠊ᪥ᮏࠊ
㡑ᅜࠊྎ‴ࡢ㸲⯟㊰࡟ࡘ࠸࡚ VAR ࣔࢹࣝࢆ⏝࠸࡚Ⲵືࡁ㔞ண ࣔࢹࣝࢆᵓ⠏ࡋࡓࠋࡑࡢ⤖ᯝࠊ⯟㊰㛫ࡢ┦㐪Ⅼ࡜ඹ㏻Ⅼࢆ⪃ᐹฟ᮶ࡓࠋࡋ࠿ࡋࠊ
฼⏝ࡋࡓࢹ࣮ࢱ௨እ࡟ࠊCLI㸦OECD ࡢᬒẼ」
ྜඛ⾜ᣦᩘ㸧ࠊ⯪⭡㔞ࠊLEI㸦⤒῭ඛ⾜ᣦᩘ㸧ࡶⲴືࡁ㔞࡟ᙳ㡪ࡍࡿྍ⬟ᛶࡀ࠶ࡿࠋࡇࢀࡽࡢᩘ
್ࢆ⏝࠸ࢀࡤࠊࡼࡾಙ㢗ᛶࡢ㧗࠸ࣔࢹࣝࢆᵓ⠏ฟ᮶ࡿྍ⬟ᛶࡀ࠶ࡿࠋࡇࢀࡽࡢኚᩘࢆ⏝࠸࡚ࣔࢹࣝࢆᵓ⠏ࡍࡿࡇ࡜ࡀ௒ᚋࡢㄢ㢟࡛࠶ࡿࠋཧ⪃ᩥ⊩[1]㣤ሯಙኵ࣭ຍ⸨ஂ࿴(2008)㸸EViews ࡟ࡼࡿ⤒῭ண ࡜ࢩ࣑࣮ࣗࣞࢩࣙࣥධ㛛㸪᪥ᮏホㄽ♫ࠋ[2]ᒣ⃝ᡂᗣ(2010)㸸ᐇᡓィ㔞⤒῭Ꮫධ㛛㸪᪥ᮏホㄽ♫ࠋ[3]ᒣᮏᣅ㸦㸧㸸⤒῭ࡢ᫬⣔ิศᯒ㸪๰ᩥ♫ࠋ40

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%DFNJURXQG
 !& "!'  *! #+ &&  
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 # !++& && <!  ' <!# @#!;>D!##@ # # $< +" &  !&$;!>   &<; #'  ! #+ &#;;#*@&!##!&"&;G N#&#;;#"<*@&;>
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41

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Enhancement of the composting of sub-critical water-treated food waste using
acetic acid-degrading yeast
Student Number: 08-16470

Name: Songhua Zheng

1 Introduction

Supervisor: Kiyohiko NAKASAKI

pH, and concentration of acetic acid were monitored
every 24 h.

In the preliminary experiment, it was confirmedthat composting of the sub-critical water-treated foodwaste (STFW) cannot be performed successfully inshort time enough to carry out practically. It is known
that when the pH of the raw composting material islow, it takes extremely long time for the composting[1], therefore, the low pH characteristic of the STFW
was considered to cause the delay in the compostingtime. It was also confirmed previously that highconcentration of acetic acid is contained in the STFW,
which is one of the components that decrease the pH
of STFW. In this study, isolation of acetic
acid-degrading microorganisms and modification of
the STFW using the isolated microorganisms for theenhancement of composting of STFW were attempted.

2.4 STFW composting
Inoculation of strain RB1 was carried in tandemwith pH adjustment to 5.0 by addition of Ca(OH)2 tothe sterilized STFW at the start of experiment.
Composting temperature was set at 30°C during thefirst 5 days. At day 5, pH of compost was adjusted to
8.0 and seeding materials of Alles G was inoculated.
Then, incubation temperature was raised to 60oC andkept constant throughout the composting of 7 days.
The degradation of organic matter was monitored by
CO2 emission. The details of composting system isshown in Fig 4.

3 Results and discussion
2 Materials and methods

Two yeast strains that can degrade acetic acid wereisolated and named CM1 and RB1. A picture of thecolonies of the RB1 is shown in Fig. 1.
Fig. 2 shows the course of concentration of acetic
acid in the TS medium where RB1 and CM1 were
inoculated. The initial concentration of acetic acid was
5.5 g/L and after 48 h cultivation, it decreased to 1.3
g/L for the culture with RB1 and to 2.2 g/L for theculture with CM, suggesting the higher degradation
ability of RB1 compered to that of CM1. Based on the
results, we focused the RB1 and preferable pH
condition for RB1 to degrade acetic acid in the STFW
was investigated.

2.1 Acetic acid-degrading microorganisms
Compost product was used as inoculum and added
to BS medium containing 0.3% of acetic acid.
After the cultivation at 30 °C for 4days, the culture
was spread on the BS agar medium containing 0.3 %
acetic acid and incubated for 30°C for 4day. Colonies
appeared on the agar medium were isolated as acetic
acid-degrading microorganisms.
2.2 Acetic acid degradation by the isolates in aliquid medium
Isolated strains RB1 and CM1 were inoculated to
TS medium where acetic acid was added. Then,
cultivation was carried out at 30°C for 48 h. aliquot ofsamples were withdrawn and subjected to themeasurement of acetic acid concentration by HPLC.
2.3 Dependence on pH for the degradation ofacetic acid by RB1
STFW was sterilized prior to the inoculation ofstrain RB1. Lab-scale experimental set-up consisted ofa mini-reactor and an incubator to maintain thetemperature at 30°C. Air was supplied at the flow rateof 5.5 ml/min and the exhaust gas was captured tomeasure the gas volume and CO2 concentration once
daily. Four runs with different pH values of 5, 6, 7,
and 8 adjusted by addition of Ca(OH)2 were conducted
for 5 days during which the cell density of strain RB1,

Fig. 1 The picture of the colonies of RB1.

43

Concentration of acetic acid (g/L)

8

8

6

6

4

4

2

2

0

0

2

4

6

8

10

12

0

Cumulative emission of CO 2 (mol/batch)

10

2

10

Time (d)Fig. 4the courses of emittion rate of CO2 andcumulative emittion of CO2 during theexperiment using large scale apparatus.

6
5
4
3
2

strain RB1
strain CM1

1
0

Control01224Time (h)36

48

1. Flow meter

6. Styrofoam㩷 insulator11. Ammonia trap

2. Ribbonheater

7. Thermocouple

12. Silicagel adsorber

3. Gas meter

8. Temp.controller

13. Recorder

4. Perforated plate 9. Solenoid valve

Fig. 2 The courses of acetic acid concentration in TS
medium inoculated with RB1 and CM1.
Concentration of acetic acid (g/g-ds)

CO emission rate (mol/h/g-ds)

Fig. 3 shows the course of acetic acid concentrationin the STFW of which pH was adjusted to theintended values during the experiment. RB1 showed
highest acetic acid-degrading capability in theexperiment at pH 5 where the concentration of acetic
acid decreased from 0.0173 g/g-ds to 0.0047 g/ g-ds.
Therefore, composting of STFW after the degradationof acetic acid contained in STFW by RB1 at thecondition of pH 5 was carried out in large scale
apparatus.
Fig. 4 shows the courses of emittion rate of CO2
and cumulative emittion of CO2 during the experimentusing large scale apparatus. The cumulative emittionof CO2 increased steadily during the experiment and it
was confirmed that the composting of STFW was
successfully conducted in 7 days that was short timeenough to carry out practically after the pretreatment
by RB1 for 5 days.

5. Reactor

14. CO2 analyzer

10. Pulse㩷 transmitter 15. Micro-computer



0.03

Fig. 5 Schematic diagram of the experimental set up
with a large scale apparatus.

0.025

4 Conclusions
Acetic acid-degrading microorganism of CM1 andRB1 could be isolated and it was clarified that RB1
have higher acetic acid-degrading ability than CM1.
Furthermore, composting of STFW could be carriedout successfully in short time enough to conduct
practically after the pretreatment of STFW by RB1 at
the condition of pH5 that was determined to bepreferable for RB1 to degrade the acetic acid.

0.02
0.015
0.01
㫇㪟㩷㪌
㫇㪟㩷㪍
㫇㪟㩷㪎
㫇㪟㩷㪏

0.005
0

0

1

2
3
Time (d)45References

Fig. 3 The courses of acetic acid concentration in
STFW of which pH were adjusted at 5, 6, 7
and 8.

[1] K.R. Keehan : PB Report, 479-491 (1991)

44

Consideration of stress distribution in an antlion's nest using advantage of angle of repose
Student Number: 08B16760 Name: Lin TANGSupervisor: Thirapong PIPATPONGSA

3.1 Antlion experiment
The material used in antlion experiment is silica sand8 and 6. Ten healthy antlions were putted into 10
paper cups filled with sand. The height of pit wasmeasured after pit was dig. Angle of repose can becomputed from maximum and minimum width of thepit. The geometry of angle ĳ1, ĳ2, radius r1, r2 inprincipal axis and angle ĳ3, ĳ4, radius r3, r4 in second
axis are described in Fig.4. Experimental results forangle ĳ, radius r, void ratio e, relative density Dr are
shown in Table1, showing that density of silica sandno.8 is relatively larger than that of no.6 Box shear
test was carried out to find friction angle of both sandand reported to Table 2 with other sand’s properties.

1

Introduction
Inverted conical sand valley represents afundamental problem in geo-mechanics whose stress
distribution has not been clearly solved. This problem
can represent a condition of antlion’s nest (see Fig.1)
for small scale and a condition of open-pit mine (seeFig.2) for large scale. Spider and antlion are only
insects which can trap other insects. In GeotechnicalEngineering’s view point, antlion employs advantage
of angle of repose to trap small insects by digging
conical pits in loose sand. When a small insect,commonly an ant, falls into the pit, its weight causes
instability of the pit; hence cascading the victim down
to the bottom of the pit where antlion is waiting for.
Despite of different scale and boundary conditions,complicated problem on stress distribution in open-pit
mine would be able to simplify to that of antlion’s nest.
Therefore, investigation of antlion’s nest and its
physical model was conducted in this study.

Table1 experiment results in antlion’s nest㼍㼢㼑㼞㼍㼓㼑 䡎䠍㻔㼏㼙㻕 㼞㻞㻔㼏㼙㻕 㼞㻟㻔㼏㼙㻕 㼞㻠㻔㼏㼙㻕㼟㼕㼘㼕㼏㼍
㼟㼍㼚㼐㻌㻤
㼟㼕㼘㼕㼏㼍
㼟㼍㼚㼐㻌㻢

Trajectories of principal stresses
Granular slope inclining at an angle of repose
physically exhibits the steepest angle of descentrelative to the horizontals where slope face is on theverge of sliding. The fixed principal axes (FPA)
closure is considered as an ad hoc explanation about
trajectories of principal stresses (see Fig.3). Based onthe, the angle of major principal direction ȥ isassumed to be fixed on each half of the sand valley
which is determined from the state of stress adjacentto the failure plane.

㼍㼚㼓㼘㼑㻝 㼍㼚㼓㼘㼑㻞 㼍㼚㼓㼘㼑㻟 㼍㼚㼓㼘㼑㻠㻔㼐㼑㼓㻕 㻔㼐㼑㼓㻕 㻔㼐㼑㼓㻕 㻔㼐㼑㼓㻕

㼑

㻰㼞㻔㻑㻕

㻝㻚㻜㻜

㻝㻚㻟㻡

㻝㻚㻜㻞

㻝㻚㻞㻡

㻟㻥㻚㻠㻣 㻟㻝㻚㻝㻣 㻟㻤㻚㻡㻝 㻟㻟㻚㻝㻡 㻜㻚㻤㻞 㻤㻝㻚㻞㻝

㻝㻚㻟㻟

㻝㻚㻤㻜

㻝㻚㻟㻤

㻝㻚㻢㻟

㻠㻠㻚㻠㻥 㻟㻡㻚㻤㻤 㻠㻟㻚㻟㻝 㻟㻤㻚㻣㻠 㻜㻚㻥㻜 㻡㻟㻚㻥㻠

2

Table 2 physical nature of silica sand 8 and 6㼙㼍㼠㼑㼞㼕㼍㼘 㼍㼚㼓㼘㼑㻌㼛㼒㻌㼒㼞㼕㼏㼠㼕㼛㼚㻔㼐㼑㼓㻕 㼑㼋㼙㼍㼤㼟㼕㼘㼕㼏㼍㻌㼟㼍㼚㼐㻌㻤䃥㻩㻟㻞㻚㻣
㻝㻚㻟㻟㻟㼟㼕㼘㼕㼏㼍㻌㼟㼍㼚㼐㻌㻢䃥㻩㻟㻡㻚㻢
㻝㻚㻝㻟㻞

㼑㼋㼙㼕㼚
㻜㻚㻣㻜㻟
㻜㻚㻣㻝㻝

㻳㼟
㻞㻚㻢㻡
㻞㻚㻢㻡

Table 3 Testing program of physical model of sandcrater in a rectangular bin
Test

1

1

SPM

2

2

3

4

5

AP

AP

3

4

Hf

DP

AP

DP

20

20

20

30

30

1

2

Note: SPM=sample preparation method; AP=air pluviation
technique; 3DP=dry deposition technique; 4Hf=final height of
excavation

3

Experiments and results
The experiment includes two parts, antlion’s nest
experiment and sand crater physical modeling.

Full length: L = 88 cm
Pressure gauges
Center5 cm

2.5 cm5 cm

Centerline
Half length: L/2 = 44 cm

2.5 cm

Fig.1 Image of antlion
catching an ant

Fig. 5(a) Outline ofembedded gauges (left)

Fig.2 Illustration of
open-pit miningƌϮ3

Fig.3 Networks of principal
stresses in sand valley under
the closure of FPA

φ4

Fig. 5(b) Alignment ofpressure gauges (right)

3.2 Physical modeling of sand crater
A summary of the tests undertaken is given in Table
3. The physical model of load transmission beneaththe sand crater in the transparent plastic rectangularbin was carried out on 5 samples of dry silica sandno.8. The geometrical dimensions are, length L = 88cm, width W = 38 cm, and height Hb = 49.5 cm. The
scale was stuck on the edge of the bin. A series of low
capacities of pressure cells were employed for the

ƌϭ

φ1 φ

Pressure gauges

φ2

Fig.4 Description ofgeometry of
antlion’s nest
45

highly accurate and precise measurement of the lowpressure induced by self-weight of tested sand stored
in rectangular bin. The vertical pressures of the
granular material exerting to the floor were measured
by twelve miniature pressure gauges, 9 of which have
10 mm in diameter and a maximum capacity of 20
kN/m2, and other three 25 mm and 20 kN/m2 as shownin Figs. 5(a) and 5(b). As outlined in Fig. 5, 3 pressuregauges were installed at centerline, 4 on the right and5 on the left asymmetrically in order to effectively
catch a pressure profile by a limited number of sensors.
A half plane of the physical model can be simply
considered due to its symmetrical geometry. As the
sample preparation is one of the most important
aspects for soil testing, the uniformity of the sample
has been a major concern. Various techniques fromtheoretical and experimental viewpoints have been
proposed to reconstitute homogeneous samples.
Therefore, the sand samples of 20 cm and 30 cm
height were prepared by two techniques (air pluviationand dry deposition) as described in Table 3 to observe
the discrepancy of load transmission underneath thesand crater in rectangular bin. For each technique,sands were deposited in a bin at a constant 2 cm drop
height in order to obtain loose samples. Sands as to air
pluviation technique were filled in a physical model
bin with a rate 2 kg/min pouring from the plastic
bottle having the bigger cross section with a small
sieve opening of 1 mm at the bottom. In regard to dry
deposition technique, sands were spread with a rate 5kg/min into the center of the bin through cap-opened
plastic bottle with a 2.2-cm-diameter nozzle. Then, thevacuum with a sharp tube was used to suck sand along
the centerline; therefore, a conical crater was formedby successively sand avalanches inclining at the angleof repose. The sand was excavated until the final
depth (Hf) reaches 12 cm.
As shown in Fig. 6, the vertical pressure σv
measured along the floor of the physical model
normalized by its geostatic pressure γH was plottedagainst χ representing the distance from the centerline
along the floor normalized by half length L/2. The
diamond, rectangular, triangular, and circular symbols
denote a chain of pressures undergoing the excavation
stages at depth 0 cm, 4 cm, 8 cm, and 12 cm,
respectively.

WƌĞƐƐƵƌĞĐŚĂŶŐĞŝŶƚĞƐƚϭEŽƌŵĂůŝǌĞĚƉƌĞƐƐƵƌĞ͕ʍǀͬɶ,Ϯ͘Ϭ ĐŚϴ
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EŽƌŵĂůŝǌĞĚĚŝƐƚĂŶĐĞǆͬ;>ͬϮͿ(d)WƌĞƐƐƵƌĞĐŚĂŶŐĞŝŶƚĞƐƚϱEŽƌŵĂůŝǌĞĚƉƌĞƐƐƵƌĞ͕ʍǀͬɶ,

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EŽƌŵĂůŝǌĞĚĚŝƐƚĂŶĐĞǆͬ;>ͬϮͿ(e)4Discussion and conclusion
Angle of repose for loose sand represents its friction
angle. By comparing Table1 and Table2, angle formed
in antlion’s nest is larger than the friction angle; thus
indicating that antlion’s nest is on meta-stable state,even a little movement can cause collapse of its nest.
According to experimental result of physical model,
stagnant of pressure change at the center is clearlyobserved, because the pressure does not transfer to thebase for deep overburden layer. However, a centralpressure dip can be observed later once the excavation
process is continued and passed the critical depth
which is consistent with FPA closure.

Fig.6 Pressure changes on the base of sand valley
during excavation process for Test 1 to Test 5 using
silica sand no.8
References[1] Wittmer, J.P., Claudin, P., Cates, M.E., Bouchaud,
J.P., An explanation for the central stress minimum insand piles. Nature, 382(6589), 336-338, 1996.
[2] T. Pipatpongsa, S. Heng, A. Iizuka and H. Ohta,
Statics of loose triangular embankment under Nadai’ssand hill analogy, Journal of the Mechanics andPhysics of Solids, Vol.58(10), 1506-1523, 2010.
46

Magnetic Shielding from Solar Energetic Particles for Manned Deep Space Explorations
Student Number: 08-17787 Name: Yasuhito Nakatani

1. Introduction

Equation of Proton’s motion is

Shielding from Solar energetic particles (SEPs) is one of
the most difficult problems for manned deep space
explorations. SEPs are high energetic particles emitted fromthe Sun in case of a solar burst. Most of SEPs are protons.
We are protected from SEPs by the earth’s magnetic field
and its atmosphere. In deep space, however, astronauts are
at risk of exposure to SEPs, and could be exposed to morethan 1 Sv SEPs during a few days of a solar burst. Big solar
bursts occur several times during a 11-years solar cycle.
A major idea to protect astronauts from SEPs is to makewalls of spacecraft thick, which is against deep space
explorations. Another idea is to generate a magnetic fieldusing superconducting coils for deflecting trajectories ofproton colliding with a spacecraft. Figure 1 is a schematicconfiguration. A magnetic field generated by a loop current
in a superconducting coil is expected to deflect protons. The
idea has a potential to adopt less mass compared with theformer. It was considered that a superconducting coilsystem requires huge amounts of power to generate asufficiently intense magnetic field, for deflecting almost all
protons.
In this paper, number density distribution of the solar
protons around the loop current is investigated, to show
effects of the diameter of the coil and those of the location.

solar

Supervisor: Yoshihiro Mochimaru݉݀࢜ൌ ‫ݍ‬ሺ࢜ ൈ ࡮ሻ
݀‫ݐ‬

(3)

where ݉ :proton’s mass. Eq. (3) is integrated by
Runge-Kutta method.
Ȁ
െʹͲ

ͷͲ െͷ

Ͳ

Ȁ 
Ͳ



ͷ

ͷ
Ȁ
Ͳ

‫ܫ‬

protons
െͷὶධ㠃

coil

Fig. 2 Computational space (not to scale)

3. Results
3.1. Number density distribution of proton
Figure 3 shows the number density distribution of protons
for d=4.0 m and I=200 kA.
The number density drops to a lower value behind the coil.
Since the magnetic field’s Z component is symmetricalabout the coordinate origin, low number density region issymmetrical about  െ  plane at  ൌ Ͳ. High number
density region is found above the low number density
region. It is because Z axis plus directional Lorentz force.
The low density region expands along Y axis.

magnetic field
extensible arm

burst
spacecraft

protonssuperconducting coilFig. 1 Magnetic shielding from SEPs.

2. Simulation method
Figure 2 shows a computational space of ͳͲ ൈ ͳͲ ൈ͹Ͳ.
SEPs are assumed to be composed only of protons. The
solar protons fly into the computational space from  െ plane at =െʹͲ. The protons are velocities parallel to Y
axis.
Since protons possessing kinetic energy less than 50 MeV
are guarded by the walls of the spacecraft, the target kinetic
energy ‫ܧ‬௞ of the proton is assumed to be(1)ͷͲ ൑ ‫ܧ‬௞ Ȁሺሻ ൑ ʹͲͲProton’s particle flux is assumed to be ͳǤͲ ൈ ͳͲସ  ିଶ ȉ ିଵ . Their kinetic energy distribution is assumed due to. [2].
The coil is located at the oriin in the computational space.
The coil plane is in parallel with  െ  plane. Coil’s
diameter ݀ is 1.0, 2.0, 3.0, or 4.0 m. The coil current ‫ ܫ‬is10, 50, 100, 200, 300, or 400 kA. An electrical field
generated by solar wind is ignored. Based on thisassumption, each proton is affected by the following
Lorentz force,(2)ࡲ ൌ ‫ݍ‬ሺ࢜ ൈ ࡮ሻ
where ࡲ:Lorentz force, ‫ ݍ‬:electrical charge of a proton,
࢜:velocity of a proton, ࡮:magnetic flux density.

Fig. 3 Number density distribution of protons around thecoil (d=4.0 m, I=200 kA)
3.2. Effects of magnetic field intensity
Figure 4 shows the number density distribution of protons
for d=4.0 m and I=50 kA.
The low density region decrease in size with decrease in
the coil current.

47

Fig. 6 Number density distribution of protons around thecoil (d=4.0 m, I=200 kA,Ͳι ൑ ߮ ൑ ͹Ǥͷι)

Fig. 4 Number density distribution of protons around thecoil (d=4.0 m, I=50 kA)

4. Conclusions
3.3. Effects of coil’s diameter
Figure 5 shows the number density distribution of protons
for d=1.0 m and I=200 kA.
The low density region decrease in size with decrease in
the coil’s diameter.

The magnetic shielding could deflect most of the SEPs’
trajectories even if the coil current is relatively low. Not
only the amount of the current but also the coil’s size andthe relative position between the coil and the spacecraft areimportant for the magnetic shielding from SEPs.

References
[1]

[2]

[3]

[4]

Fig. 5 Number density distribution of protons around thecoil (d=1.0 m, I=200 kA)
3.4. Effects of proton’s incident angle
Figure 6 shows the number density distribution when the
protons have variations of the incident angle in the range ofs7.5 deg in Y-direction. The coil diameter and the coilcurrent are d=4.0 m and I=200 kA, respectively.
In this case, some protons can fly to a region behind thecoil without being affected by the magnetic field. Becauseof that, there is the most appropriate position in Y-direction
for the spacecraft.

48

L. W. Townsend, J. W. Wilson, J. L. Shinn, J. E. Nealy,
L. C. Simonsen, “Radiation Protection Effectivenessof a Proposed Magnetic Shielding Concept for
Manned Mars Missions”, SAE Technical Paper,
901343, 1990
Joseph H. King, “Solar proton fluences as observed
during 1966-1972 and as predicted for 1977-1983
space missions”, NASA TM X-70508, 1973
P. Spillantini, F. Taccetti, P. Papini, L. Rossi,“Radiation shielding of spacecraft in mannedinterplanetary flights”, Nuclear Instruments andMethods in Physics Research Section A, Vol.443,
p.254-263, 2000
Tatsuzo Obayashi, “Solar Terrestrial Physics”,
SHOKABO, 1970

Effect of operating condition on solvent extraction of La with PC-88A
Student Number: 08_18048

Name: Kazuma NAKAMURA Supervisor: Ryuichi EGASHIRA, Hiroaki HABAKI
where (RH)2 is extractant dimmer, and  ଷ ሺ ሻଷ is extracted
species. CRH,org is an extractant concentration as dimmer.
Dissolution of La hydroxide in water is written as follows:ሺ ሻଷ ൌ ଷା ൅ ͵ ି
(4)From Eqs (3) and (4), we have Eq (5):(5)
ሺ ሻଷ ൅ ͵ሺ ሻଶ ൌ  ଷ ሺ ሻଷ ൅ ͵ ଶ 
From Eq (5), Kex is expressed by:

1. Introduction
Rare earth metals are mainly used as raw materials for highpurity individual rare earth chemicals. Lanthanide elements
have major applications in magnets, ceramics, electronics,
nuclear technology and so on [1].
Solvent extraction is well known as an effective method forseparation of lanthanides on the industrial scale. Due to thegrowing demand of high purity metals with the developing
high-technology industry, establishing the more effective
operating condition of solvent extraction is needed. 2-ethylhexyl
phosphonic acid mono-2-ethylhexyl ester (PC-88A) is an acidicorganophosphorus extractant. PC-88A has been widely used insolvent extraction of lanthanides industry for the separation andpurification of the metal ions. However in the previous studies
the concentration ranges of rare-earth metals and extractant
were much lower than those used in the industrial scale. The
rare-earth metal concentrations were so low relative to those of
extractant in organic phases that it was assumed that the free
extractant concentration could not be affected by the metal
extraction. Nishihama et al. [2] reported that in the highconcentration region, the extraction reaction was quite different
from those in the low concentration ranges.
In the present work, solvent extraction of La with PC-88A is
studied. La was selected from the rare-earth metals and theextraction equilibrium of La between the organic phases of
PC88A and the aqueous phases in which the proton
concentration was adjusted. Moreover the effects of La
concentration in the system on the extraction equilibrium werealso studied.
2. Experimental
Table1 shows the experimental conditions for distribution
equilibrium measurements. The organic solutions were preparedby diluting PC-88A with solvesso150. Solvesso150 is one of thepetroleum fractions, rich in aromatic hydrocarbons. The
aqueous solution of nitric acid was used as the aqueous phases.
La hydroxide was used as La source.
The solute, aqueous and organic phases were put into 50ml
conical flasks with screw cop, and stirred with magnetic stirrer
for 24h. The two phases were then separated with a separating
funnel and the obtained aqueous solutions were analyzed by
ICP-AES.

‫ܭ‬ୣ୶ ൌ஼ై౗ǡ౗౧ǡబ஽஼ై౗ǡ౥౨ౝ

ାE[-]

0.5 equilibrum
1.0 equilibrium

0.2
0
1E-08
10-8

10-6 0.00001
10-4
CH,aq

10-20.01

100[kmol/m3]

Fig.1 Effect of initial and equilibrium CH,aq on the extraction degree.
CLa,aq,0=0.1 kmol/m3, CRH,org,0=0.5, 1.0 kmol/m3
10.8

CRH,org,0
0.6
E[-]

0.5

1.0

0.4
0.2
0
0

0.05
0.1
CNA,aq.0[kmol/m3]

0.15

Fig.2 Effect of nitric acid concentration on the Extraction degree.
CLa,aq,0=0.1 kmol/m3, CRH,org,0=0.5, 1.0kmol/m3

In this study, reaction is assumed to be as follows [3]:ଷା ൅ ͵ሺ ሻଶ ՞  ଷ ሺ ሻଷ ൅ ͵

0.6
0.4

(2)

஼ై౗ǡ౗౧

0.5 initial
1.0 initial

Distribution ratio, D, is defined by:‫ܦ‬ൌ

CRH,org,0

0.8

(1)

ଵା஽

(6)

1

3. Results and Discussion
3.1 Solvent extraction of La hydroxide
The extraction degree, E, is given by:ൌ஼ౄǡ౗౧ య஼౎ౄǡ౥౨ౝయ

Table2 The effect of PC-88A dissolution in 2.0×10-4m3 water
Organic phase
CH,aq
PC-88A 0.02(kmol/m3)
2.77
2.84
PC-88A 0.02(kmol/m3) + solvesso150 (2.0×10-4m3)

SoluteLa(OH)3
Aqueous phase
Aqueous solution of HNO3
Organic phase
Organic solution (solvesso150) of PC-88A
Mole load relative toaqueous phaseCLa,aq,0[kmol/m3] 0, 0.1,0.05,0.025
Concentration of nitric0~0.175
acid in aqueous phase .
CNA,aq,0 [kmol/m3]
Concentration of PC-88A
in organic phase
CRH ,org,0[kmol/m3]
0.5, 1.0
2.0×10-4
Volume of aqueous phase
Vaq [m3]
3
Volume of organic phase
Vorg [m ]2.0×10-4
Stirring timet [h]2460,000Stirring speedv [h-1]஼ై౗ǡ౥౨ౝ

ൌ‫ܦ‬ൈ

From Eq (3), concentration of free extractant, CRH,org,
expressed by:(7)
CRH,org = CRH,org,0-3CLa,org
The effect of initial and equilibrium H+ concentration, CH,aq,
on the extraction degree, E, is shown in Fig.1. Fig.2 shows theeffect of nitric acid concentration, CNA,aq, on the extraction
degree. All La hydroxide was extracted with aqueous solutionof no nitric acid and the organic phase of CRH,org,0=0.5kmol/m3
could extract all La in case of CLa,aq,0=0.1kmol/m3.
CH,aq increased after the extraction but Eq(5) means that
CH,aq isn’t change before and after the extraction. This change
may be because of PC-88A dissolved in aqueous phase. The
effect of organic phase contact with water is shown in Table2.
This result means that CH,aq is decrease without extraction.

Table1 Experimental condition of La hydroxide extraction‫ܧ‬ൌ஼ై౗ǡ౥౨ౝൈ஼ౄǡ౗౧ య஼ై౗ǡ౗౧ൈ ஼౎ౄǡ౥౨ౝ య

(3)

49

CRH,org,0=0.5 kmol/m3, CLa,aq,0=0.1, 0.05, 0.025 kmol/m3

3.2 Measurement of extraction equilibrium constantFig.3 shows the effect of free extractant concentration on
Kex. When the free extractant concentration was lower than
0.3kmol/m3, Kex was not constant. In the range of
CRH,org>0.3kmol/m3, Kex was almost constant but the value wasaffected by La concentration.
Fig.4 shows the slope analysis method to determine Kex ateach La concentration. Kex could be obtained by the interceptsof the line of which solpe is 3 and pass the all plot. In Fig.4,
Eq(3) was assumed to be able to apply each conditions where
Kex are constant. The obtained Kex was listed in Table3.

From Eq (6) and (7), the distribution ratio at each hydroxide
concentration, could be predicted by CLa,aq,0 and CRH,org,0.
The measured distribution ratios and the predicteddistribution ratios obtained by using of the value of Kex in
Table3 are shown in Fig.5.
The predicted distribution ratio could express the measured
data well when CLa,aq,0= 0.05kmol/m3 and 0.025kmol/m3, butwhen CLa,aq,0=0.1kmol/m3, it cannot express all distribution
ratio .
To express the distribution ratio of CLa,aq,0=0.1kmol/m3 , theextraction reaction was assumed to be Eq (8).
തതതതതതതത
(8)ଷା ൅ ͵ሺሻଶ ൌ തതതതതതതതതതതതതത ଺ ଺ି୫ ൅  ା
Fig.6 shows the slope analysis method to determine m andKex in Eq(8) .

10-10.1
CLa,aq,0Kex[-]-2
100.01

0.1
0.05
0.025

-3
0.001
10

4

3

-4

log(D(CRH,org)-3)

100.0001-5100.00001
0.1

1CRH,org [kmol/m3]

log(D(CRH,org)-3) =-5.68 logCH,aq-7.91

2

1

Fig.3 Effect of free extractant concentration on the extract equilibrium
constant. CRH,org,0=0.5kmol/m3,CLa,aq.0= 0.1,0.05, 0.025 kmol/m3
3.0

0
-2.5

log(D*(CRH,org)-3)

CLa,aq,0
slope = 3

-1.5

-0.5

0.5

log(CH,aq)

Fig.6 Determine of Kex and m by using of the slope analysis
methodCLa,aq,0=0.1 kmol/m3, CRH,org,0=1.0 kmol/m3

0.1

2.0
0.05
0.025

Fig.6 shows that Kex is 10-7.91 and m=5.68. The solid line in
Fig.5 compares the predicted obtained from Fig.4 and Fig.6
with measured data. Fig.5 shows that the prediction line
obtained from Fig.6 (solid line) can express the measured plot
better than that of from Fig.4 (dotted line).

1.0

0.0
-2.5

-2

-1.5

-1

4. Conclusions
Extraction La hydroxide with PC-88A by using of solvent
extraction was achieved. 0.5kmol/m3 PC-88A was appropriate
to extract 0.1kmol/m3 feed La hydroxide and nitric acid for pH
control was unnecessary.
The ratio of free extractant may have relation to theextraction reaction. And concentration of feed La has relation to
the extraction equilibrium constant.

logCH,aqFig.4 Determination of Kex by using of slope analysis method.
CRH,org,0=0.5kmol/m3, C La,aq,0= 0.1,0.05, 0.025 kmol/m3
Table3Extraction equilibrium constants at each feed La concentration
CLa,aq,0 [kmol/m3]
intercepts
Kex[-]
0.1
3.81
1.55×10-4
0.05
3.83
1.48×10-4
0.025
4.18
6.61×10-4

NomenclatureCi= concentration of i
[kmol/m3]
Kex=extrac tio n e q uilib riu m cons ta nt[-]
E=extraction degree[-]D=distribution ratio[-](R H ) 2 = d i me r ic s p e c ie s o f e x t r a c t a n t[k mo l / m 3 ]<Subscripts>
0=aqueous or organic feed solutionaq=aqueous phaseorg=organic phase<Superscripts>=organic phase speciesത
തതത
References[1]S.Radhika,B.Nagaphani Kumar,M.Lakshmi Kantam, B Ramachandra Reddy,
Liquid-Liquid extraction and separation possibilities of heavy and light rare-earths
from phosphoric acid solutions with acidic organophosphorus reagents, Separation
and Purification Technology75(2010)295-302[2]Syouhei Nishihama, Takayuki Hirai, Isao Komasawa, Design of Liquid-Liquid
Extraction Process for Separation of Metal ions, KAGAKU KOGAKURONBUNSHU, 26(2000)497-505[3]Hsiang-Chien Kao, Pei-Shin Yen, Ruey-Shin Juang, Solvent extraction ofLa(Ϫ)and Nd(Ϫ) from nitrate solutions with 2-ethylhexylphosponic acid mono2-ethylhexyl ester, Chemical Engineering journal, 119(2006)167-174

100.00102

CLa,aq,0
0.1
0.05

10.00
10
D[-]

0.025
0.1
0.05

0
1.00
10

0.025
0.1 Eq(10)

10-1
0.10
0

0.02

0.04

0.06

CH,aq [kmol/m3]

Fig.5 Effect of hydrogen ion concentration on the distribution ratio. The
comparison of observed distribution ratio (plot) with predicteddistribution ratio (broken and dotted line).

50㒔ᕷቃ⏺ᒙ࡟࠾ࡅࡿ኱Ẽ஘ὶཬࡧࣇࣛࢵࢡࢫࡢᖺ㛫ࢫ࣌ࢡࢺࣝゎᯒᏛ⡠␒ྕ㸸08-18976 Ặྡ㸸㔝ཱྀῐᾏ ᣦᑟᩍᐁ㸸⚄⏣Ꮫ࡛࠶ࡿ㸬࡞࠾㸪ฟຊࡍࡿࢫ࣌ࢡࢺࣝ࡟㛵ࡋ࡚ࡣ᪥㔝(1977)࡟ࡼࡿᖹ⁥໬ᡭἲࢆ⏝࠸㸪㧗࿘Ἴᩘഃࡢࣀ࢖ࢬ
㝖ཤࢆ⾜ࡗࡓ㸬1㸬ࡣࡌࡵ࡟ ኱Ẽቃ⏺ᒙ஘ὶࡢ࿘ᮇⓗ≉ᛶࡣ㸪ࣇࣛࢵࢡࢫ⟬ᐃࡸࢫࢣ࣮ࣝࢆ▱ࡿ㝿࡟㔜せ࡛࠶ࡾ㸪ᵝࠎ࡞ᆅᙧୖ࣭ሙ
ᡤ࡛ࡢቃ⏺ᒙෆࡢ஘ὶゎᯒࡀୡ⏺୰࡛㢖⦾࡟⾜ࢃࢀ࡚ࡁࡓ㸬ࡑࡢ⤖ᯝ Kaimal(1977)ࡽࡣ㸪࢝ࣥࢨࢫ࡛ࡢᐇ㦂⤖ᯝ࡟ᇶ࡙ࡁ㸪஘ὶሙ࡛ࡢࢫ࣌ࢡࢺࣝᙧࢆᥦ᱌ࡋࡓ㸬ࡲࡓ㸪ࢫ࣌ࢡࢺࣝࢠࣕࢵࣉࡢ᭷↓ࡸ㸪➼᪉ᛶ஘ὶࢆព
࿡ࡍࡿ័ᛶᑠ㡿ᇦࡢᏑᅾࢆ♧ࡍ㸫2/3 ஌๎࡟ᚑ࠺㡿ᇦࡢᏑᅾ࡞࡝ࡶ㸪㏣ᚑࡍࡿ◊✲࡟ࡼࡾ☜ㄆࡉࢀ࡚࠸ࡿ㸬ࡋ࠿ࡋࡇࢀࡽࡢከࡃࡣ㸪ప࿘Ἴཪࡣ▷ᮇ㛫ࡢゎᯒ࡟ࡼࡗ࡚ᚓࡽࢀࡓࡶࡢ࡟㐣ࡂࡎ㸪㧗࿘Ἴ࠿ࡘ㛗ᮇほ ࢹ࣮ࢱࡢゎᯒ࠿ࡽ┤᥋♧ࡋࡓ౛ࡣ㸪㠀ᖖ࡟ᑡ࡞࠸㸬ࡑࢀࡣ㸪ゎᯒ࡟Ⳙ኱࡞ࢧࣥࣉࣝࢹ࣮ࢱࢆせࡍࡿࡇ࡜ࡸ㸪ྲྀᚓࢹ࣮ࢱࡢಙ㢗ᛶ࡜࠸࠺Ⅼ࡟࠾࠸࡚㸪㛗ᮇ㛫(౛࠼ࡤᖺ㛫ࣞ࣋ࣝ)ࡢ㐃⥆ࢹ࣮ࢱࢆྲྀᚓࡍࡿࡇ࡜ࡀᅔ㞴࡛࠶ࡿ࠿ࡽ
࡛࠶ࡿ㸬᭦࡟㸪◁₍࣭ᾏୖ࣭᳃ᯘ➼࡛ࡢ஘ὶほ ࡣ㢖⦾࡟⾜ࢃࢀ࡚ࡁࡓࡀ㸪㒔ᕷ㒊࡟࠾ࡅࡿ஘ὶほ ࡣ㸪ほ⏝ࢱ࣮࣡ࡢタ⨨᮲௳➼ࡢไ⣙࠿ࡽ㸪ୡ⏺ⓗ࡟ぢ࡚ࡶ࡯࡜ࢇ࡝⾜ࢃࢀ࡚࠸࡞࠸࡜࠸࠺⌧≧ࡀ࠶ࡿ㸬 ᮏ◊✲࡛ࡣ㸪㒔ᕷ㒊ᒃఫ༊࡟ᘓ࡚ࡽࢀࡓࠕஂࡀཎࢱ
࣮࣡ࠖࡢほ ࢹ࣮ࢱ(᳃⬥ࡽ㸪2006)ࢆࡶ࡜࡟㸪1 ᖺ㛫ࡢ㛗ᮇࢹ࣮ࢱࢆ⏝࠸㸪㧗࠸ࢧࣥࣉࣜࣥࢢ࿘Ἴᩘ(8Hz)࡛ࡢ
ゎᯒࢆヨࡳࡓ㸬ஶ‫ݔ‬ሺ‫ݐ‬ሻ ൌ ‫ି׬‬ஶ ܺሺ߱ሻ݁ ௜ఠ௧ ݀߱ܵሺ߱ሻ ൌ  ்՜ஶ

͐(1)ଶగ௑ሺఠሻ௑ ‫ כ‬ሺఠሻ்ൌ  ்՜ஶଶగȁ௑ሺఠሻȁమ͐(2)்ࡲࡓ㸪ࢱ࣮࣡ほ ࠿ࡽᚓࡽࢀࡓ᫬⣔ิࢹ࣮ࢱࡣ㸪㝆㞵ࡸࢹ࣮ࢱࡢᅇ཰࡟క࠸Ḟᦆࡀ⏕ࡌࡿࡓࡵ㸪᏶඲࡞ 1ᖺ㛫ࡢ㐃⥆ࢹ࣮ࢱ࡛ࡣ࡞࠸㸬ࡋࡓࡀࡗ࡚඲ࢹ࣮ࢱࡢ࠺
ࡕ㸪᭱ࡶḞᦆࡢᑡ࡞࠸༊㛫(ࢹ࣮ࢱḞᦆ⋡⣙ 6%)ࢆ㑅ᢥࡋࡓୖ࡛㸪ࢹ࣮ࢱࡀḞᦆࡋ࡚࠸ࡿ⟠ᡤ࡟㛵ࡋ࡚ࡢ⿵᏶
ࢆ⾜ࡗࡓ㸬᭦࡟㸪ࡑࢀࡒࢀࡢ஘ὶ⤫ィ㔞࡟㛵ࡋ࡚ࡣ㸪
㢼㏿࡟ࡘ࠸࡚୺᪉ྥኚ᥮࡜ McMillen(1998)࡟ᚑ࠸ഴᗘ
⿵ṇ㸪Ẽ ࡟ࡘ࠸࡚ࡣ Liu ࡽ(2001)ࢆཧ⪃࡟ᶓ㢼㸪‵ᗘ⿵ṇࢆ⾜࠸㸪஧㓟໬Ⅳ⣲⃰ᗘ࡟ࡣᐦᗘ⿵ṇࢆ⾜ࡗࡓ㸬4㸬⤖ᯝཬࡧ⪃ᐹ(1)ࢹ࣮ࢱḞᦆ㒊ࡢ⿵᏶࡟㛵ࡋ࡚ ࢹ࣮ࢱḞᦆ㒊࡛ࡢ⿵᏶࡛ࡣ㸪Ḟᦆ⟠ᡤ࡟ᑐࡋ࡚௨ୗ
ࡢ 3 ✀ࡢ᪉ἲࢆヨࡳࡓ㸬(a)඲య࡛ࡢᖹᆒ್ࢆ௦ධࡍࡿ㸬(b)୍᪥๓ࡢࢹ࣮ࢱࢆ฼⏝ࡍࡿ㸬(c)Ḟᦆ༊㛫࡟┦ᙜࡍࡿ㛗ࡉࡢ┤๓ࡢࢹ࣮ࢱࢆ㸪㐃⥆ⓗ࡟฼⏝ࡍࡿ㸬
ࡇࡇ࡛㸪ヨࡳࡓࡇࢀࡽࡢ⿵᏶᪉ἲ࡟ࡼࡿࢫ࣌ࢡࢺࣝ
ᙧ࡬ࡢᙳ㡪ᗘࢆ⾲ࡋࡓࡶࡢࢆḟࡢᅗ 1 ࡟♧ࡍ㸬ྛ⿵᏶᪉ἲࢆ⏝࠸ࡓሙྜࡢ୺ὶ᪉ྥ㢼㏿ࡢࢫ࣌ࢡࢺࣝゎᯒ⤖ᯝ࡛ẚ㍑ࡋ࡚࠸ࡿࡀ㸪≉࡟኱ࡁ࡞㐪࠸ࡣぢࡽࢀ࡞࠸ࡇ
࡜ࡀศ࠿ࡿ㸬ࡋ࠿ࡋ㸪㐃⥆ᛶࡢ☜ಖ࡜࠸࠺Ⅼ࡟❧ࡕ㸪(c)ࡢ᪉ἲ࡟ࡼࡿ⿵᏶ᡭἲࢆ௚ࡢゎᯒ࡛౑⏝ࡋࡓ㸬

2㸬◊✲ࡢ┠ⓗᮏ◊✲࡛ࡣ௨ୗࡢ 3 Ⅼࢆ┠ⓗ࡜ࡍࡿ㸬(1)㧗࿘Ἴ(8Hz)࠿ࡘ㛗࿘ᮇ(1 ᖺ)ࡢ㐃⥆ࢹ࣮ࢱゎᯒ࡟ࡼࡾ㸪័ᛶᑠ㡿ᇦ࠿ࡽ⥲ほሙࡢࢫࢣ࣮ࣝࡢ㐃⥆ࡋࡓࢫ࣌ࢡࢺࣝࢆ♧ࡋ㸪ࢫ࣌ࢡࢺࣝࢠࣕࢵࣉࡢ᭷↓㸪័ᛶᑠ㡿ᇦࡢᏑᅾ࡞࡝࡟ࡘ࠸࡚┤᥋ⓗ࡞᳨ウࢆ⾜࠺㸬(2)ࡇࢀࡲ࡛࡟ゎᯒࡉࢀࡓࡇ࡜ࡢ࡞࠸㸪㒔ᕷ㒊ᒃఫ༊࡛ࡢ㛗ᮇ஘ὶほ ࢹ࣮ࢱ࡟ᇶ࡙ࡃࢫ࣌ࢡࢺࣝᙧࢆ♧ࡋ㸪ࡑࡢ≉ᚩࢆぢฟࡍ࡜࡜ࡶ࡟㸪᪤ ࡢ◊✲࡜ࡢẚ㍑ࢆ⾜࠺㸬(3)㧗ᛶ⬟ࡢほ ჾ(㉥እ⥺࣮࢜ࣉࣥࣃࢫ࢔ࢼࣛ࢖ࢨ࣮)࡟ࡼࡗ࡚ᚓࡽࢀࡓ஧㓟໬Ⅳ⣲⃰ᗘ➼ࡢࢫ࣮࢝ࣛ㔞ࡢ㛗ᮇࢫ࣌ࢡࢺࣝࢆ⟬ฟࡋ㸪ࢫ࣮࢝ࣛ㔞ࡑࡢࡶࡢࡢ஘ὶ≉ᛶࢆⓎぢࡍࡿ㸬

10䝟䝽䞊䝇䝨䜽䝖䝹f䞉S(f)[(m/s)2 䞉Hz]

1

3㸬◊✲ᴫせ ᮾி㒔኱⏣༊ஂࡀཎ࡟タ⨨ࡉࢀࡓほ ࢱ࣮࡛࣡ྲྀᚓ
ࡉࢀࡓ㛗ᮇ(2001㹼2007 ᖺ)᫬⣔ิࢹ࣮ࢱࢆ฼⏝ࡋ㸪పᒙఫᏯᆅ(ᖹᆒ㧗ࡉ 7.3m)࡟࠾ࡅࡿ኱Ẽ஘ὶࡢ㢼㏿(࣋ࢡࢺࣝ)㸪ࢫ࣮࢝ࣛኚືࢫ࣌ࢡࢺࣝࢆ⟬ฟࡍࡿ㸬ゎᯒ࡟ࡣ㸪୺ὶ᪉ྥ㢼㏿(u)㸪୺ὶࡢỈᖹ┤ゅ᪉ྥ㢼㏿(v)㸪㖄
┤᪉ྥ㢼㏿(w)㸪Ẽ (t)㸪஧㓟໬Ⅳ⣲⃰ᗘ(c)㸪ཬࡧỈ⵨Ẽ⃰ᗘ(q)ࡢ 6 ࡘࡢࢹ࣮ࢱࢆ౑⏝ࡋࡓ㸬ࡇࢀࡽࡣ㸪ࢱ࣮࣡࡟࠾ࡅࡿᆅୖ 21㸪29m ᆅⅬࡢ 2 ⟠ᡤ࠿ࡽࡑࢀࡒࢀᚓ
ࡽࢀࡓࡶࡢ࡛࠶ࡿ㸬8Hz ࡛ྲྀᚓࡉࢀࡓ 1 ᖺ㛫ࡢࢹ࣮ࢱ(ࢹ࣮ࢱᩘ 252,288,000 ಶ)ࢆ㸪ᮾᕤ኱ᡤ᭷ࡢࢫ࣮ࣃ࣮ࢥ
ࣥࣆ࣮ࣗࢱ TSUBAME2.0 ࢆ⏝࠸࡚ FFT ₇⟬ࡋ㸪ࣃ࣮࣡ࢫ࣌ࢡࢺࣝཬࡧࢥࢫ࣌ࢡࢺࣝࢆฟຊࡉࡏࡓ㸬ゎᯒཎ⌮ࡣ㸪ᘧ(1)㸪(2)࡛♧ࡉࢀࡿ㏻ࡾ࡛࠶ࡿ㸬‫ݔ‬ሺ‫ݐ‬ሻࢆධຊࡍ
ࡿ᫬⣔ิࢹ࣮ࢱ࡜ࡋ㸪ܺሺ߱ሻࡣࡑࡢ」⣲ࣇ࣮࢚ࣜᡂศ࡛
࠶ࡿ㸬ࢫ࣌ࢡࢺࣝܵሺ߱ሻࡣ㸪ࡑࡢᐇᩘ㒊ศࢆ࡜ࡗࡓࡶࡢ

0.1

0.01

(a)ᖹᆒ್䜢௦ධ(b)1᪥๓䝕䞊䝍฼⏝(c)㐃⥆ⓗ䛻䝁䝢䞊䛧䛶฼⏝

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-020.001
1.E-01
1.E+00

1.E+01

࿘Ἴᩘf[Hz]ᅗ  ྛ⿵᏶᪉ἲ࡟ࡼࡿࢫ࣌ࢡࢺࣝᙧ࡬ࡢᙳ㡪(2)㢼㏿ኚືࡢࣃ࣮࣡ࢫ࣌ࢡࢺࣝ
ᅗ 2 ࡣ㸪u,v,w 㢼㏿ࡢᖺ㛫ࣃ࣮࣡ࢫ࣌ࢡࢺ࡛ࣝ࠶ࡿ㸬࠸ࡎࢀࡢ㢼㏿ࢫ࣌ࢡࢺࣝ࡟࠾࠸࡚ࡶ㸪㧗࿘Ἴᩘഃࡢ஘
ὶᇦ࡟ࣆ࣮ࢡࢆ᭷ࡋ࡚࠸ࡿࡇ࡜ࡀศ࠿ࡿ㸬ࡇࡢࣆ࣮ࢡ
ࡢ఩⨨ࡣ㸪ᶓ㍈ࢆ↓ḟඖ໬࿘Ἴᩘ࡜ࡋ㸪⦪㍈ࡢࢫ࣌ࢡࢺࣝࢆ u*ࡢ 2 ஌࡛๭ࡿࡇ࡜࡛つ᱁໬ࢆ⾜࠺࡜㸪Kaimalࡢ◊✲⤖ᯝ࡟ᇶ࡙ࡃᬑ㐢ᙧ࡜ࡢ୍⮴ࡀ☜ㄆ࡛ࡁࡓ㸬ࡲࡓ㸪ࡇࡢ஘ὶᇦ࡛ࡣ Kolmogorov(1941)ࡢḟඖⓗ࡞⪃ᐹ㸪ཬࡧᚋࡢᩘࠎࡢᐇ㦂⤖ᯝ࡟ࡼࡾ㸪័ᛶᑠ㡿ᇦࡢᏑᅾࢆ
♧ࡍ㸫2/3 ஌๎࡟ᚑ࠺㡿ᇦࡢᏑᅾࡀ♧ࡉࢀࡓࡀ㸪ࡇࡢ㡿
51

   ᇦࡢᏑᅾࡶࡣࡗࡁࡾ࡜☜ㄆ࡛ࡁࡿ㸬ࡘࡲࡾ㸪
Kolmogorov ࡢ⌮ㄽ࡟ᚑ࠺ᚤ⣽࡞➼᪉ᛶ஘ὶ㡿ᇦ࠿ࡽ㸪
1 ᖺ࿘ᮇࡢప࿘Ἴ㡿ᇦࡲ࡛ࢆ㸪㐃⥆ࡋࡓࢫ࣌ࢡࢺࣝᙧ
≧࡟ࡼࡗ࡚♧ࡍࡇ࡜ࡀ࡛ࡁࡓࡇ࡜࡟࡞ࡿ㸬ຍ࠼࡚㸪࠸ࡎࢀࡢ㢼㏿ኚື࡟࠾࠸࡚ࡶ㸪᫂☜࡞ࢫ࣌ࢡࢺࣝࢠࣕࢵࣉࡀ 3.010-4Hz(⣙ 1 ᫬㛫)㏆ഐ࡟࠾࠸࡚
Ꮡᅾࡋ࡚࠾ࡾ㸪ᮏ◊✲࡟࠾࠸࡚ࡑࡢᏑᅾࡀ☜ᅛࡓࡿࡶࡢ࡜ࡋ࡚ᐇドࡉࢀࡓ㸬᭦࡟㸪࿘ᮇ⣙ 1 ᪥࡟࠿ࡅ࡚ࡢ㸪ࢫ࣌ࢡࢺࣝࢠࣕࢵࣉࡼࡾࡶప࿘Ἴᩘᇦ࡛ࡣ㸪෌ࡧ㸫2/3
஌๎࡟ᚑ࠺ࠕ␲ఝ័ᛶᑠ㡿ᇦࠖ࡜࠸࠺ࢫ࣌ࢡࢺࣝᙧ≧ࡀ☜ㄆ࡛ࡁࡿ㸬ࡇࡢ⤖ᯝࡣ㸪1 ᪥⛬ᗘࡢࢫࢣ࣮ࣝࡢ
ࡀ㸪1 ᫬㛫⛬ᗘࡢࢫࢣ࣮ࣝࡲ࡛࢝ࢫࢣ࣮ࢻࢲ࢘ࣥࡋ࡚
࠸ࡿࡇ࡜ࢆព࿡ࡋ࡚࠸ࡿ㸬ࡇࡢ␲ఝ័ᛶᑠ㡿ᇦ࡟࠾࠸࡚ࡣ㸪༢⣧࡞࢝ࢫࢣ࣮ࢻࢲ࢘ࣥ࡟ᚑࢃࡎ࡟㸪࢚ࢿࣝࢠ࣮ࡢ༟㉺ࢆ♧ࡍ㗦࠸ࢫ࣌ࢡࢺࣝࣛ࢖ࣥࢆᙧᡂࡍࡿ࿘ᮇ
ࡀᏑᅾࡋ࡚࠸ࡿࡀ㸪ࡑࢀࡀ 1 ᪥㸪༙᪥㸪1/4 ᪥࿘ᮇ࡛࠶
ࡿ㸬ࡇࡢ஦ᐇࡼࡾ㸪᪥࿘ᮇኚືࡢ࢚ࢿࣝࢠ࣮ᙉࡉࡀ኱
ࡁࡃാࡃࡇ࡜ࡀศ࠿ࡿࡀ㸪᭱ࡶ༟㉺ࡍࡿ࿘ᮇࡣ༙᪥࡜
࡞ࡗ࡚࠸ࡿ㸬ࡇ࠺ࡋࡓ࿘ᮇࡢ༟㉺せᅉ࡟ࡣ㸪ᾏ㝣㢼➼ࡢᙳ㡪ࡀ⪃࠼ࡽࢀࡿ㸬(4)ࣇࣛࢵࢡࢫࡢࢥࢫ࣌ࢡࢺࣝ
 ᅗ 4 ࡣ㸪㐠ື㔞㸪㢧⇕㸪஧㓟໬Ⅳ⣲㸪Ỉ⵨Ẽࣇࣛࢵࢡࢫࡢࢥࢫ࣌ࢡࢺࣝࢆ㸪ࡑࢀࡒࢀᖺ㛫ࡢඹศᩓ್࡟ࡼࡗ࡚つ᱁໬ࡋࡓࡶࡢ࡛࠶ࡿ㸬ࡇࡢࢥࢫ࣌ࢡࢺࣝᙧ࡟࠾
࠸࡚ࡶ㸪஘ὶᇦ࡟࠾࠸࡚័ᛶᑠ㡿ᇦࡢᏑᅾࢆ♧ࡍഴࡁ
Ѹ4/3 ஌࡟ᚑ࠺㡿ᇦࡀᏑᅾࡋ࡚࠸ࡿࡇ࡜ࡀศ࠿ࡾ㸪ࡲࡓ࿘ᮇ⣙ 1 ᫬㛫㏆ഐ࡟࠾ࡅࡿ᫂☜࡞ࢫ࣌ࢡࢺࣝࢠࣕࢵࣉ
ࡢᏑᅾࡶ☜ㄆ࡛ࡁࡿ㸬ࢥࢫ࣌ࢡࢺࣝ࡟࠾ࡅࡿࢫ࣌ࢡࢺ
ࣝࢠࣕࢵࣉࡣ㸪ࡇࢀࡲ࡛ࡶ⣙ 1 ᫬㛫⛬ᗘࡢ࿘Ἴᩘᖏ࡟
Ꮡᅾࡋ࡚࠸ࡿ࡜ࡉࢀ㸪 ┦㛵ἲ࡜࠸ࡗࡓࣇࣛࢵࢡࢫィࡢ≀⌮ⓗ⫼ᬒ࡜ࡋ࡚❧⬮ࡋ࡚࠸ࡿࡀ㸪ࡇࢀࢆ㛗ᮇ㛫࡟࠾ࡅࡿほ ࢹ࣮ࢱ࠿ࡽ♧ࡋࡓࡶࡢࡣ➹⪅ࡢ▱ࡿ㝈ࡾᏑᅾࡋ࡞࠸㸬ࡑࡢཎᅉ࡜ࡋ࡚ࡣ㸪㛗ᮇ㛫࡟Ώࡿࢫ࣮࢝ࣛ⃰ᗘ ᐃࡀᅔ㞴࡛㸪࡞࠾࠿ࡘࡇࢀࡽࡢ᫬⣔ิኚືࢆ࡜ࡽ࠼ࡿ ᐃᡭἲࡀẚ㍑ⓗ᪂ࡋ࠸ࡶࡢ࡛࠶ࡿ࡜࠸࠺ࡇ
࡜ࡀᣲࡆࡽࢀࡿ㸬࡞࠾㸪ᮏゎᯒ࡛ࡢࢠࣕࢵࣉࡢᏑᅾࡀ
♧ࡉࢀࡓࡇ࡜ࡣ㸪ࣇࣛࢵࢡࢫ࡟ᐤ୚ࡍࡿ ࡜ࡋ࡚㸪⥲ほⓗ࡞ኚື ࡲ࡛ࡶ⪃៖ࡍࡿᚲせᛶࡀ⏕ࡌᚓࡿࡇ࡜ࢆ
ព࿡ࡋ࡚࠸ࡿ㸬

1.E+02䠉2/3

1.E+01

1.E+00䠉2/3

1.E-01䠉2/3
1.E-021᪥

1.E-07

1.E-06

1.E-05

1.E-04

1.E-02

1.E-03

1.E-04

1.E-08

1.E-03

1.E-02

1.E-01

1.E+00䝟䝽䞊䝇䝨䜽䝖䝹f䞉S(f)1.E-04

1.E-03

1.E-02

1.E-01

1.E-01

1.E+00

1.E-05
1.E+01

J.C.Kaimal(1977) 㸸 Horizontal Velocity Spectra in an
Unstable Surface Layer
Liu et al (2001)㸸New equations for sonic temperature
variance and buoyancy heat flux with an omnidirectional
sonic anemometer᪥㔝ᖿ㞝(1977)㸸ࢫ࣌ࢡࢺࣝゎᯒ᳃⬥ுࡽ(2006)㸸ఫᏯ⾤࡟࠾ࡅࡿ෤Ꮨኪ㛫ࡢᒁᡤⓗ෭Ẽỿ㝆࡜ࢫ࣮࢝ࣛ⃰ᗘࣉࣟࣇ࢓࢖ࣝࡢᙧᡂᶵᵓ㸪Ỉᕤㄽᩥ㞟㸪➨ 50
ᕳ

1.E-01

1.E-04

1.E+00

1.E-02ཧ⪃ᩥ⊩

1.E-05

1.E-05

1.E-03

1.E+00

1.E-03

1.E-06

1.E-04

1.E+01

1.E+02

1.E-02

1.E-07

1.E-055㸬⤖ㄽ 㧗࿘Ἴᩘࡢࢧࣥࣉࣜࣥࢢ㛫㝸࡛㛗ᮇ༊㛫ࡢゎᯒࢆ⾜ࡗࡓࡇ࡜࡛㸪ࢫ࣌ࢡࢺࣝࢠࣕࢵࣉ㸪័ᛶᑠ㡿ᇦ㸪␲ఝ័ᛶᑠ㡿ᇦࡢᏑᅾࢆ᫂☜࡟♧ࡍࡇ࡜ࡀ࡛ࡁࡓ㸬ࡲࡓ㸪㒔ᕷ㒊࡟࠾࠸࡚ࡶ㸪஘ὶሙࡢࢫ࣌ࢡࢺࣝ≉ᛶࡣ᪤Ꮡࡢ
௚ࡢ᥋ᆅᒙ࠿ࡽᚓࡽࢀࡓ⤖ᯝ࡜㢮ఝࡋࡓࡶࡢ࡜࡞ࡗࡓ㸬᭦࡟㸪ࢫ࣮࢝ࣛ㔞⮬యࡢኚື࡟╔┠ࡋ㸪஧㓟໬Ⅳ⣲⃰
ᗘཬࡧỈ⵨Ẽ⃰ᗘࡢ㛗ᮇⓗ࡞ኚື≉ᛶࢆぢฟࡍࡇ࡜ࡀ
࡛ࡁࡓ㸬ࣇࣛࢵࢡࢫࡢࢥࢫ࣌ࢡࢺࣝ࠿ࡽࡣ㸪័ᛶᑠ㡿ᇦࡢᏑᅾࡢ☜ㄆ㸪ཬࡧ␲ఝ័ᛶᑠ㡿ᇦࡢᏑᅾࡢྍ⬟ᛶࢆ♧ࡍࡇ࡜ࡀ࡛ࡁࡓ㸬(3)ࢫ࣮࢝ࣛኚືࡢࣃ࣮࣡ࢫ࣌ࢡࢺࣝ
 ᅗ 3 ࡣ㸪Ẽ 㸪஧㓟໬Ⅳ⣲⃰ᗘ㸪Ỉ⵨Ẽ⃰ᗘࡢᖺ㛫ኚືࢫ࣌ࢡࢺ࡛ࣝ࠶ࡿ㸬Ẽ ࡢࢫ࣌ࢡࢺࣝ࡟ࡣ㸪ࢃࡎ࠿࡟ࢫ࣌ࢡࢺࣝࢠࣕࢵࣉࡢᏑᅾࡀぢཷࡅࡽࢀࡿࡀ㸪஧㓟໬Ⅳ⣲⃰ᗘཬࡧỈ⵨Ẽ⃰ᗘ࡟ࡣ᫂☜࡞ࢫ࣌ࢡࢺࣝࢠࣕࢵࣉࡀぢᙜࡓࡽ࡞࠸㸬ࡇࢀࡣ஧㓟໬Ⅳ⣲㸪Ỉ⵨Ẽ࡞࡝ࡢࢫ࣮࢝ࣛኚື㔞ࡣ㸪ࢩ࢔࣮ࡸᾋຊ➼ࡢᙳ㡪࡟ࡼࡿ஘ὶኚື࡬ࡢ౫Ꮡᛶࡀᑠࡉࡃ㸪ᖺᖹᆒⓗ࡟ぢࡿ࡜୍ᵝ࡟Ꮡᅾࡋ࡚࠸ࡿ࡜࠸࠺ࡇ࡜ࢆព࿡ࡋ࡚࠸ࡿ㸬

䠉2/3

1.E-06ᅗ  つ᱁໬ࡋࡓࣇࣛࢵࢡࢫࡢࢥࢫ࣌ࢡࢺࣝ

࿘Ἴᩘf[Hz]䠉2/3

1.E-07

࿘Ἴᩘf[Hz]

1.E-05
1.E+01ᅗ  Ỉᖹ࣭㖄┤᪉ྥ㢼㏿ኚືࣃ࣮࣡ࢫ࣌ࢡࢺࣝ

Ẽ
஧㓟໬Ⅳ⣲
Ỉ⵨Ẽ

1.E+00

1.E-01

1.E-041᫬㛫

1.E+01−4/3

1.E-03

1.E-08

1.E-08−4/3

䝁䝇䝨䜽䝖䝹f䞉S(f)䝟䝽䞊䝇䝨䜽䝖䝹f䞉S(f)[(m/s)2䞉Hz]

U
V
W

㐠ື㔞䝣䝷䝑䜽䝇
㢧⇕䝣䝷䝑䜽䝇
஧㓟໬Ⅳ⣲䝣䝷䝑䜽䝇Ỉ⵨Ẽ䝣䝷䝑䜽䝇

1.E-06
1.E+01

࿘Ἴᩘf[Hz]ᅗ  ࢫ࣮࢝ࣛኚືࡢࣃ࣮࣡ࢫ࣌ࢡࢺࣝ

52ࢳࣕࢿࣝࢧ࢘ࣥࢹ࢕ࣥࢢ࡬ࡢ㹇㸭㹏ࢫ࣮࢟ࣗࡢᙳ㡪Ꮫ⡠␒ྕ㸸08B20559

1ᣦᑟᩍဨ㸸㧗⏣ ₶୍, 㔠࣑ࣥࢯࢡẶྡ㸸ࣇ࢓࣒ࣦ࢓ࣥࣇ࢙

ࡁࡿ. ࡲࡎ, ᅗ 1 ࡢࡼ࠺࡟ཷಙ࣮࣋ࢫࣂࣥࢻᅇ㊰࡛⏕ࡌࡿ◊✲⫼ᬒIQ ࢫ࣮࢟ࣗࢆࣔࢹࣝ໬ࡍࡿ. ⌮᝿ⓗ࡟ࡣྠ┦ᡂศ xI (t)࡜┤⾜ᡂศ xQ (t) ࡣ 90 ◦ ఩┦ࡀ␗࡞ࡿࡀ, ࣁ࣮ࢻ࢙࢘࢔

ᦠᖏ㟁ヰ࡟௦⾲ࡉࢀࡿ⛣ື㏻ಙࢩࢫࢸ࣒ࡣ㏆ᖺᛴ㏿
࡟ᬑཬࡋ, ࢫ࣐࣮ࢺࣇ࢛ࣥ࡞࡝ࡢ➃ᮎࡢ㧗㏿໬࡟ࡼࡗ࡚,ࡢ୙᏶඲ᛶ࡟ࡼࡾㄗᕪ (IQ ࢫ࣮࢟ࣗ) ࡀ⏕ࡌ, ⤖ᯝ࡜ࡋࢹ࣮ࢱࡢࡸࡾྲྀࡾࡣ㡢ኌ࠿ࡽ, ⏬ീ, ື⏬࡬࡜ከᵝ໬ࡋ࡚࡚ࢧ࢘ࣥࢲࡢ ᐃ⢭ᗘࢆຎ໬ࡉࡏࡿ. ᗈᖏᇦࡢఏᦙ㊰ࢆࡁ࡚࠸ࡿ. ᑗ᮶ࡢ⛣ື㏻ಙࢩࢫࢸ࣒࡛ࡣ, ࡉࡽ࡞ࡿ㏻ಙ

ᐃࡍࡿࡓࡵ࡟ࡣ,㏿ᗘࡢ㧗㏿໬ࡀ㔜せ࡞ㄢ㢟࡜࡞ࡗ࡚࠸ࡿ. ࡋ࠿ࡋ࡞ࡀࡽ

ᐃᖏᇦෆ୍࡛ᵝ࡞ࢫ࣌ࢡࢺࣝࢆᣢ

ࡘಙྕࢆ㏦ಙࡋ, ཷಙഃ࡛┦㛵ฎ⌮ࢆ⾜࠺ࡇ࡜࡛ఏᦙ㊰Ⰻዲ࡞ఏᦙ≉ᛶ࠿ࡽ, ࡇࢀࡲ࡛ᖜᗈࡃ⏝࠸ࡽࢀ࡚ࡁࡓ 6࡟ࡼࡿṍࡳᡂศࢆ᥎ᐃࡍࡿ. ࡇࡇ࡛, ᘧ (1) ࡢࡼ࠺࡟ N
GHz ௨ୗࡢ࿘Ἴᩘᖏࡣ᪤Ꮡࡢ↓⥺㏻ಙࢩࢫࢸ࣒࡟ࡼࡗ
ಶࡢ」⣲ṇᘻἼࡢ࿴࡛⾲ࡉࢀࡿᗈᖏᇦ࣐ࣝࢳࢺ࣮ࣥಙྕ
࡚࡯ࡰ༨᭷ࡉࢀ࡚࠾ࡾ, ࡉࡽ࡟㏻ಙ㏿ᗘࡢ㧗㏿໬ࢆᐇ⌧ࢆ⏝࠸ࡿ.
ࡍࡿࡓࡵ࡟ࡣ 6 GHz ௨ୖࡢ㧗࠸࿘Ἴᩘᖏᇦࢆά⏝ࡍࡿ
N/2−1
ࡇ࡜ࡀᚲせ࡜⪃࠼ࡽࢀࡿ. ࡲࡓ, 」ᩘࡢ࢔ࣥࢸࢼࢆ㏦ཷಙ

(t)
+
jx
(t)
=
Ψm ej2πΔF mt
(1)
x(t)
=
x
I
Q
ᶵ࡛⏝࠸࡚✵㛫ⓗ࡟᝟ሗࢆከ㔜ࡋ࡚ఏ㏦ࢆ⾜࠺ MIMO
m=−N/2(Multi-Input Multi-Output) ఏ㏦ࡢᑟධࡶ, ࿘Ἴᩘ㈨※ࡢ☜ಖࢆేࡏ࡚ᚲせ୙ྍḞ࡛࠶ࡿ [1].

2k2 π(k = 0, 1, . . . , N − 1)N
ࡣ Newman ఩┦ [3],ΔF = 2B/N ,2B ࡣ㏦ಙ࿘Ἴᩘᖏᇦ
ࡓࡔࡋ,Ψm ≡ Ψk = ejφk ,φk =ᮏ◊✲ࡢ┠ⓗᖜ࡛࠶ࡿ. ཷಙᶵ࡛ࡣ࢔ࢼࣟࢢࢹࢪࢱࣝኚ᥮ჾ (ADC)᪂ࡓ࡞࿘Ἴᩘᖏࢆᑗ᮶ࡢ↓⥺㏻ಙࢩࢫࢸ࣒ࡢࡓࡵ࡟㛤࡟ࡼࡗ࡚ࢧࣥࣉࣜࣥࢢ㛫㝸 Ts ࡛ᶆᮏ໬ࡉࢀࡓࢹࢪࢱࣝ

ᣅࡍࡿ࡟ࡣࠊ᪂࿘Ἴᩘᖏ࡛ࡢ㟁Ἴఏᦙ≉ᛶࢆ᫂ࡽ࠿࡟ࡍ
ࡿࡇ࡜ࡣ୙ྍḞ࡛࠶ࡾ, 6GHz ௨ୖࡢ㧗࣐࢖ࢡࣟἼᖏ࡟

ಙྕࢆࡔ࣮࡝ࢹ࢕ࢫࢡ࡟ಖᏑࡉࢀࡿ. ࡇࡇ࡛ࡣ, ㏦ཷಙᐃ, ࣔࢹࣝ໬ࡍࡿᚲせࡀ࠶ࡿ. ᮏᶵࡢ࣮࣋ࢫࣂࣥࢻ┤⤖ࡢ⌮᝿ⓗ࡞ᵓᡂ࡛,IQ ࡢࢫ࣮࢟ࣗ

◊✲࡛ࡣ, [2] ࡛㛤Ⓨࡋ࡚࠸ࡿ ᐃ⿦⨨ࡢ⢭ᗘຎ໬せᅉ࡜ࡢᙳ㡪ࢆホ౯ࡍࡿ. ఏ㐩㛵ᩘ࡬ࡢ᥎ᐃㄗᕪࡣṇつ໬ᖹᆒ࡞ࡾ, ࣮࣋ࢫࣂࣥࢻᅇ㊰࡛⏕ࡌࡿ IQ ࢫ࣮࢟ࣗࢆࣔࢹࣝ

஧஌ㄗᕪ (NMSE) ࡛ᐃ⩏ࡍࡿ.NMSE ࡢゎᯒ್ࡣ࠾࠸࡚ఏᦙ㊰≉ᛶࢆ໬ࡋ,ᐃ⢭ᗘ࡬ࡢᙳ㡪ࢆᐃ㔞ⓗ࡟ホ౯ࡍࡿ. ࡉࡽ࡟ゎᯒ⤖ᯝ࡟ᇶ࡙ࡁ, ఏᦙ㊰

1
NMSE =
Nᐃ⿦⨨࡜ࡋ࡚せồࡉࢀࡿ IQ ࢫ࣮࢟ࣗࡢ⢭ᗘࢆ᫂ࡽ࠿࡟ࡍࡿ.

3

IQ ࢫ࣮࢟ࣗ࡟ࡼࡿ ᐃㄗᕪࡢᐃᘧ໬

3.1

ಙྕࣔࢹࣝ

k=0

Ĥk − Hk

N −1
k=0

2

(2)

2

Hk 

ࡇࡇ࡛,Hk ࡜ Ĥk ࡣ➨ k ࢺ࣮ࣥࡢ IQ ࢫ࣮࢟ࣗࡀ࡞࠸࡜
࠶ࡿሙྜࡢఏ㐩㛵ᩘ࡛࠶ࡿ.

NMSE =MIMO ࢳࣕࢿࣝࢧ࢘ࣥࢲࡣ」ᩘࡢ㏦ཷಙᅇ㊰࠿ࡽᵓᡂࡉࢀࡿࡀ, ᮏ◊✲࡛ࡣ 1 ᑐ 1 ࡢ㏦ཷಙ⣔ࡢࡳࢆ⪃࠼, ࣮࣋

2Nࢫࣂࣥࢻᅇ㊰ࡢ୙᏶඲ᛶ࡟ࡘ࠸࡚㆟ㄽࡍࡿ. ࡞࠾, ホ౯⤖ᯝࡣ MIMO ⿦⨨඲య࡜ࡋ࡚ࡢᙳ㡪ホ౯࡟Ᏻ᫆࡟ᣑᙇ࡛



 
 


 

N
−1

k=0

kμτ /Ts
1 − cos 2π
N


sin

k2 π
N

2 
(3)

μτ ࡣࢫ࣮࢟ࣗࡢᖹᆒ್࡛࠶ࡿ. ᘧ (3) ࢆ⏝࠸ࡿࡇ࡜࡛, ࢫ࣮࢟ࣗᙳ㡪ୗ࡛ࡢ ᐃㄗᕪࡢホ౯ࡀྍ⬟࡜࡞ࡿ.
3.2



N −1

ࢩ࣑࣮ࣗࣞࢩࣙࣥ࡜⤖ᯝ

IQ ࢫ࣮࢟ࣗ τ ࡀṇつศᕸ N (μτ , στ2 ) ࡟ᚑ࠺࡜௬ᐃࡋ,
ࢩ࣑࣮ࣗࣞࢩࣙࣥ࡟ࡼࡾ,ᐃㄗᕪࢆホ౯ࡋࡓ.

ᅗ 3a ࡣࢫ࣮࢟ࣗࡀ࡞࠸࡜ࡁࡢ⌮᝿ⓗ࡞ࢫ࣌ࢡࢺ࡛ࣝ,ᅗ 3b ࡣࢫ࣮࢟ࣗࡀ࠶ࡿ࡜ࡁ࡛ࠊࢫ࣌ࢡࢺࣝࡀ኱ࡁࡃṍࢇ࡛࠸ࡿ. ᅗ 3 ࡣࢩ࣑࣮ࣗࣞࢩࣙࣥ (2) ࡜ゎᯒ್ (3) ࡢᅗ 1: ཷಙ࣮࣋ࢫࣂࣥࢻ࡟࠾ࡅࡿ IQ ࢫ࣮࢟ࣗࣔࢹࣝNMSE ࡢẚ㍑࡛࠶ࡾ, ࢫ࣮࢟ࣗࡢ೫ᕪࡢᙳ㡪ࢆ♧ࡋ࡚࠸
ࡿ. ⤖ᯝ࠿ࡽࢃ࠿ࡿࡼ࠺࡟,στ < μτ ࡢሙྜࠊNMSE ࡣ࡯
531⾲ 1: ࢩ࣑࣮ࣗࣞࢩࣙࣥ᮲௳

⾲ 2: ࢳࣕࢿࣝ 1 ࡢࢫ࣮࢟ࣗࡢ೫ᕪ [ps]

SNR

50 dB

SG-Rx

Tx-Rx

Tx (᥎ᐃ)ࢧࣥࣉࣜࣥࢢ࿘Ἴᩘ (Fs )400 MHz

5.01

16.5

15.7

ࢺ࣮ࣥᩘ (N )2048࿘Ἴᩘᖏᇦᖜ (B)400 MHz

1.5

1.5

1

1

0.5

0.5

0.1

0.1

0.08

0.08

0.06

0.06

0.04

0.04

0.02

0.02

0

0
−50

0
[ps]

50

(a) Rx
0
−200

−100
0
100
Frequency[MHz]

200

0
−200

−100
0
100
Frequency[MHz]







⪃࠼ࡽࢀ㸪Tx ࡜ Rx ࢆ᥋⥆ࡋ࡚

















(a) ᶆ‽೫ᕪ στ = 10 ps





















ࢫ࣮࢟ࣗࡢ☜⋡ศᕸ࠿ࡽ Tx ࡢ I/Q ࢫ࣮࢟ࣗࢆ᥎ᐃࡍࡿ(b) ᶆ‽೫ᕪ στ = 100 psࡇ࡜ࡀ࡛ࡁࡿ㸬ᅗ 4a ࡼ࠺࡟,Rx ࡢࢫ࣮࢟ࣗࡢࡣṇつศᕸ࡜ࡼࡃ୍⮴ᅗ 3: NMSE ࡜ࢫ࣮࢟ࣗࡢ㛵ಀࡋ,Tx ࡢ᪉ࡀ Rx ࡟ẚ࡭࡚ࢫ࣮࢟ࣗࡢኚືࡀ኱ࡁ࠸ࡇ࡜
ࡀࢃ࠿ࡗࡓ㸬ࡇࢀࡣ㸪Tx ࡜ Rx ࡢࢡࣟࢵࢡ౪⤥⣔ࡢᅇ㊰ࡰ୍ᐃࡢ್࡜࡞ࡿ.μτ ࡣࣁ࣮ࢻ࢙࢘࢔ࡢㄪᩚ࡟ࡼࡾ, ࠶ࡿᵓᡂࡢ㐪࠸࡟ࡼࡿࡶࡢ࡜⪃࠼ࡽࢀࡿࡀ㸪ヲ⣽࡟ࡘ࠸࡚ࡣ

⛬ᗘᰯṇࡍࡿࡇ࡜ࡀྍ⬟࡛࠶ࡿࡀࠊστ ࡣಙྕ※ࡢᛶ⬟௒ᚋ᳨ウࡀᚲせ࡛࠶ࡿ㸬ࡲࡓ㸪ᮏࢩࢫࢸ࣒ࡢ IQ ࢫ࣮࡛࢟ࣗỴࡲࡾㄪᩚ࡛ࡁ࡞࠸. ࡇࡢࡇ࡜࠿ࡽ, ࢫ࣮࢟ࣗࡢᰯṇ࡟ᰯṇ࡟࠾ࡅࡿࡋࡁ࠸್ࡣ㸪ᖹᆒ್࡛ 16.5ps ࡜ࡍࢀࡤࡼᚲせ࡜ࡉࢀࡿ⢭ᗘࡣࢫ࣮࢟ࣗࡢᶆ‽೫ᕪ࡜ྠ⛬ᗘࡢ࣮࢜࠸ࡇ࡜ࡀ᫂ࡽ࠿࡜࡞ࡗࡓ㸬ࢲ࣮࡛࠶ࡿࡇ࡜ࡀุࡿ.

4ࢩࢫࢸ࣒ࡢホ౯

4.1

ࢫ࣮࢟ࣗࡢ

ᐃࡋࡓ I/Q ࢫ࣮࢟ࣗࡣ୧⪅ࡢ࿴࡛⾲ࡉࢀࡿࡢ࡛㸪ࡑࡢ☜⋡ศᕸ࡜ Rx ࡢ I/Qᐃ࡛ࡁ࡞࠸ࡓࡵ,Tx-Rx ┤⤖࡟ࡼࡿࢫ࣮࢟ࣗࡢ ᐃ⤖ᯝ࠿ࡽ Tx ࡢ I/Q ࢫ࣮࢟ࣗࢆ᥎ᐃࡍࡿ.
Tx ࡜ Rx ࡢ I/Q ࢫ࣮࢟ࣗࡣࡑࢀࡒࢀ⊂❧࡟ኚືࡍࡿ࡜








ࢫ࣮࢟ࣗࡣ┤᥋࡟
 











ᐃࡋࡓࢫ

࣮࢟ࣗࡣ Rx ࡢᙳ㡪࡛࠶ࡿ࡜⪃࠼ࡽࢀࡿ㏦ಙ (Tx) ࡢ I/Q
 (b) Txಙྕ࡟ࢫ࣮࢟ࣗࡣ↓ど࡛ࡁࡿ࡜௬ᐃࡍࡿ࡜,





50

ᅗ 4: ࢳࣕࢿࣝ 1 I/Q ࢫ࣮࢟ࣗࡢศᕸ

ᅗ 2: ఏ㐩㛵ᩘࡢ᥎ᐃ⤖ᯝ


0
[ps]

200

(b) μτ = 500 ps ࡜στ = 10 ps ̭(a) ࢫ࣮࢟ࣗࡀ࡞࠸ሙྜ

−50

5

⤖ㄽ
ᮏ◊✲࡛ࡣ㸪ࢳࣕࢿࣝࢧ࢘ࣥࢲ࡟࠾ࡅࡿ ᐃ⢭ᗘຎ໬ᐃཎ⌮ࡢせᅉ࡜࡞ࡿ IQ ࢫ࣮࢟ࣗ࡟ࡘ࠸᳨࡚ウࡋ㸪 ᐃㄗᕪࢆI ࢳࣕࢿࣝ࡜ Q ࢳࣕࢿࣝ࡟ྠࡌ࿘Ἴᩘ f0 ṇᘻἼࢆධࢫ࣮࢟ࣗࡢ㛵ᩘ࡜ࡋ࡚ᐃᘧ໬ࡋࡓ㸬ࡲࡓ㸪ᐇࢩࢫࢸ࣒࡛

ຊࡍࡿ࡜, ࣁ࣮ࢻ࢙࢘࢔ࡢࡢ୙ᆒ୍ᛶ࡟ࡼࡾฟຊࡢಙྕ

⏕ࡌࡿࢫ࣮࢟ࣗࢆ ᐃࡋ㸪࣮࣋ࢫࣂࣥࢻᅇ㊰ࡢࢫ࣮࢟ࣗ

࡟ࢫ࣮࢟ࣗࡀ⏕ࡌࡿ (ᅗ 1). ࢫ࣮࢟ࣗ࡟ࡼࡿ఩┦ᕪࢆồᰯṇ࡟࠾ࡅࡿࡋࡁ࠸್࡟ࡘ࠸࡚᫂ࡽ࠿࡟ࡋࡓ㸬ࡵࡿࡓࡵ࡟,ADC ฟຊ xI [n], xQ [n] ࡟ᑐࡋ࡚㞳ᩓࣄࣝ࣋ࣝࢺኚ᥮ [4] ࢆ㐺⏝ࡋ㸪」⣲ಙྕ zI [n], zQ [n] ࢆ⏕ᡂࡍཧ⪃ᩥ⊩ࡿ㸬ࡇࡢ」⣲ಙྕࢆ⏝࠸࡚㸪ḟࡢᘧ࡛ࢫ࣮࢟ࣗࢆồࡵࡿ㸬 [1] G. J. Foschini, “Layered space-time architecture for wireless



z [n]

communication in a fading environment when using multiple antennas,” Bell Labs. Tech. J., vol. 1, no. 2, pp. 41–51,
1996.



N
Imag zQI [n]
1 1 


τ = τQ − τI =arctanz [n]2πf0 N n=1
Real Q[2] Takada, Y. Konishi, M. Kim, and M. Ghoraishi, “Development of 11GHz MIMO Channel Sounder with 400MHz
Bandwidth using Software Radio Architecture, ” COST
2100 TD(10) 12058, 2010.

(4)

zI [n]

ࡓࡔࡋ,N ࡣධຊಙྕࡢ 1 ࿘ᮇᙜࡓࡾࡢࢧࣥࣉࣝᩘ࡛࠶ࡿ.

4.2[3] S. Boyd, “Multitone signals with low crest factor,” IEEE
Trans. Circuits Syst., vol. 33, no. 10, pp. 1018–1022, Oct.
1986.

ᐃ࡜⤖ᯝ

[4] S. Hahn, Hilbert Transforms in Signal Processing. Norwell,
MA: Artech House,1996.

ᇶ‽ࡢ㏦ಙಙྕࡣಙྕⓎಙჾ (SG) ࢆ౑⏝ࡋ, ཷಙ (Rx)࣮࣋ࢫࣂࣥࢻ࡟࠾ࡅࡿ I/Q ࡢࢫ࣮࢟ࣗࢆ ᐃࡋࡓ.SG ࡢ
54
2

INFLUENCE OF CIRCUMFERENTIAL STRAIN OF DISK SURFACEON PIN-ON-DISK ADHESION CONTACT
Student Number: 08_21493

Name: DOOYOUNG BAEKSupervisor: KUNIO TAKAHASHI[mm] and Young’s modulus ‫ ܧ‬ൌ ͷǤͳ ൈ ͳͲହ [Pa] was used as
the elastic plane which is set on GCD, and a Plano-convex
spherical lens of diameter φ30 [mm] and radius of curvature
R=207.6 [mm] was used as the rigid sphere which is set on
LFB in fig 2(a). The displacement was applied vertically bystage in fig 2(c), and the radius of contact area during theexperiment process was observed using an optical
microscope in fig 2(d), and the force was measured by abalance in fig 2(e). The experiment was done in an acrylic
box to avoid the effect of dust and moisture, and indoor
temperature was fixed at 22°C. First step of experimental
procedure is loading the sphere to elastic plane up to 60μm
by 1μm step. Second is unloading the sphere by 1μm step toseparate from elastic plane. Third is adjusting circumferential
strain on disk surface ߝ௥ to 2.5%, 5%, 10% and repeat theexperiment from first step. And, wait 30 seconds both on
loading and unloading process between 1μm steps.

1. Introduction
Applications using the adhesion contact between solids arestudied in various fields including joining technology andmicro manipulating technology. And, the surface state ofelastic body which forms an interface with rigid body is themost important variable in the adhesion phenomenon,
because “work of adhesion” (the energy change from surface
to the interface per unit area) is used to analyze the adhesion
phenomena. In those studies, the influence of deformation ofelastic surface on adhesion mechanism is not yet clarified
theoretically and experimentally. As practical problems on
using adhesion contact for application [3], we discuss theinfluence of deformation by adding circumferential strain of
disk surface on Pin-On-Disk adhesion contact using the JKR
theory [1] in this study.

2. Pin-On-Disk adhesion contact modelFig 1 shows a schematic illustration of the model used inthis study based on JKR theory [1].

Figure 1 Schematic illustration of JKR theory.
ࢿ࢘ is circumferential strain of disk surface.

Figure 2 Schematic illustration of the experimental system.

The relation between the equilibrium state of total energy‫୲ܧ‬୭୲ୟ୪ and the contact radius ܽ can be expressed as‫୲ܧ‬୭୲ୟ୪ ൌͺʹ ͺߨ‫ܧ‬οߛ ଻
‫ܧ‬
ඨ
ܽହ െ
ܽଶ ൅ ߨܽଶ οߛሺͳሻͳͷ ሺͳ െ ‫ ݒ‬ଶ ሻܴଶ͵ܴ ͳ െ ‫ ݒ‬ଶwhere ‫ ܧ‬is Young’s modulus, ‫ ݒ‬is the Poisson ratio, ܴ issphere’s radius, a is radius of contact area, ߜ is displacementof rigid sphere, ‫ ܨ‬is adhesion force and οߛ is the energychange from surface to the interface per unit area. The
relation between the equilibrium state of displacementߜ,force ‫ ܨ‬and contact radius ܽ can also be expressed as

Figure 3 Photographs of Generator of circumferential strain of
disk surface(fig 2(b) : GCD)and Lens fixing base(fig 2(a) : LFB).

4. Experimental result and discussion
In the JKR theory, the static condition is assumed. But,
discussing the influence on dynamic condition is also needed,because of using elastic adhesion contact on various
applications. Therefore, in this study, we carried out theexperiment on dynamic condition as the first objective.
As preliminary to discuss the influence of circumferential
strain from the experimental result, we need to remove someexperimental variables. Experiment result of fig 4 shows
dominant trend of occurrence of hysteresis between loading
and unloading process is due to the instability caused by the
dynamic factor on this experiment. First of all, from change
of forces for 30 seconds at each point (a) to (h) in fig 4ձ and
fig 4ղ, experiment process is carried out before the force bestable. Also, contact radius constant area (c) to (e) in fig 4ճ
and fig 4մ is observed for the energy gradient in the

ܽଶ
Ͷ‫ܽܧ‬
ʹ‫ܽܧ‬
ʹߨሺͳ െ ‫ ݒ‬ଶ ሻοߛܽߜ ൌ ൜ͳ ൅ൠ െ ൜ͳ ൅
ൠඨ
ሺʹሻ
ଶ
ଶ
͵݇ሺͳ െ ‫ ݒ‬ሻ݇ሺͳ െ ‫ ݒ‬ሻ
‫ܧ‬
ܴ

‫ܨ‬ൌ

ʹߨ‫ ߛ߂ܧ‬ଷͶ‫ܧ‬ܽଷ െ ʹඨܽଶ ሺ͵ሻͳ െ ‫ݒ‬ଶ͵ሺͳ െ ‫ ݒ‬ଶ ሻܴ

by using the JKR theory [1].

3. Experiment
Experiments to measure the force between the elastic plane
and the rigid sphere were carried out by controlling thedisplacement using experimental system as shown in fig 2.
Generator of circumferential strain of disk surface (GCD)
was manufactured for this experiment is shown as fig 2(b)and fig 3. A semitransparent silicon rubber sheet of 60×60×10

55

ձ

(a)

ղ

(b)

ճ

մ

Figure 5 Experimental result of relation between force-displacement
when circumferential strain is applied ࢿ࢘ =0%, 2.5%, 5%, 10% (a),Its theoretical line when Δγ=0.036, 0.034, 0.030, 0.028 (b)Figure 4 ձRelation between loading/unloading and timedependence of each (a)~(h) point, ղforce-displacement,ճdisplacement-contact area, մforce-contact area.

5. Conclusion
The influence of deformation of elastic surface on adhesion
mechanism is confirmed by applying circumferential strain
on disk surface of pin-on-disk experimentally. There are trendof decreasing of work of adhesion as increasing thecircumferential strain. But, unstable conditions of energy
both loading and unloading process occurs due to the
dynamic elements of the experiment. From perspective of theapplications using elastic adhesion contact phenomenon in
manipulation, surface strain (or deformation) of elastic bodyis determined to be almost negligible in dynamic condition,
because the effects of surface strain is less than the impact ofother factors such as surface washing method, material ofelastic body and etc. However, this study needs to be more
researched in terms of explain this phenomenon fully bytheoretically in static condition.

measured values are nearly flat. Therefore, it was consideredthat the hysteresis is observed commonly in this experiment.
The experimental results of applying circumferential strain
on disk surface (ߝ௥ =0%, 2.5%, 5% and 10%) are shown in fig
5(a). We can calculate the parameter work of adhesion whichis the important parameter for identify the influence of
circumferential strain by energy condition of experimental
data and [2]. And work of adhesion is decrease as increasing
the circumferential strain of disk surface as shown in legendof fig 5(a), οɀ଴Ψ ൌ ͲǤͲ͵͸, οɀଶǤହΨ ൌ ͲǤͲ͵Ͷ, οɀହΨ ൌ ͲǤͲ͵Ͳ
and οɀଵ଴Ψ ൌ ͲǤͲʹͺ . The relation between force anddisplacement by using the calculated value of work ofadhesion and equation (2), (3) is shown in fig 5(b). As aresult of loading process, regularity of decreasing of work ofadhesion as increasing the circumferential strain of disk
surface is observed in experimental result on fig 5(a). On theother hand, irregularities are observed in unloading process ofexperimental result on fig 5(a). In addition, maximum
adhesion force in unloading process of experimental result iscompletely irrelevant as shown in fig 5(a). Overall, hysteresisof experimental result is observed in common as analyzed byusing circumferential strain on disk surface ߝ௥ =0% of fig 4.
And, there is no relationship of maximum adhesion force on
fig 5(a) with calculated value of work of adhesion on fig 5(b)completely. But, in the macroscopic point of view,experimental result of fig 5(a) behaves similarly regardless ofthe circumferential strain on disk surface.

References[1] K. L. Johnson, K. Kendall and A. D. Roberts, Proc. R. Soc. London Ser. A 324,
301-313 (1971)[2] K. L. Johnson, Tribology Int. 31, 413-418 (1998)[3] Y. Sekiguchi, Tokyo Tech, Dept. I. D. E., Master Thesis (2010)

56

Optimum Duty Ratio of Boost Switching Regulator for Charging
Tiny Electric Energy to Capacitor
Student Number: 08 22021

1

Name: Yoshito HONDA

Introduction      Various kinds of energy sources exist. But some ofthe sources are hard to be used, because these energy
is very small. So, the energy is wasted or unused. In
this study, tiny electric energy is deﬁned as electric energy which has too small open circuit voltage or too
small short circuit current to use directly. A capacitor
is used as a storage devise,since the internal resistanceof the capacitor is usually lower than that of rechargeable battery [1]. In order to utilize tiny electric energy,
a boost switching regulator is required(Fig.1) to accumulate it [2].












Fig. 2: Equivalent Circuit
this time is i1 . During the switch is on 2, the energy isreleased to accumulate in the capacitor and the currentof this time is i2 . T1 and T2 are the switching period
on 1 and 2, respectively, and the switching cycle T isgiven by T1 + T2 = T . The duty ratio of the switchingregulator is deﬁned as d˜ = T2 /T . The gain is deﬁned asg̃ = Vout /Vin . r̃ = R2 /R1 is the normalized resistance.
T̃ = R1 T /L is the normalized switching duty ratio.
Considering the boosting process, the voltage ofthe capacitor increases very slowly. So, the shape ofthe current approaches to a saturated shape as shownin Fig.3. The current can be expressed as



1−g̃
1 − eβR1r̃ − 1Vin
Vin
i1 =e− L t +
,
(1)
α+β
R1
1−e
R1


 Fig. 1: Boost switching regulator
In this study, the converter is modeled to obtain analytical solution. The optimum duty ratio which maximizes the stored energy into the capacitor per unit
time is obtained from the analytical solution of theconverter. The relation between the optimum duty ratio and the bounder of the applicable area of the model
is discussed.

2Supervisor: Kunio TAKAHASHI


Vin
i2 =
R1



1−g̃
r̃ − 1(1 − eα )e−r̃T̃ +α − er̃αR2e− L t +Vin − Vout
,
R2

(2)



when α = − 1 − d˜ T̃ and β = −r̃d˜T̃ .

Model of Switching Regulator

In this study, in order to adjust all scale of the energy
storage system, a rough model of switching regulator
is constructed. The power source is assumed to havethe linear I-V characteristics with internal resistanceRP . In order to obtain the analytical solution, theinductor is assumed as a resister RL with inductanceL, the diode is assumed as a resister RD and the FET
is assumed as a switch with resistance RS , respectively.
R1 and R2 are given by R1 = RP + RL + RS and R2 =RP + RL + RD . Vin is the open circuit voltage of thepower source. For the purpose of the accumulation andthe utilization of the tiny energy, capacitance is needed
to be enough large. So, the capacitor can be assumed
as buttery with a constant voltage Vout . Because theenergy eﬃciency is not considered in this study, theenergy loss of the FET is not taken into the calculation.
Then, the model is shown in Fig.2.
During the switch is on 1, the current increases andthe energy is stored in the inductor and the current of















Fig. 3: Switching Cycle and Current
The characteristics such as Vin and the circuit parameters such as L, R, etc... cannot be changed in theoperation. Vout increases with increasing charge in thecapacitor during the operation. Therefore, the gain
is a given parameter. The control parameter should
be controlled to obtain the eﬃcient accumulation of
the tiny electric energy. Because the Switching period
should be deﬁned considering energy loss of the FET
and resisters, the switching cycle cannot be optimize inthis study. So, only the duty ratio is the controllable
parameter.
57

3

Optimum Duty Ratio

4

Comparing the Conditions

In the equivalent circuit, the diode is assumed to
be a resister. The reverse current might ﬂow to the T
inductor when the voltage of the capacitor grows. Theq=i2 dt(3) existence of the reverse current makes a failure of theT1equivalent circuit because the reverse current cannot
ﬂow to the inductor. The boundary condition which
The optimum duty ratio can be obtained by maxithe reverse current does not ﬂow to inductor ismizing,
The charge accumulated in the capacitor is

q
T

=



⎧ 
˜
˜
⎨ 1 1 − e−(1−d)T̃
1 − e−r̃dT̃

Vin
R2 ⎩ T̃˜

i1 (0) = i2 (T ) > 0.

From Eq.8, the boundary condition is obtained as˜

1 − e−(1−d+r̃d)T̃˜− (g̃ − 1) d .(4)g̃ − 1
1+
r̃

(8)

g̃ <

r̃eβ (1 − eα )+ 1.
1 − eβ(9)From Eq.9, the applicable area of the model in which
the reverse current does not ﬂow is shown in Fig.5.

The optimum duty ratio d˜ should be chosen so that



=0

(5)








  




∂ d˜

    ∂ Tq

is satisﬁed. Eq.5 leads toαβα+β+r̃e2α+β −eα+2βr̃ −e +r̃e +2(1−r̃)e(1−eα+β )2g̃ = 1 +αβα+β +r̃e2α+β −eα+2β ,r̃ − −e +r̃e +2(1−r̃)e(1−eα+β )2

(6)









Eq.5 is only a necessary condition of the optimum duty ratio. In order to verify whether conditionof Eq.6 is maximizing condition or minimizing condition, the sign of ∂ 2 Tq /∂ d˜2 should be veriﬁed. Since ∂ 2 Tq /∂ d˜2 is always negative when











 

Fig. 5: Boundary Condition of approximation

In order to operate the FET without the failure of
the equivalent circuit, the optimum condition is neededg̃ > 1 − r̃(7) to satisfy Eq.9. The optimum condition is comparedwith the boundary condition of the model in order tois satisﬁed. The gain obtained by Eq.6 is always verify the relation between both conditions. Then it issatisﬁed Eq.7. So, Eq.6 shows the most eﬃcient duty made clear that the optimum condition always satisﬁes
ratio as a function of the switching period, the circuit the boundary condition.
parameters and the gain. Eq.6 is plotted in Fig.4. Fig.4
Conclusionshows the relation between gain and optimum duty ra- 5
tio. The gain g̃ increases with increasing the voltage
The optimum condition of the duty ratio and the1of the capacitor. When the gain is less than 1+r̃, cabounderof the applicable area of the model are obpacitor should be connected the power source. Whentainedasfunctions of the switching period and the cir1
the gain is larger than 1+r̃ , the switching regulator is
cuitparameters.
Comparing both conditions, the optistarted to operation and the duty ratio is chosen acmum
duty
ratio
alwayssatisﬁes the applicable area ofcording to the voltage of the capacitor.
the model. The optimum duty ratio without the failureof the equivalent circuit is obtained.






  

References    [1] G. Walden, J. Stepan and C. Mikolajczak, 2011,”Safety Considerations when Designing Portable
Electronics with Electric Double-Layer Capacitors(Supercapacitors)”, IEEE Symp. Product compliance Engineering, 1-5.















 



[2] Otome Keisuke, Eﬀects of conditions of use on theenergy eﬃciency of boost switching regulator, master thesis, (2010)



Fig. 4: The Optimum Duty Ratio of Equivalent Circuit
58

Wideband IQ Imbalance Compensation Techniqueusing Spectrum Analyzer
Student Number:08B23061

1

Name:Yuta Maruichi

Introduction

Supervisor: Jun-ichi Takada, Minseok Kim
xI (t)New communication systems are constantly being developed, as demand for various new wireless applications continues to increase rapidly. Thus, super high bit-rate mobile systems are strongly desired. However, considering therecent explosion of mobile Internet usage and congestionof conventional frequency spectrum (below 3 GHz), it will
be necessary to explore new frequency spectrum which canaccommodate systems with wider bandwidth than current
ones. MIMO (multiple-input multiple-output) technology
has been considered to realize the required performance byincreasing spectrum efﬁciency.
When MIMO systems using higher frequencies andwideband signals are utilized, it is necessary to evaluate
the channel capacity. For this purpose, implementation ofa MIMO channel sounder is in progress [1]. To measure
the channel response correctly using the channel sounder,
system calibration is very important. This research focuseson IQ imbalance compensation for quadrature modulator in
the RF circuit.

ΔI

cos(2πfct)z (t)π/2+ θ
1+εxQ (t)

ΔQ

Figure 1: IQ imbalance model of quadrature modulator.

the output signal can be ideally generated. That shows if
the model parameters can be estimated properly, the IQ imbalance can be easily eliminated using digital pre-distortion
on the original transmit signals. By using the spectrum analyzer, the power spectrum of image distortion and carrierleakage can be obtained. From the information of ampli2 Narrowband IQ Imbalance Com- tude of each component, it is possible to estimate the error
parameters using the algorithm which is proposed in [2].
pensation Method
Then, the output signal can be ideally generated. In thisA slight discrepancy in the quadrature modulator causes
thesis, the model parameter is optimized to suppress the folan imbalance between the in-phase and quadrature compolowing values
nents, named IQ imbalance. It should be sufﬁciently compensated because it causes image (mirror frequency) distorE[zei (t)](ε2 + 2ε + 2) − 2(1 + ε) cos θ, (3)tion and generates carrier leakage. By now, lots of com- LIRR = E[z(t)] =4
pensation methods for IQ imbalance have been intensively
studied.
which denotes the image power reduction ratio (IRR) , andAssuming that the frequency characteristic of each cirΔ2I + Δ2Q
cuit component is negligible, the IQ imbalance in quadraE[zec (t)]
=
,
(4)
L
=
CRR
ture modulator can be simply modeled as Fig. 1, where εE[z(t)]
A2x
denotes the amplitude difference, θ denotes the phase offset
or quadrature skew of the quadrature oscillator branches, which denotes the carrier leakage power reduction ratio
and ΔI and ΔQ denote the DC offset components for I and (CRR) after ε and θ are compensated. Ax is the amplitude
Q branches respectively [3]. A simpler method using only of the input signal.
NMSE (Normalized Mean Square Value) is used to estispectrum measurement with a spectrum analyzer to get themate
the effect of IQ imbalance. From the equaltions above,
amplitude of signal is used, because it is a handy and efﬁit
canbe derived that NMSE corresponds to LIRR .
cient method providing sufﬁcient accuracy.
From the model, distorted output signal can be written as
z̃(t)

=

(Ew(t))T (x(t) + Δ)=z(t) + zei (t) + zec (t),3(1)

Wideband IQ Imbalance Compensation Method

By using the method above, the IQ imbalance can be compensatedfor any speciﬁc frequency, but it is valid only forwhere z(t), zei (t), zec (t) denote ideal output, image distora
narrowband
system. If it is used in a wideband system,
tion, carrier leakage respectively, and E denote error matrix
the
whole
band
can not be compensated properly. Fig.2of ε and θ, w(t) denote the career wave, x(t) denote baseshowstheresultofnarrowband compensation for wideband
band wave, and Δ denote DC offset. In the equation (1), if
signal(-200MHz
to0MHz) that has been compensated at
the Baseband wave have already distorted as
f0 =6.25MHz. It can be seen as from the ﬁgure that the IRRx (t) = (E−T x(t) − Δ,(2) level increases as frequency is further away from 6.25MHz.
59

−20

−10

whole bands
4 sub−bands
16 sub−bands
64 sub−bands

−30

−20

−40
[dBc]

−30

−50

IRR

−40
−50

L

Power Spectrum[dBm]

0

−70

−60
−70

−60

−200

−100

0

100

−80

200

Frequency[MHz]

−90
−200

Figure 2: Wideband signal after narrowband compensation(f0 = 6.25MHz)

0100
Frequency[MHz]200

Figure 3: LIRR vs. Frequency

It is because ε and θ that has been estimated at any speciﬁcfrequency may not be the optimum value at different frequencies. Thus, it is necessary to alter this simple narrowband model into wideband model to perform compensation
over multiple frequencies.
To compensate all frequencies and totally decrease theIRR level, ε and θ can be regarded as ε(f ) and θ(f ), soerror matrix E also becomes E(f ). In (2), the variable istime and the signal is compensated in the time domain, but
the variable of the parameters here are frequency, so it isnecessary to use Fourier transform and compensate in thefrequency domain. Thus, distorted signal in the frequency
domain can be written asX (f ) = {E(f )T }−1 X(f ),−100Power Spectrum[dBm]

0
−10
−20
−30
−40
−50
−60
−70

−200

−100

0

100

200

Frequency[MHz]Figure 4: Wideband signal after wideband compensation(number of sub-bands, N = 16 ).

(5)and the distorted signal becomesx (t) = F −1 [X (f )] − Δ,

−3

10

(6)

where F −1 denotes the operator of inverse Fourier transform.
Error matrix is measured at center frequency of each subband individually by dividing the target frequency band into
several number of sub-bands. For narrowband testing, theIRR level can be decreased as shown in Fig.3. Fig.4 shows
one of the results of wideband testing using wideband compensation method. It is concluded that the wideband IQ imbalance can be compensated by using the proposed method.
Finally, narrowband testing and wideband testing arecompared in Fig.5. In wideband testing, it includes the effect of intermodulation, so the NMSE of wideband testing
is worse than narrowband testing.

4

NMSE(narrowband testing)
NMSE(wideband testing)−4

NMSE(L

IRR

)

10

−5

10

−6

10

−7

10

1

2

4
8
16
32
Number of sub−bands

64

Figure 5: NMSE vs. Number of sub-bandsof The Ministry of Internal Affairs and Communications,Japan.

Conclusion

This thesis proposed an automatic wideband IQ imbalance compensation technique for quadrature modulator using spectrum measurement with a spectrum analyzer. The
method is efﬁcient to reduce the effect of frequency characteristics which occur in wideband systems by dividing
whole band into several sub-bands and compensating at
each sub-band .

References[1] Y. Konishi, M. Kim, M. Ghoraishi, J. Takada, S. Suyama and H.
Suzuki, “Channel Sounding Technique using MIMO Software Radio Architecture,” Proc. European Conference on Antennas andPropagation (EuCAP), Apr. 2011.
[2] M. Kim, Y. Konishi, J. Takada, B. Gao, “IQ Imbalance Compensation Technique for Quadrature Modulator by Single-Tone Testing,”
submitted to IEICE Trans. Commun.
[3] D. Tandur and M. Moonen, “Joint Compensation of OFDM
Frequency-Selective Transmitter and Receiver IQ Imbalance”,
EURASIP J. Wirel. Commun. Netw. (2007.)

Acknowledgment
This work was partly supported by “The research and development project for expansion of radio spectrum resources”
60

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62

RITemperature Distribution Change by Point Contact Current
Student Number: 08B26958

Name: Kazuki WAKABAYASHI

Supervisor: Kunio TAKAHASHI

at t (> τ ) is given as (2).

1 Introduction

In industrial world, the Joule heat generation is used to join1dδq(r1 )(r1 − r)2
exp −dδT (r, r1 , t, τ ) =materials, such as resistance welding technology, but it can damCρd (4πD(t − τ ))3/2
4D(t − τ )(2)
age the contact point of electro devices, such as relays in highThe
energysourceofthissystemiscurrent.
The
current
densityfrequency application.
A bunch of researches are carried out to understand the heat distribution of the system to which current 2I[A] is applied to22generation[1]∼[6] . In the electro devices investigation, researches origin is expressed as i(r1 ) = 2I/4π|r1 | = I/2π|r1 | . There-−3
are carried out to investigate temperature distribution of the de- fore, instantaneous heat input dδq(r1 )[J m ] at r1 is given as
vices for preventing melting[2] [3] . In the welding technology, follows,2I
researches are carried out to investigate the melting condition [4]dδq(r1 ) = ρ
δtdv
(3)
2
2π|r1 |
[5]
. For those, temperature distribution change is important toestimate if material melts because it is possible to estimate if ma- where ρ[Ω m] is resistivity, δt is differential time, dv is differenterial melts to compare the temperature with the malting temper- tial volume. Temperature distribution change is obtained by substituting Eq. (3) for Eq. (2) and integrating it for space r1 ≥ 0
ature of a material.

In order to get temperature distribution change, numerical anal- and for time t ≤ theat . However the result diverges due to theyses using FEM( Finite Element Method) are carried out to ob- singular point of heat input at the origin.
tain the temperature distribution change inside a material[6] under 2.3 An approximation for removal of the singular point
In this system, current density distribution i(r1 ) is proportional1
tosingular point is unclear. Around a singular point, the energy is
r1 2 , so it diverges at the origin. In fact, there is an area at theconcentrated and it has large affection to temperature. Further- contact between electrode and solid and current density has ﬁnite
more, the result can not be applied to the system in which differ- values. Therefore, this model is not appropriate as a model of thethe point contact of two materials. However, the treatment of aent material is used or different scale is settled. Then, the temper- heat generation.
An contact area a is deﬁned as shown in Fig. 2. In the outature distribution change which is available for various systems
sideof semi-sphere whose radius is a, the current density is prois needed. In this research, we focus on obtaining an analytical1solution of temperature distribution change which is useful for portional to r1 2 since it is not affected by the existence of thecontact area. Inside of the semi-sphere, current density seems toestimation of the melting condition.

have complex distribution. Even though, it is reported that the

2 Derivation of theoretical equation of temperature current density has distribution which has peak at r = a [6] , indistribution change by point contact current
this research, the purpose is to roughly estimate the melting con-

2.1 Semi-inﬁnite solid model
dition. Therefore, a simple approximation that current density
A semi-inﬁnite solid system to which current ﬂows from ainside r ≤ a is constant is introduced. The constant value ispoint is considered as a model. The heat which occurs at r1 propset to obtain a upper/lower limit of temperature and the limits isagates to r by heat diffusion and reﬂection at surface.
compared with melting point for estimation of melting. Fig. 1
In the calculation, an inﬁnite solid system to which current
shows two ways of approximations:
2I[A] is applied is introduced because the sum of heat increase at
In order to obtain the lower limit, an approximation(1) current
r in the inﬁnite system is equivalence to that in the semi-inﬁnite
density distribution inside r ≤ a is the value of current density at
system to which current I[A] is applied.
r = a, is introduced. In this approximation, less energy amount
Heat loss from surface to atmosphere is assumed negligible
is considered than input energy so that it provides us a lower limit
because the loss is generally small compared with temperature
increase by heat propagations in the solid. Since phase transformation is not considered, latent heat is assumed negligible. Even
though materials are not homogeneous because of surface oxida-  tion and others, material constants( speciﬁc heat, density, resistivity, thermal diffusivity) are assumed homogeneous to investigate
a simple system, and not to change during the heat generation.
2.2 Heat equation
Heat equation is∂T (x, y, z, t)= DΔ T (x, y, z, t)∂t(1)

where Δ is Laplace operator. When a quantity of heat dδq(r1 )[J] is deposited instantaneously at r1 at t = τ , the solution at r
63

Fig.1 An approximation ofcurrent distribution in r ≤ a

Fig.2 Current
with contact area

distribution

energy so that it provides us the rough upper limit of tempera-a4DD̃ = a2 /t
heat

= 100

0.25

1

0.2
0.15

2

0.1
0.05

T̃ (r̃, t̃) =

0.35

1

1.5

0.25

r̃ = 01.5

24DD̃ = a2 /t
heat

= 100

1

1

0.5

2

0
0

0.10

0.15
1.10

0.1
0.05

0.01

2.00
0.5

0.5

1

1

1.5

Fig.5 Temperaturechangewith time in the approximation(2) (D̃ = 100)

1.5

2

2

T̃ (r̃, t̃) =T (r̃,t̃,a,theat )−T0

1.5

2

1

t

a

d

4D
D̃ = a2 /t
heat

= 100
1

0.5

1.01

0.10

0.01
1.10
2.00

0
0

0.5

1

1.5

2

Fig.6 Temperaturechangewith distance from origin in theapproximation (2) (D̃ = 100)(6) [2] Bor-Jeng Wang Chi H Leung, Anthony Lee. Thermal modeling of electrical contacts in switches and relays. IEEE Trans.

3.1 Temperature Distribution Change
Fig. 3,6 shows the result of Eq. 5,6 with approximation(1)(2)

on Compo. Pack. and Manuf. Technol., Part A, Vol. 19, No. 3,
pp. 346–352, 1996.
4D
where D̃ = a2 /t= 100. Note that D̃ is deﬁned by thermalheat[3] Daniel Hyman and Mehran Mehregany. Contact physics
diffusivity D, contact area a, heat input time theat .
of gold microcontacts for mems switches. IEEE Trans. on
Fig. 3,5 shows the temperature change with time. Temperature
Compo. Pack. and Manuf. Technol., Vol. 22, No. 3, pp. 357–
is monotonically increasing with time in the period of heat input.
364, 1999.
When heat input ﬁnishes (t̃ = 1), temperature changes as dif[4] Y. YOSHIOKA. Research on the welding phenomena of aferential coefﬁcient is noncontinuous. This is because heat input
model contact and the practical contacts. The Journal of thesuddenly disappears. Temperature goes back to initial temperaInstitute of Electrical Engineers of Japan, Vol. 87-2, No. 941,
ture with sufﬁcient passage of time.
pp. 97–105, 1966.
Fig. 4,6 shows temperature distribution. The temperature is[5] T. OMORI. Consideration on the ratio of the short time curhighest at origin and is monotonically decreasing with distancerent carrying capacity and the continuous current carrying cafrom origin.
pacity of the point contacts. The Journal of the Institute of

4 Conclusion

Electrical Engineers of Japan, Vol. 85-8, No. 923, pp. 111–
A theoretical equation of temperature distribution change is ob115, 1965.
tained for investigating heat generation phenomena by point con[6]Takayoshi
Kubono. Temperature distribution in the constrictact current with considering the model that semi-inﬁnite solid totion region of electri cal contacts carrying the dc-current.
which current is applied and with introducing an approximation
The transactions of the Institute of Electrical Engineers ofthat current distribution is constant inside r ≤ a, under the as-by the result. If material melts, we can also roughly estimate
when the melting occurs. Fig. 5,6 simply express the result thatshows relationship between temperature and time and the relationship between temperature and distance from the origin.
64

2.5

Normalized Distance r̃ (= r/a)

ReferenceIn the case of period after heat input ﬁnishes (t̃ > 1), τ̃ =1
1
1[1] Nannji Saka Bor-Jeng Wang. Thermal analysis of electrodeT̃ (r̃, t̃) =2r̃ τ̃ =0 (t̃ − τ̃ )1/2
⎡





⎤
heating and melting due to a spark. IEEE Trans. on Compo.
 r̃ =12
2
1 (r̃1 −r̃)
1 (r̃1 +r̃)
+c2 r̃11=0 r̃1 exp − D̃− exp − D̃
dr̃1
t̃−τ̃
t̃−τ̃
Hybr. and Manuf. Technol., Vol. 16, No. 4, pp. 456–466,
⎣ 
⎦ dτ̃





2
2
r̃1 =∞ 1
1 (r̃1 −r̃)
1 (r̃1 +r̃)− exp − D̃ t̃−τ̃dr̃1+ r̃1 =1 r̃3 exp − D̃ t̃−τ̃1993.
1

sumptions that material constants are constant and latent heat andradiant heat are negligible. Given material, contact area, heat input time and input current, we can estimate if the material melts3ρ
heat 2 1
2 Cρ
( 2πI ) ( 4πD) 3t̃ = 1.00(5)

3 Result of theoretical analysis

2.5

Normalized Distance r̃ (= r/a)

Normalized Time t̃ (= t/theat )

1

= 100

2
a

4D
D̃ = a2 /t
heat

Fig.4 Temperaturechangewith distance from heat origin
in the approximation (1)(D̃ = 100)

1

t

d

a

0.2

2

T (r̃,t̃,a,theat )−T0ρheat 2 1
2 Cρ
( 2πI ) ( 4πD
) 3

1

t

1.01

0

Fig.3 Temperaturechangewith time in the approximation(1) (D̃ = 100)T̃ (r̃, t̃) =

2

d

0
0.5

T (r̃,t̃,a,theat )−T0ρheat 2 1
2 Cρ
( 2πI ) ( 4πD) 3t̃ = 1.00

0.3

Normalized Time t̃ (= t/theat )Normalized Temperature T̃ (r̃, t̃)lows. In the case of period of heat input (t̃ ≤ 1), τ̃ =t̃1
1
T̃ (r̃, t̃) =2r̃ τ̃ =0 (t̃ − τ̃ )1/2
⎡




⎤
 r̃ =1 2
2
1 (r̃1 −r̃)
1 (r̃1 +r̃)
+c2 r̃11=0 r̃1 exp − D̃
−
exp
−
dr̃
1
t̃−τ̃ ⎣  D̃ t̃−τ̃ 2 
⎦ dτ̃
2
r̃ =∞
1 (r̃1 −r̃)
1 (r̃1 +r̃)
−
exp
−
dr̃
+ r̃11=1 r̃13 exp − D̃1t̃−τ̃D̃ t̃−τ̃1tr̃ = 00.3

2Normalized temperature distribution is ﬁnally obtained as fol-

2

d

0

Nondimensionalization is carried out as follows: t̃ =4D
t/theat , τ̃ = τ /theat , r̃ = r/a, r˜1 = r1 /a and D̃ = a2 /t
.
heat
(4)

T (r̃,t̃,a,theat )−T0ρheat 2 1
2 Cρ
( 2πI ) ( 4πD) 30ture. By those approximations, we can obtain the rough range oftemperature.
2.4 Nondimensionalization of T (r, t)

In addition to those, T is nondimensionalized as
T (r̃, t̃, a, theat ) − T0T̃ (r̃, t̃) = I 2  theat  12 1
ρ
2 Cρ
2π
4πD
a3
d

T̃ (r̃, t̃) =0.35

Normalized Temperature T̃ (r̃, t̃)I
r = a is 2πa
2 . The approximation(1) has same energy as input

0.4

0.4
Normalized Temperature T̃ (r̃, t̃)tion(2) current density distribution inside r ≤ a is the value of theaverage of current density in 0 ∼ a is introduced. The average
3Iof current density iave (a) is 2πa
2 , the value of current density at

Normalized Temperature T̃ (r̃, t̃)of temperature. In order to obtain the higher limit, an approxima-

Japan.A, Vol. 104, No. 6, pp. 47–53, 1985.

3㻸㼍㼞㼓㼑㻌㻱㼐㼐㼥㻌㻿㼕㼙㼡㼘㼍㼠㼕㼛㼚㻌㼁㼟㼕㼚㼓㻌㻾㼑㼍㼘㻌㼑㼤㼕㼟㼠㼕㼚㼓㻌㼏㼕㼠㼥㻌㻳㼑㼛㼙㼑㼠㼞㼕㼏㼍㼘㻌㼐㼍㼠㼍㻌㻌㼒㼛㼞㻌㼁㼞㼎㼍㼚㻌㼎㼛㼡㼚㼐㼍㼞㼥㻌㼘㼍㼥㼑㼞㻌㻌㻌ᐇ㒔ᕷᗄఱᙧ≧䜢⪃៖䛧䛯㒔ᕷቃ⏺ᒙ䛾 㻸㼍㼞㼓㼑㻌㻱㼐㼐㼥㻌㻿㼕㼙㼡㼘㼍㼠㼕㼛㼚㻌Ꮫ⡠␒ྕ䠖㻜㻤㻮㻞㻣㻝㻠㻞㻌 㻌 Ặྡ䠖Ώ㎶ಟ㻌 㻌 㻌 㻌 㻌 ᣦᑟᩍᐁ䠖⚄⏣Ꮫ㻌 ൐   ൐  ൐ 䛸䛔䛖⤖ᯝ䛻䛺䛳䛶䛔䜛䚹㻌
㻌
Station
Cubic
Tower
Dome

700
600
500
400
300

Height[m]䠍䠊䛿䛨䜑䛻㻌
㒔ᕷ䛻䛚䛔䛶ఫ⎔ቃ䛾ኚ໬䛿䜑䛦䜎䛧䛟䚸ᕷ⾤ᆅ䛾㛤Ⓨ᫬䛾኱Ẽ⎔ቃ䜰䝉䝇䝯䞁䝖䛜ᚲせ䛻䛺䛳䛶䛔䜛䚹ᘓ⠏≀䛾࿘ᅖ䛾ὶ䜜䛻ᑐ䛩䜛ᙳ㡪䛿⣔䛸䛧䛶ຠᯝ䛜ฟ䛶䛟䜛䛾䛷䚸䛭䛾෌⌧ィ⟬䛻䛿㧗䛔✵㛫ゎീᗘ䛜せồ䛥䜜䚸኱㔞䛾ィ⟬㈨※䜢ᚲせ䛸䛩䜛䚹䛭䛾䛯䜑ᐇ㒔ᕷ䛾኱Ẽ஘ὶ䛾◊✲䛷䛿୺䛻䚸᫬㛫ᖹᆒ⤫ィ㔞䛻㔜Ⅼ䜢⨨䛔䛯䜒䛾䛜ከ䛔䚹ᮏ◊✲䛷䛿 㻸㼍㼞㼓㼑㻌 㻱㼐㼐㼥㻌㻿㼕㼙㼡㼘㼍㼠㼕㼛㼚㻌䜢฼⏝䛧䚸ᐇ㒔ᕷ䛾ᗄఱ䜢฼⏝䛧䛯஘ὶ䛾
Ⓨ⏕䛸䚸㧗ᒙᘓ㐀≀⩌䛾ᙳ㡪䛾⣽㒊䛻↔Ⅼ䜢ᙜ䛶ほᐹ䛩䜛䚹㻌㻌䠎䠊ィ⟬ᴫせ㻌ྡྂᒇᕷෆ䛛䜙㑅䜀䜜䛯Ỉᖹ 1km ᅄ᪉䛾㡿ᇦ
䠄1km×1km×0.6km䠅䜢䜾䝸䝑䝗䝃䜲䝈 2.5m 䛷ゎീ䛧䛯ὶయィ⟬䜢⾜䛳䛯䚹䛒䜛≉ᚩⓗ䠐䛴䛾䜶䝸䜰䠄㻲㻲㼕㼓㼡㼞㼑㻝䠅
䛻䛴䛔䛶䛭䜜䛮䜜 㻟 ḟඖᖹᆒὶ䝕䞊䝍䜢ฟຊ䛧䚸㻰㼕㼟㼜㼑㼞㼟㼕㼢㼑㻙㻲㼘㼡㼤䚸඲㐠ື㔞䝣䝷䝑䜽䝇䛺䛹䜢ィ⟬䛧䚸䜾䝸䝑䝗䛤䛸䛾䝥䝻䝣䜯䜲䝹䜢⟬ฟ䛧䚸䝻䞊䜹䝹䛺㐠ື㔞䝣䝷䝑䜽䝇䛾✵㛫ศᕸᙧ≧䛻䛴䛔䛶᳨ウ䛧䛯䚹㻌
㻌

200
100
0
-0.2

-0.15

-0.1

-0.05

0

Total Momental Flux <UW> [m2s-2] m^2/‫^ݏ‬2
൏  ൐ ሾଶ Ȁ‫ ݏ‬ଶ ሿ

㻌㻲㼕㼓㼡㼞㼑㻌㻞㻌㻌 Ỉᖹ᩿㠃ᖹᆒ㐠ື㔞䝣䝷䝑䜽䝇䛾㖄┤ศᕸ㻌㻌㐠ື㔞䝣䝷䝑䜽䝇䛿஘ὶᡂศ䚸䛸ᖹᆒⓗ䛺㏿ᗘᡂศ㻞 ᡂศ䛻ศゎ䛩䜛䛣䛸䛜䛷䛝䛶䚸㻌㻌ᇱ  ᇱ ൐ ൅൏ തതതതതത൏ തതതത ൐ൌ൏ തഥ ൐㻌඲㐠ື㔞䝣䝷䝑䜽䝇䠙ኚືᡂศ㻌 䠇ᐃᖖᡂศ㻌 㻌㻌䛭䛾஘ὶኚືᡂศ䝣䝷䝑䜽䝇䜢 㻱㼐㼐㼥㻙㻯㼛㼢㼍㼞㼕㼍㼚㼏㼑㻌䠄 ௨ᚋ 䠘 u’w’ 䠚 䠅 ᖹᆒὶ㏿ ᡂศ䛻 䜘䜛䝣䝷 䝑䜽 䝇䜢㻰㼕㼟㼜㼑㼞㼟㼕㼢㼑㻙㻲㼘㼡㼤㻌 㻔௨ᚋ䠘䠘 u䠚䠘w䠚㻕䛸䛧䛶䛔䜛䛜䚸䛭䛾 㻰㼕㼟㼜㼑㼞㼟㼕㼢㼑㻙㻲㼘㼡㼤 䜢᭩䛛䛫䛯䜒䛾䜢ྠ䛨䜘䛖䛻⤯ᑐ್ 㡰 䛻 ୪ 䜉 䜛 䛸 䚸  ൐  ൐  ؆ 㻌䛸䛺䛳䛯䚹㻌㻌㻯㼍㼟㼑㻌㻦㻌㻿㼠㼍㼠㼕㼛㼚㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻯㼍㼟㼑㻌㻦㻌㻯㼡㼎㼡㼕㼏㻌㻌

Station
Cubic
Tower
Dome

700
600

400
300

Height[m]

500

200
100
0
-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

㻌

൏  ൐൏  ൐ ሾଶ Ȁ ଶ ሿ㻯㼍㼟㼑㻌㻦㻌㼀㼛㼣㼑㼞㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻯㼍㼟㼑㻌㻦㻌㻰㼛㼙㼑㻌㻲㼕㼓㼡㼞㼑㻌㻟㻌 Ỉᖹ᩿㠃ᖹᆒ 㻰㼕㼟㼜㼑㼞㼟㼕㼢㼑㻌㻲㼘㼡㼤㻌䛾㖄┤ศᕸ㻌㻲㼕㼓㼡㼞㼑㻌㻝㻝 ィ⟬㡿ᇦ䛾ᘓ≀㧗䛥䛾䝁䞁䝍䞊ᅗ㻌㻌䈜䜹䝷䞊䛿㧗ᗘ䜢⾲䛧䛶䛔䜛㻌䛣䛾 㻲㼕㼓㼡㼞㼑㻟 䜢ぢ䜛䛸 㻿㼠㼍㼠㼕㼛㼚 䛾䜶䝸䜰䛻䛚䛔䛶䛿㻌䝣䝷䝑䜽䝇䛜኱䛝䛔䛾䛿䛭䛾䜎䜎䛰䛜䚸䛣䛾ᖹᆒὶᡂศ䜢ぢ䛯᫬䛻䛿඲䝣䝷䝑䜽䝇䛿 㻞 ␒┠䛻኱䛝䛛䛳䛯㻯㼡㼎㼕㼏 䛾䜶䝸䜰䜒 㼀㼛㼣㼑㼞㻌䛾䜒䛾䛸Ṥ䛹ኚ䜟䜙䛺䛔኱䛝䛥䛻䛺䛳䛶䛔䜛䚹㻌㻌㻌䠏䠊⤖ᯝ㻌㻲㼕㼓㼡㼞㼑㻞 䛿㐠ື㔞䝣䝷䝑䜽䝇䛾㖄┤ศᕸ䜢♧䛧䛯ᅗ䛻䛺䜛䚹䜶䝛䝹䜼䞊䜢྾཰䛧䛶䛔䜛䛾䛿ᆅ㠃ഃ䛺䛾䛷್䛿䝬䜲䝘䝇䛻䛺䛳䛶䛔䜛䚹⤯ᑐ್䛾኱䛝䛔䜒䛾䛛䜙䚹65ḟ䛻ሙᡤ䛤䛸䛻䛭䛾൏  ൐ǡ ൏ ᇱ  ᇱ ൐ǡ൏  ൐൏  ൐ 䛸 Building Coverage(䛒䜛Ỉᖹ᩿㠃䜢䛸䛳䛯᫬䛻ᘓ≀䛜༨䜑䛶䛔䜛㠃✚䛾๭ྜ)㻌 䜢୪䜉䛶䜾䝷䝣䛻䛧䛯䜒䛾䜢㍕䛫䜛䚹ᶓ㍈䛾䝣䝷䝑䜽䝇䛿ሙ䛾≉ᚩⓗ䛺ᛂຊ䛻䜘䜚ᐃ⩏䛥䜜䜛ᦶ᧿㏿ᗘ䛷↓ḟඖ໬䛥䜜䛶䛔䜛䚹㻿㼠㼍㼠㼕㼛㼚㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻯㻯㼡㼎㼕㼏㻌㻌

㼀㼛㼣㼑㼞㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻰㼛㼙㼑㻌㻌㻌㻯㼍㼟㼑㻌㻿㼠㼍㼠㼕㼛㼚㻌  Ȁ‫כ‬ଶ 㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌 㻌 㻌 㻌 㻌 㻌 㻌㻌㻌㻌Ȁ‫כ‬ଶ 㻌㻲㼕㼓㼡㼞㼑㻌㻡㻡 ᖹᆒ㢼㢼㏿ሙ䛾䛾㖄┤᩿㠃ᅗ㻌㻌㉥▮༳䛾䛸䛣䜝䛻䛿ୖὶ䛻㟁Ἴሪ㻌䚸㉥䛔෇ᘼ䛾ୖὶ䛻䛿䝘䝂䝲䝗䞊䝮䛜䛒䜛䚸ษ᩿㠃䛿 㻲㼕㼓㼡㼞㼑㻌㻝 ィ⟬㡿ᇦ䛾ᘓ≀㧗䛥䛾䝁䞁䝍䞊ᅗ䛾ⓑ⥺ୖ㻌㻌  Ȁ‫כ‬ଶ 㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌㻌 㻌 㻌 㻌 㻌 㻌 㻌㻌㻌㻌Ȁ‫כ‬ଶ 㻌㻲㼕㼓㼡㼞㼑㻌㻠
㻠㻌Ỉᖹ᩿㠃ᖹᆒ㐠ື㔞䝣䝷䝑䜽䝇䛾㖄┤ศᕸ㻌ỈⰍ䠖඲㐠ື㔞䝣䝷䝑䜽䝇䚸㉥ 㻱㼐㼐㼥㻙㻯㼛㼢㼍㼞㼕㼑㼚㼏㼑䚸㻌㻌⥳䠖㻰㼕㼟㼜㼑㼞㼟㼕㼢㼑㻙㻲㼘㼡㼤䚸䠄ᶓ㍈䛿ᦶ᧿㏿ᗘ䛷↓ḟඖ໬䛧䛶䛒䜛䠅㻌㻌㻌㻲㼕㼓㼡㼞㼑㻠 䛿ྛ䜶䝸䜰䛻䛚䛡䜛൏  ൐ǡ ൏ ᇱ  ᇱ ൐ǡ㻌൏  ൐൏  ൐䛾㖄┤ศᕸᅗ䛻䛺䜛䚹඲㐠ື㔞䝣䝷䝑䜽䝇䛜䛚䛚䜘䛭ᘓ≀䛾䛒䜛㧗䛥䛻䛚䛡䜛ศᕸ䛸ᑐᛂ䛧䛶䛔䜛䛾䛜ぢ䛶ྲྀ䜜䜛䚹䜎䛯䚸㻰㼛㼙㼑 䛻䛚䛔䛶䛸䛶䜒ᖹᆒὶ䛻䜘䜛㐠ື㔞䛾㍺㏦䛜኱䛝䛔䛣䛸䛜䜟䛛䜛䚹䜎䛯䚸ᖹᆒ໬᫬㛫䜢༑ศ䛸䛳䛶䛔䜛䛾䛷䚸㼀㼛㼣㼑㼞㻌 䛻䜘䜛ᙳ㡪䜒䜟䛪䛛䛺䛜䜙䛻䝥䝻䝣䜯䜲䝹䛛䜙ぢ䜛䛣䛸䛜䛷䛝䛶䚸൏  ൐䛾㡬Ⅼ䛾㧗䛥䛛䜙 㼀㼛㼣㼑㼞 䛾㧗䛥䛷䛒䜛 㻝㻜㻜㼙௜㏆䛾㧗䛥䜎䛷ഴ䛝䛾ᑡ䛧㐪䛖㡿ᇦ䛜䛒䜚䚸䛣䜜䛜㟁Ἴሪ䛾ὶ䜜䛻୚䛘䛶䛔䜛ᙳ㡪䛷䛒䜛䛸䛔䛘䜛䚹㻌㻲㼕㼓㼡㼞㼑㻡 䛿䛭䛾ᖹᆒⓗ䛺ὶ䜜䜢ὶ䜜᪉ྥ䛻ᑐ䛧䛶ᆶ┤䛻ษ䛳䛶䚸䛹䛾䜘䛖䛺ᚠ⎔䛜⏕䛨䛶䛔䜛䛛䜢⾲䛧䛯ᅗ䛻䛺䜛䚹䝧䜽䝖䝹䛾㛗䛥䛜ᖹᆒὶ㏿䛾ᙉ䛥䜢♧䛧䛶䛔䜛䛾䛷䚸㻿㼠㼍㼠㼕㼛㼚䚸㻰㼛㼙㼑 䛷䛿኱ᆺᘓ㐀≀䛾ᙳ㡪䛷ᘓ≀䜢㑊䛡䛶㏻䜛㢼䛜ᙉ䛔ᐃᖖὶ䜢స䜚ฟ䛧䛶䛔䜛ᵝᏊ䛜ぢ䛘䜛䚹ᑐ䛧䛶 㻯㼡㼎㼕㼏㻘㼀㼛㼣㼑㼞 䛾㡿ᇦ䛷䛿ᑐὶ䛿ぢ䜙䜜䜛䜒䛾䛾䝧䜽䝖䝹䛾㛗䛥䜒▷䛟䚸㼀㼛㼣㼑㼞 䛷䛾⣽䛟䛶㧗䛔ᘓ≀䛾ᙳ㡪䛿ぢ䛶ྲྀ䜛䛣䛸䛜䛷䛝䛺䛔䛾䛷䜋䜌ᐃ ᖖ ὶ 䜢 Ⓨ ⏕ 䛥 䛫 䛶 䛔 䛺 䛔 䛸 ゝ 䛘 䜛 䚹 ㏫ 䛻 㻯㼡㼎㼕㼏㻌㼀㼛㼣㼑㼞㻌 䛾䜘䛖䛺ᙧ≧䛾⾤䛷䛿䚸஘ὶᡂศ䛜䜘䜚㔜せ䛸䛔䛖䛣䛸䜒䛷䛝䜛䚹㻌䛣䜜䜙䜘䜚䚸㡿ᇦ䛻ᑐ䛧䛶㧗䛔ᘓ≀䛾❧䛳䛶䛔䜛㠀୍ᵝᗄఱ䛾ୖ✵䛷䛿 㻰㼕㼟㼜㼑㼞㼟㼕㼢㼑㻌 㻲㼘㼡㼤 䜒↓ど䛷䛝䛺䛔⛬ᗘ䛾䝣䝷䝑䜽䝇䛾ᐤ୚䜢♧䛧䚸䛭䛾ᑐὶ䛾఩⨨䛾ᅛᐃ䛿᢬ᢠ䛻┤᥋ⓗ䛻㛵ಀ䛺䛔䛸䛥䜜䜛䜘䛖䛺ᘓ≀䛷䜒㉳䛣䜚䛖䜛䛣䛸䛜䜟䛛䛳䛯䚹㻌㻌䠐䠊⤖ㄽ㻌᢬ᢠ䛸䛺䛳䛶䛔䜛༢యᘓ㐀≀䛜䛒䜛䛸ゝ䛖䜘䜚䛿䚸ᗄ䛴䛛䛾ᘓ≀䛜⩌䛻䛺䜛䛣䛸䛷䛭䛾㐠ື㔞㍺㏦䛜኱䛝䛟䛺䜛䛸䛔䛖஦䜢♧၀䛩䜛⤖ᯝ䜢ᚓ䛯䚹㻌኱䛝䛺ᘓ≀䛜Ꮡᅾ䛧䛶䛔䜛䛸䛝䛿䛭䜜䛜㍈䛻䛺䛳䛶኱Ẽ䛾ᚠ⎔䛾ᐤ୚㒊ศ䜢኱䛝䛟䛧䛶䛔䜛䚹㏿ᗘ䛾ᖹᆒⓗ䛻㐜䛔㡿ᇦ䛾 㻰㼕㼟㼜㼑㼞㼟㼕㼢㼑㻌 㻲㼘㼡㼤 䜢ཎᅉ䛸䛩䜛㒊ศ䛿䚸ᘓ≀䛜᢬ᢠ䜢స䜚ฟ䛧䛶䛔䜛䛸ゝ䛖䜘䜚䜒ᘓ≀䛜ୖ᪼ὶ䜢స䜚ฟ䛩䛣䛸䛻䜘䛳䛶ప✵䛾ప㏿㡿ᇦ䜢ᣢ䛱ୖ䛢䚸㛫᥋ⓗ䛻ᘓ≀ୖ✵䛻ᐃᅾ䛩䜛㐜䛔㡿ᇦ䜢స䜚ฟ䛧䛶䛔䜛䛸䛔䛖⤖ᯝ䛻䛺䛳䛯䚹㻌
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ཧ⪃ᩥ⊩㻌
[1] Pijush K. Kundu and Ira M Cohen “Fluid Mechanics㻌
Third Edition” ELSEVIR ACADEMIC PRESS, 2004
[2] Kip S. Throne “Application of Classical Physics”
California Institute of Technology, California , 2011[3] Marieta Cristina Castillo, Atsushi Inagaki and Manabu
Kanda “The Effect of Inner- and Outer Layer Turbulence in a
Convective Boundary Layer on the Hear-Neutral Inertial
Sublayer Over an Urban-Like Surface” Boundary-Layer
Meteorol DOI 10.1007/s10546-011-9614-4, 2011[4] Marcus Oliver Letzel “High resolution Large-Eddy
Simulation of turbulent flow around buildings” Sept 2007

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