A COMPREHENSIVE FRACTAL APPROACH IN DETERMINATION …eprints.uthm.edu.my/9884/1/MOHD_FIKRI_BIN_AZIZAN.pdf · eksperimen kekonduksian haba melalui ujian pelantar. Pengukuran ujian

A COMPREHENSIVE FRACTAL APPROACH IN DETERMINATION OF THE

EFFECTIVE THERMAL CONDUCTIVITY OF GAS DIFFUSION LAYERS IN

POLYMER ELECTROLYTE MEMBRANE FUEL CELLS

MOHD FIKRI BIN AZIZAN

A project report submitted in partial fulfillment of the requirement for the award of

the Degree of Master of Mechanical Engineering

Faculty of Mechanical and Manufacturing Engineering

Universiti Tun Hussein Onn Malaysia

JUN 2017

iii

Special dedication to my beloved mother and father

(Azizan bin Saad and Norsyakimah Lee binti Abdullah), and all family members for

their love and encouragement

Special thanks to my friends, my lecturer and faculty members for all your care,

support and endless best wishes

iv

ACKNOWLEDGMENT

In the name of Allah S.W.T, The Most Gracious and Merciful.

Alhamdulillah, praise to Allah S.W.T. for giving me the strength to complete my Master

Research project with successfully. First and foremost, I would like to give my deepest

thanks to both my parents (Azizan bin Saad and Norsyakimah Lee binti Abdullah), and

also to family members (Mohd Faizal bin Azizan, Intan Syafinaz binti Azizan,

Muhammad Fauzi bin Azizan and Intan Diyana binti Azizan) for their continuing

support and encouragement in completing this project.

I would like to express my deepest gratitude to my supervisor, Prof Madya Dr.

Bukhari bin Mansoor and also to Dr. Hamidon bin Salleh, as the co-supervisors for

constantly guiding and encouraging me throughout this study. I would like to convey my

greatest appreciation to him for giving me the professional training, advice, motivation

and suggestion to carry this project to its final form.

In particular, my sincere thanks are also extended to all my friends and others

who have provided assistance at various occasions especially to all members

Automotive Laboratory, and Principal, Fellows and Assistance Fellows of Tun Fatimah

Resident Collage. With my heartfelt would like to say thanks to each and every one of

them. Their views and tips are useful indeed. Regrettably, it is not possible to list all of

them in this limited space.

Thank you. May Allah S.W.T. bless upon all of you.

v

ABSTRACT

The challenges in the fuel cell industry is to produce the efficient thermal and water

management for accurate determination of the effectiveness thermal conductivity of

gas diffusion layers (GDL) used in polymer electrolyte membrane fuel cells

(PEMFC‟s). This is one of the factors affecting the durability of a fuel cell and need

to get a solution to minimize costs and optimize the use of electrodes and cells. The

main objectives of this research focus on the capability of the fractal approach for

estimation the effectiveness of thermal conductivity of gas diffusion layer. Moreover,

on this research also to propose modified fractal equations in determination of the

effective thermal conductivity of GDL in PEMFCs based on previous study. Other

objectives in this study are demonstrated the thermal conductivity of GDL treated

with PTFE contents by using through-plane thermal conductivity experiment

method. The through-plane measurement (experiment method) has been used in

estimating through-plane thermal conductivity of the GDL. Thermal resistance for

GDL also has been investigated under compression pressure 0.1 MPa until 1.0 MPa.

In fractal equation, the determination of tortuous and pore fractal dimension can be

done by using Scanning Electron Microscopy (SEM) method. Determination of

effectiveness thermal conductivity using of fractal equation with slightly modified.

In findings, it was found that fractal equation have been modified and measured on

the GDL parameter characteristics. It was shown that the value of the effectiveness

thermal conductivity of the sample using fractal approach is in good agreement with

the experimental value. Finally, all the effective thermal conductivity measured by

experimental and fractal approach have been determined with the variant temperature

and compression pressure to show the validation result between of this two methods.

vi

ABSTRAK

Cabaran dalam industri fuel cell adalah untuk pengurusan haba dan air yang cekap

dan juga penentuan tepat secara keberkesanan bagi keberaliran haba pada lapisan

resapan gas (GDL) yang digunakan dalam membran elektrolit polimer fuel cell

(PEMFCs). Ini adalah merupakan salah faktor-faktor yang mempengaruhi ketahanan

pada fuel cell dan hal ini perlu mendapat penyelesaian yang terbaik untuk

mengurangkan kos dan mengoptimumkan penggunaan elektrod dan sel-sel. Objektif

utama fokus penyelidikan ini adalah untuk mengetahui keupayaan pendekatan fraktal

untuk anggaran keberkesanan kekonduksian haba pada PEM dan GDL. Selain itu,

kajian ini juga untuk mencadangkan pengubahsuaian terhadap persamaan fraktal

dalam penentuan kekonduksian haba yang berkesan GDL di PEMFCs berdasarkan

kajian sebelumnya. Objektif lain dalam kajian ini menunjukkan keberaliran haba

pada GDL dengan kandungan PTFE berbeza dengan menggunakan kaedah

eksperimen kekonduksian haba melalui ujian pelantar. Pengukuran ujian pelantar

(kaedah eksperimen) telah digunakan dalam menganggarkan kekonduksian haba

daripada GDL. Rintangan haba untuk GDL juga telah dijalankan di bawah tekanan

mampatan 0.1 sehingga 1.0 MPa. Dalam persamaan fraktal, penentuan kelikuan dan

liang dimensi fraktal boleh dilakukan dengan menggunakan kaedah Scanning

Electron Microscopy (SEM). Penentuan keberkesanan kekonduksian haba

menggunakan persamaan fraktal dengan sedikit pengubahsuaian telah dijalankan.

Hasilnya telah didapati bahawa nilai keberkesanan keberaliran haba terhadap bahan

ujikaji dengan menggunakan kaedah pendekatan fraktal adalah bersamaan dengan

nilai yang diperoleh dengan menggunakan kaedah eksperimen. Akhir sekali,

kesemua nilai keberkesanan kadar aliran haba telah diukur dengan kaedah

eksperimen dan fractal telah ditunjukkan debgan berlainan suhu pemanasan dan

tekanan mampatan yang berbeza bagi menunjukkan pengesahan keputusan bagi

kedua-dua kaedah ini.

vii

TABLE OF CONTENTS

TITLE i

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENT vii

LIST OF FIGURES ix

LIST OF TABLES xiiii

LIST OF SYMBOLS AND ABBREVIATIONS xiv

LIST OF APPENDICES xvi

CHAPTER 1 INTRODUCTION 1

1.1 Research background 1

1.2 Problem statement 3

1.3 Significance 5

1.4 Objective 5

1.5 Scope of study 6

CHAPTER 2 LITERATURE REVIEW 7

2.1 Introduction 7

2.2 Application of fuel cell 8

2.3 Proton exchange membrane fuel cell 9

2.4 Gas diffusion layer in PEMFCs 11

2.5 Fractal model of effective thermal conductivity of gas diffusion layer in

polymer electrode membrane fuel cells 13

2.6 Determination of the required fractal dimension 19

2.7 Determination of tortuous fractal dimension 20

2.8 The modified algorithm for the generation of random multi-fractal media 21

viii

2.9 Measurement the through plane thermal conductivity of the PEM and GDL 22

2.10 Summary 25

CHAPTER 3 METHODOLOGY 26

3.1 Introduction 26

3.2 Experimental setup 28

3.3 Measurement and uncertainty analysis 30

3.4 Calculation of heat flux, Q and thermal conductivity, k of PEM and GDL 35

3.5 Scanning of electron microscope 35

3.6 Fractal approach to thermal conductivity 36

3.7 Summary 39

CHAPTER 4 RESULTS AND DISCUSSION 40

4.1 Thermal conductivity of the PEM and GDL 40

4.2 Temperature profile of PEM and GDL 41

4.3 Determination of the PEM and GDL fractal dimension 42

4.4 Effect of the temperature on the through-plane thermal conductivity of

PEM and GDL 46

4.5 Thermal conductivity by fractal approach 55

4.6 Summary 57

CHAPTER 5 CONCLUSION AND RECOMMENDATION 59

5.1 Introduction 59

5.2 Conclusion 59

5.3 Recommendation 60

REFERENCES 62

ix

LIST OF FIGURES

1.1 A functionally description of fuel cells 2

1.2 Schematic of Membrane Electrode Assembly

(MEA) in PEMFCs

4

2.1 The illustration movements of hydrogen atom

cross-over the membrane

10

2.2 Nafion ® Du Pont membrane fuel cell 10

2.3 (a) The reconstructed microporous layer 13

2.3 (b) SEM image of a microporous layer 13

2.4 Microstructure and material parameter for the

fractal model

14

2.5 The series-parallel model 15

2.6 A comparison of the effective thermal

conductivity between the fractal parallel

models, series model and the series-parallel

layer models of thermal conductivity

19

2.7 (a) Scanning electron microscope picture of

typical carbon fiber paper sheets use in fuel

cell - Toray TGPH-060 CFP with no PTFE

20

2.7 (b) Scanning electron microscope picture of


cell - close-up view of the TGPH-060 CFP

with no PTFE

20

2.7 (c) Scanning electron microscope picture of


20

x

cell - Toray TGPH-060 CFP with 20% PTFE

2.7 (d) Scanning electron microscope picture of


cell - close-up view of the TGPH-060 CFP

with 20% PTFE

20

2.8 Measured thermal conductivity of the GDLs

at different compression loads

23

2.9 Measured thermal conductivity of the

membrane as a function of the temperature for

dry Nafion ® membrane (N112 and N117),

error bar calculated at 15%

24

2.10 Measured thermal conductivity as a function

of temperature for GDL- Toray carbon paper

(TGP-H-060), error bar calculated at 15%

25

3.1 A methodology flow chart 27

3.2 Schematic diagram of the test setup to

measure

29

3.3 (a) Schematic diagram thermocouple location 32

3.3 (b) Actual through-plane test rig for location

along the aluminum bronze

32

3.4 A typical steady state temperature profile

through the GDL

33

3.5 Schematics thermal resistance network of the

upper and lower standard material and

specimen of PEM

34

4.1 Steady state temperature profile at

thermocouple location for Nafion

N117 Catalyst Coated Membrane at variants

ambient temperature

42

4.2 (a) SEM Image cross-sections TGP-H-060 44

4.2 (b) SEM Image cross-sections TGP-H-090 44

4.2 (c) SEM Image cross-sections ELAT LT1400W 44

xi

4.2 (d) SEM Image cross-sections Untreated N117

Membrane

44

4.2 (e) SEM Image cross-sections Coated N117

Membrane

44

4.2 (f) SEM Image cross-sections CT 44

4.2 (g) SEM Image cross-sections Sigracet 35 AA 44

4.2 (h) SEM Image cross-sections Sigracet 35 BA 44

4.3 (a) SEM Image surface TGP-H-060 45

4.3 (b) SEM Image surface TGP-H-090 45

4.3 (c) SEM Image surface ELAT LT1400W 45

4.3 (d) SEM image surface untreated N117

membrane

45

4.3 (e) SEM image surface coated N117 membrane 45

4.3 (f) SEM image surface CT 45

4.3 (g) SEM image surface Sigracet 35 AA 45

4.3 (h) SEM image surface Sigracet 35 BA 45

4.4 Measured thermal conductivity as function

temperature for samples of PEM and GDL

48

4.5 Measured thermal resistance for the tested

PEM Coated N117

50


PEM Untreated N117

50


GDL Toray Carbon Paper TGP-H-060

51


GDL Toray Carbon Paper TGP-H-090

51


GDL CT

52


GDL ELAT

52


GDL Sigracet 35 AA

53

xii


GDL Sigracet 35 BA

53

4.13 Hysteresis in thickness of the GDL under the

external compression for TGP-H-060

(increase and decrease in pressure)

54

4.14 Hysteresis in thickness of the GDL under the

external compression for TGP-H-090

(increase and decrease in pressure

55

4.15 Comparison of thermal conductivity using

experimental and fractal approach between

each sample of GDL on the Room

Temperature

57

4.16 The effect of compression pressure with the

thermal conductivity by using experimental

and fractal approach between each sample of

GDL

57

xiii

LIST OF TABLES

2.1 Properties of two type GDL material 8

2.2 Specifications of Nafion membrane

within the membrane electrode

assemblies for N117 catalyst coated

membrane

11

2.3 Estimated thermal conductivities of

GDL sample at 20°C

12

2.4 Various specific values of thermal

conductivity

12

2.5 Microstructure and material parameter

of the sample „a‟ and „b‟

18

3.1 The specification of the PEM and GDL 28

4.1 Characteristics of the GDL sample for

pore fractal dimension, tortuous fractal

dimension and thermal conductivity

56

4.2 Comparison of the experimental thermal

conductivity at room temperature with

measured using fractal approach

57

xiv

LIST OF SYMBOLS AND ABBREVIATIONS

ΔT - Temperature drop across thin film

- Ratio of the number of perpendicular channels to the

total number of channels

λ - Size of the path

λmin, λmax - The minimum and maximum pore diameters

τ - Tortuous pathways

Ɛ, Ø - Porosity

A - Total area of a structural size

As - Area of heat transfer

C - Intercept of the line constant to the data in a log-log plot

Cph Phonon heat capacity

DAQ - Data Acquisition

Df - Fractal dimension

Dp - Specific pore fractal dimension

Dt - Tortuous fractal dimension

f() - The effects of anisotropy and structure in the in-plane

and through plane directions

FC - Fuel Cell

GDL, DM - Gas Diffusion Layer and Diffusion Media

k - Thermal conductivity

kg - Thermal conductivity of the occupied gas

ks1 - Thermal conductivity of the carbon fiber

ks2 - Thermal conductivity of the PTFE

Keff - Effective thermal conductivity

Keff,s - Effective thermal conductivity of solid phase

xv

Keff,p - Effective thermal conductivity of gas phase

kmem - Thermal conductivity of PEM and GDL

L(λ) - Corresponding path of length

Lo - Linear length of capillary pathway in the flow direction

Ls - Thickness of PEM and GDL

M(L) - Fractal structure

MEA - Membrane Electrode Assembly

MPL - Microporous layer

N - The cumulative pore size distribution

P - Pressure

PEM - Polymer Eletrolyte Membrane

PEMFCs - Polymer Eletrolyte Membrane Fuel Cells

PTFE - Polytetrafluoroethylene

Q - Heat flow rate

R - Pore size

r - Pore radius

R - Thermal resistance

RH-sample - Contact resistance between the sample and the holder

RL - Lower resistance

rmax - Maximum pore radius

Rsample - Thermal of sample

Rtotal - Total thermal resistance

RU - Upper resistance

SEM - Scanning Electron Microscope

T - Temperature

Tc - Temperature of the cold plate

Th - Temperature of the hot plate

Vp - Pore volume related with a certain pressure

vph - phonon velocity

Iph - Phonon mean free path

xvi

LIST OF APPENDICES

APPENDIX TITLE PAGE

1 Steady state temperature profile for

through-plane thermal conductivity

experimental

65

2 Example calculation value of Q and k

for through-plane thermal conductivity

Experimental and fractal of TGP-H-060

69

3 Image of sample and image of SEM for

PEM and GDL

73

4 Design and development through-plane

thermal conductivity test rig

88

5 Published paper 104

CHAPTER 1

INTRODUCTION

1.1 Research background

The renewal and cleanest energy sources become more serious attention in nowadays

due to the depletion of petroleum resources and potential to reduce emission

pollution. In current high demand on that, the one of the most efficient

environmental-friendly generation of energy is called fuel cell technology. Fuel Cell

technology have received more attention in recent years is due to the high efficiency

and low emissions. There are several categories of fuel cell which is Polymer

Electrolyte Membrane (PEM) fuel cells or PEMFC, Solid Oxide Fuel Cells (SOFC),

Phosphoric Acid Fuel Cells (PAFC), Alkaline Fuel Cells (AFC) and Molten

Carbonate Fuel Cells (MCFC). The focusing on this research is Polymer electrolyte

membrane fuel cells (PEMFC), where constructed from membrane electrode

assembly (MEA) including electrodes, electrolytes, catalysts, and gas diffusion

layers. The polytetrafluoroethylene (PTFE) Nafion® membrane acting as proton

conductor and (Pt)-based material as catalyst. PEMFCs convert chemical energy

stored in hydrogen (as a fuel) and oxygen directly and efficiently into electrical

energy with water as the only byproduct, have the ability to reduce energy

consumption, pollutant emissions and dependence to generation of electric energy

using fossil fuels. Figure 1.1 Efficiency of PEMFC can reach as high as 60 % in the

overall conversion of electrical energy and 80 % in cogeneration of electricity and

heat with a reduction of more than 90 % in the primary pollutants (in United State of

America).

2

The main applications of PEMFCs not only focusing on transportation, but it

does include of portable and stationary power generation. In automotive industries,

the well-known company as Honda, Toyota, General Motor and Hyundai has been

developed and demonstrated their product based on technology fuel cell (Fuel Cell

Vehicle) not only to fuel energy consumption saving but because of the potential

impact on the environment, such as emissions control greenhouse gases (Wang et al.,

2011) (Wu et al., 2014) (You et al., 2017).

Figure 1.1: A functionally description of fuel cells (Energy Design Resources, 2013)

Since PEMFCs is a renewable energy source of the cleanest, and fuel

(hydrogen and oxygen) are abundant in the earth, there are many researchers are

racing to make fuel cells are much more efficient and low cost. Recently,

considerable progress has been in improving power density, stability operations and

design structure PEMFCs. However, there are still many challenges to overcome for

profitable commercial applications. The challenges in fuel cell are its durability of

life time while operating. Among the factors that predispose to failure of PEMFCs is

the membrane manufacturing process, the material properties of the cell components,

installation of the fuel cell, and fuel cell system operating conditions. In application

fuel cell in transport, the frequency starting up and shutting down or nearly power

3

random cycle load are effect the humidity condition of Nafion membrane and also to

GDL. (Wu et al., 2014)

The main challenge in the design of fuel cells is to transfer heat from the gas

diffusion layer (GDL) in the polymer electrolyte membrane due to the result of a

chemical reaction to produce electricity. Analysis of this process requires the

determination of the effective thermal conductivity and thermal contact resistance is

also associated with the interface between the GDL and the adjacent surface or

coating. However, thermal conductivity of diffusion media or GDL is more difficult

to estimate due it porosity structure. GDL porosity makes it necessary to use

effective thermal conductivity to describe heat transfer in solid and liquid phases.

Because of GDL is anisotropic and having high porosities, there are widely dispersed

in literature thermal conductivity values.

In measuring thermal conductivity, there are several methods can be used.

The prediction methods for effective thermal conductivity of porous media can be

predicted by empirical formulas, numerical simulations, or theoretical models. The

empirical formulas and numerical simulation have several issues where empirical

method only based on simplifying assumption and empirical constants didn‟t indicate

any specific physical meanings. For numerical simulation method, the difficult to

analyst detail of GDL geometry model of porosity structure. (Nikooee et al., 2011)

Fractal approach can be used to estimate the thermal conductivity of diffusion media.

Fractal theory can analyze complex structure such as high porosities characteristic.

Fractal theory widely used in diverse engineering application which is involve

physical phenomena in disordered structures and over multiple scales.

1.2 Problem statement

The main objectives for all researchers in the development of novel membrane

materials are to improve the performance and durability of the fuel cells and reduce

the overall cost of fuel cells. Thermal conductivity of the PEM components must be

estimated accurately for better understanding the process of heat transfer in proton

exchange membrane fuel cells. The electrochemical reaction and irreversibility

4

associated in proton exchange membrane fuel cells generate large amounts of heat

which produces a temperature gradient in the various components of the cell.

In literary studies, Nafion commonly used as the thermal conductivity of the

membrane. GDL is one of the main components in MEA. The center part in MEA is

Proton Exchange Membrane or Polymer Electrolyte Membrane (PEM). Figure 1.2

shows the schematic of MEA and their components. However, it is difficult to

estimate the thermal conductivity of thermal diffusion media or gas diffusion layer

because of GDL is anisotropic and having high porosities. However GDL porosity

makes it necessary to use an effective thermal conductivity to describe heat transfer

in solid and liquid phases. As mentioned before, in having high porosity materials

and also anisotropic of GDL, which is probably be the reason why the thermal

conductivity are widely scattered in the literature (Zakil et al., 2016).

Among the methods used including of empirical formulas, numerical

simulations, or theoretical models to in determine the thermal conductivity, it‟s

experiencing one of the following three things: analytical methods are usually based

on very simplifying assumptions. Numerical solution with realistic assumptions that

can capture details GDL as heterogeneous porous media usually takes time using

simulations. The Empirical relationships including significant physical constants. In

Figure 1.2: Schematic of Membrane Electrode Assembly

(MEA) in PEMFCs (Iranzo, 2017)

5

that case, fractal methods have been recommended by Nikoee et al., (2011) as the

successful method to estimate the thermal conductivity of diffusion media.

Fractal geometry has been widely used in recent years to characterize the

complex, heterogeneous porous media. The main idea behind fractal geometry is the

scale parameter extraction invariant, which can describe the structure of complex

geometry. Physics phenomena that occur in such media can be associated with this

parameter. The main role of fractal geometry, in this case, is to simplify the structure

of porous media complex diffusion into the fractal dimension and the physical basis

for the derivation of the targeted phenomena.

There are different fractal dimensions that describe the characteristics of the

pores in the porous medium. Among the various fractal dimension, fractal dimension

and tortuous pore fractal dimension (called pores or areas based on the number of

fractal dimension calculation method) is the most important. Parameters that can be

measured by simple experiment can be used to develop the thermal conductivity

equation. To overcome these weaknesses, fractal geometry will be proposed as an

approach that will help to estimate the effective thermal conductivity of the polymer

electrolyte membrane.

1.3 Significance

This study aim to contributed regarding the temperature and heat transfer mechanism

to determine the thermal conductivity of the components of the membrane electrode

assembly and gas diffusion layer in order to approach of fractal method.

1.4 Objectives

This study embarks on the following objectives:

i. To investigate the capability of fractal approach in order to determine

thermal conductivity in GDL and propose a new approach (fractal

6

equation) in determination of the effective thermal conductivity of GDL

in polymer electrolyte membrane fuel cells.

ii. To validate by varying temperature, pressure and thermal conductivity of

GDL by using experimental method (through-plane method).

1.5 Scopes of study

The scopes of this study are listed as below:

i. An experimental study to determine the thermal conductivity

(experimental) of the PEM samples; coated Nafion® 117 and untreated

Nafion 117, the GDL samples; CT, ELAT® LT1400W, Sigracet 35 AA

(0 % PTFE) and Sigracet® 35 BA (50 % PTFE) and additional of thermal

contact resistance experiment between PEM and GDL samples with a

metal plate as a function of temperature and pressure.

ii. Comparisons have been made between the fractal methods and the

existing experimental results measured by one dimensional through-plane

thermal conductivity.

iii. Using equation of effective thermal conductivity in series and parallel

model to obtain pore fractal dimension from GDL pore size distribution.

iv. Scanning Electron Microscope (SEM) images of diffusion media also had

been used, and conventional methods of fractal image processing such as

box counting can be used to determine the pore fractal dimension.

v. The effect of the temperature on the through-plane thermal conductivity

of all the components in the PEM and GDL was investigated in the

temperature range 27 ºC - 90 ºC.

vi. The measurements temperature along the standard material and

specimens were performed under camber in order to eliminate the heat

transfer by convection.

vii. The compression pressure ranges between of 0.1 to 1 MPa.

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

In Polymer Electrolyte Membrane Fuel Cell (PEMFC) the membrane and catalyst

layer (consisting of the MEA), both of which require further research is essential to

identify and develop cost-effective material alternatives. Correlation properties of the

membrane for the performance of polymer electrolyte materials generally are more in

need. The process of heat transfer in porous GDL has been investigated and

appropriate predictive model of effective thermal conductivity by using fractal

theoretical characterization of the actual microstructure of GDL (Wang et al., 2011).

In general, the heat transfer mechanism is controlled by convection, radiation

and conduction. When the flow of liquid happen in pores the convection heat transfer

were occurs. Generally, the effect of convection is more pronounced in the case of

large pore size, while it is negligible for small pore size less than 100 μm at a lower

temperature (below 373 K) due to lack of intensive fluid- circulation in the pores. In

PEMFC, the GDL operating temperature produce in system is below than 375 K due

to its pore sizes which is 100 µm and in this scenario the convection or radiation can

neglected. (Shi et al., 2008) (Kantorovich et al., 1999).

8

Radiation heat transfer occurs through the release or absorption of heat

radiation pore walls. From studies on the type of coal, it has been found that radiation

mechanism has a significant effect on heat transfer through large pore size (> 10 μm)

at high temperatures (>1000 K), while foremost carbon material, the effect of

radiation heat transfer is negligible for temperatures below 1000 K (Shi et al., 2008).

Table 2.1 show summarizes properties of two type GDL materials. According to

table, its show that the pore size of GDL is less than 100 µm (Wang et al., 2016).

Table 2.1: Properties of two type GDL material (Wang et al., 2016)

Material PTFE loading (%) Porosity Pore size (µm)

sssCarbon cloth 0 0.78 97

Carbon paper TGP-H-120 0 0.81 32






2.2 Application of fuel cell

Technologies of fuel cells have got an attention in the transportation industry

including public transport and private vehicles. This is because the potential energy

produced by the fuel cell is capable of producing high efficiency, economical used

and the ability to reduce the environmental pollution caused by carbon monoxide gas

(Wang et al., 2011).

System Proton exchange membrane fuel cells operating on hydrogen and has a

power density as high as 1:35 kW / liter have been shown. This system has been

integrated into the vehicle concept by a number of manufacturers. Specific design

features of the fuel cell system 60 kW and operate on hydrogen supplied from a

cryogenic liquid hydrogen tank. This vehicle has a range of 400 km, a top speed of

135 km/h, and can be accelerate 0 - 100 km/h in less than 16 s (Ellis et al., 2001).

9

Development of fuel cell technology not only focusing on transportation, but

there are many of manufacturer working on other application for example, vending

machine, vacuum cleaners machines and traffic light. The growing of fuel cell

technologies also involved of power generation for hospital, police station and the

bank because of the advantages its-self (Segura et al., 2009).

2.3 Proton exchange membrane fuel cell

There are many types of fuel cell as explained in Chapter 1. As PEMFC were

selected, by using proton (hydrogen ion) conducting membrane will remain squeezed

between two porous platinum-catalyzed electrodes. At first, this membrane is based

on polystyrene, but currently Teflon-based products namely "Nafion" is used. It

offers high stability, high oxygen solubility, good thermal and high mechanical

stability. Nafion membrane is widely used for PEMFCs and have variety thickness

and specific application. These membrane are proton-conductive polymer film, also

as known as electrolyte or ionomer. The membrane functions are to allow only

protons to through-pass to cathode (Oxygen) from anode (Hydrogen) as shown in

Figure 2.1. Others are to spate the anode and cathode compartment of PEMFCs.

PEMFCs are low temperature fuel cell and operating temperature is relatively low

(between 60 – 100 °C). Hydrogen fuel cells require oxygen for humidified and

operations. Pressure, in general, remains the same on both sides of the membrane.

Operating at high pressure is necessary to achieve high power density, especially

when the air is selected as the anodic reactants. The PEMFC meets the demands of

rapid startup, acceleration, high power density and reliability. PEMFC will be best

for light-duty vehicle applications (T-raissi et al., 1992) (Mekhilef et al., 2012).

As known the mechanical strength very important for membrane since the

activities of the proton conductivity of the membrane. Figure 2.1 shows the

illustration movement of hydrogen proton cross-over the membrane. Membranes, for

example, need to carry protons, but not electrons. It is as thin as possible, so that

proton current is affected as little as possible and the voltage drop across the

10

membrane is reduced. The PEM must have durability in an acidic environment at

high temperatures for thousands of hours. It should also have a reasonable low

permeability to fuel. Nafion‟s developed by Du Pont as showed in Figure 2.2 is the

most PEM used widely known and used because it can resistant of temperature

below 100 ºC. Nafion‟s also become benchmark for comparison with other material

to develop new material for low cost to produce fuel cells. In the Table 2.2, the

Nafion N117 measurements are taken with membrane conditioned to 23 ºC, 50 %

relative humidity (Khandelwal et al., 2006) (Vishnyakov, 2006).

Figure 2.2: Nafion ® Du Pont membrane fuel cell

(Stripe et al., 2011)

Figure 2.1: The illustration movements of Hydrogen atom cross-over

the membrane (Scramlin et al., 2015)

H+

H+

H+

H+ H

+

H+

Permeate H+

11

Table 2.2: Specifications of Nafion membrane within the membrane electrode

assemblies for N117 catalyst coated membrane

Specifications

Dimension 300 mm x 300 mm

Thickness 0.183 mm

Basis weight 360 g/m2

2.4 Gas diffusion layer in PEMFCs

Gas Diffusion layer as kwon as Diffusion Media (DM), have a play roles as: (1)

electronic connection between the electrode and bipolar plate with channel, (2)

transit for reactant transport and heat/water removal, (3) mechanical support to the

membrane electrode assembly (MEA), and (4) protection of the catalyst layer from

corrosion or erosion caused by flows or other factors. (Wang et al., 2011). GDL

structural are very important for transport inside of GDLs for fuel cell conversion.

There are Carbon-based GDL such as carbon paper and carbon cloth where are

commonly used in PEMFC due to its porosity, electronic conductivity, and

flexibility. The better performance for GDL material is carbon cloth than carbon

paper as mention by Ralph et al at high current more than 0.5A cm-2

with internal

humidification (Stacks, 1997) (Park et al., 2012).

Large differences in the thermal conductivity of solid and liquid phases and

high porosity micro-structure GDL makes it necessary to define the effective thermal

conductivity, which transport parameters that play an important role in the analysis

of the performance of the fuel cell and is required in computing model. Several

studies in the literature have focused on the analysis model producing experimental

measurements of conductivity values lower than most values reported in the

literature (Wang et al., 2016). By using the concept of unit cells, the authors have

recently presented a solid model analysis to determine the effective thermal

conductivity GDLs (Sadeghi et al., 2011). A review from the previous study by

Ramousse et. al., (2008) shows that thermal conductivity of Nafion membrane

(Table 2.3) is well known where the thermal conductivity of GDLs is more difficult

to estimate (Ramousse et al., 2008). The porosity of the GDLs makes it necessary to

12

use effective thermal conductivities for describing heat transfer in the solid and fluid

phases (Yu et al., 2002). In Table 2.4, it‟s estimated from the thermal conductivities

of each phase and their volumetric fraction in the medium.

Table 2.3: Estimated thermal conductivities of GDL sample at 20 °C.

(Ramousse et al., 2008)

Sample

(A = 16 cm2)

Rmeasured

(KW-1

) 𝑅𝑚𝑀𝑎𝑥

(Km2 W

-1)

𝑘𝑒𝑓𝑓𝑀𝑖𝑛

(Km-1

W-1

)

𝑅𝑚𝑀𝑖𝑛

(Km2 W

-1)

𝑘𝑒𝑓𝑓𝑀𝑎𝑥

(Km-1

W-1

)

Quintech (190 µm) 0.327 5.23E-04 0.36 1.39E-04 1.36

Quintech (190 µm) 0.537 8.59E-04 0.33 4.75E-04 0.59

Quintech (190 µm) 0.727 1.16E-03 0.20 7.79E-04 0.30

SGL Carbon (420 µm) 1.008 1.61E-03 0.26 1.23E-03 0.34

Table 2.4 Various specific values of thermal conductivity (Shi et al., 2008)

Material Thermal conductivity

(Wm-1

K-1

) Material

Thermal conductivity

(Wm-1

K-1

)

Silver 4.186 x 102 Hydrogen 0.167

Aluminum 2.093 x 102 Oxygen 0.025

Quartz 8.392 Air 0.026

Sandstone 3.767 benzene 0.159

Clay 0.837 – 1.256 Petroleum 0.147

Water 0.461 Glass 0.502 – 1.088

In the previous studies by Zamel et al., (2012), the three dimensional

reconstruction of the layer in Figure 2.3 (a) and 2.3 (b) have been used to estimate

the diffusion coefficient and thermal conductivity of the microporous layer. The

parameters on the transport properties on these studies involve the effect of MPL

porosity, thickness and penetration.

13

2.5 Fractal model of effective thermal conductivity of gas diffusion layer in

polymer electrode membrane fuel cells

The model accounts for the actual microstructures of the GDL in terms of two fractal

dimensions, one relating the size of the capillary flow pathways to their population

and the other describing the tortuosity of the capillary pathways have been discussed

in previous study. The fractal permeability model is found to be a function of the

tortuosity fractal dimension, pore area fractal dimension, sizes of pore and the

effective porosity of porous medium without any empirical constants. A large

number of reports show that the microstructure and pore size distribution of porous

media have fractal characteristics. Therefore, it is possible to get through porous

media permeability pore structure fractal analysis (Shi et al., 2006).

Figure 2.3: (a) The reconstructed microporous layer : (b) SEM image of a

microporous layer (Zamel et al., 2012)

14

2.5.1 Fractal parallel model

In a porous medium having fractal characteristics, surface area, pore volume and the

length of capillary pathways follow a power law (Nikooee et al., 2011) (Shi et al.,

2012). Figure 2.4 shows the microstructure and material parameters for the fractal

model. The length L(λ) of the capillary pathway equation (1) can be considered as

below:

𝐿(𝜆)

𝐿0=

𝐿𝑜

𝜆 𝐷𝑡−1

(1)

Where, Lo represents the linear length of these capillary pathways in the flow

direction, λ denotes the diameter of the path, L(λ) is its corresponding length and Dt

is the tortuous fractal dimension (Shi et al., 2008). From the equation (2), the

tortuous pathways can be obtained by:

𝜏 = 𝐿(𝜆)

𝐿0

2=

𝐿𝑜

𝜆

2𝐷𝑡−2

(2)

For porous media have fractal characteristics, the cumulative pore size

distribution N can be attributed with the maximum pore size as equation (3) follows:

λ Lo

λ(x)

Figure 2.4: Microstructure and material parameter for the fractal model

15

𝑁 𝑅 ≥ 𝑟 = 𝑟𝑚𝑎𝑥

𝑟 𝐷𝑝

(3)

Where, R denotes an arbitrary pore size for which pores with the larger than

radii, r have been counted, rmax is the maximum pore radius and Dp specifies pore

fractal dimension. Based on the method of fractal analysis the pore fractal dimension

can vary, for instance, if the image of the GDL cross section is used, the pore fractal

dimension would vary between 1 and 2 and if the pore size distribution (example as

obtained by porosimetry methods) is employed the dimension would be between 2

and 3 (Nikooee et al., 2011) (Zheng et al., 2012).

The series–parallel model (Figure 2.5), Nikooee et al.,(2011) claimed that Shi

et al., (2012), was proposed a fractal model for estimation of thermal conductivity

(Nikooee et al., 2011) (Shi et al., 2012). The effective thermal conductivity of the

medium can be obtained as equation (4) :

𝑘𝑒𝑓𝑓 =1

1−𝜉

𝑘𝑒𝑓𝑓 ,𝑝+

𝜉

𝑘𝑒𝑓𝑓 ,𝑠

(4)

Where, keff is effective thermal conductivity, keef,p represents effective thermal

conductivity of the gas phase in parallel and keef,s is the effective thermal conductivity

if the gas and solid phases in perpendicular pore channel . ξ is the ratio of the number

Figure 2.5: The series-parallel model

16

of perpendicular channels to the total number of channels, with values range from 0

to 1.

The effective thermal conductivity through of parallel and perpendicular to

the heat direction can be described by using equation (5) and (6) :

𝑘𝑒𝑓𝑓 ,𝑠 = 1

휀

𝑘𝑔+

0.22

𝑘𝑠1+

0.78−휀

𝑘𝑠2

(5)

𝑘𝑒𝑓𝑓 ,𝑝 =𝑘𝑔 2−𝐷𝑝 휀𝜆𝑚𝑎𝑥

𝐷𝑡−1 1− 𝜆𝑚𝑖𝑛𝜆𝑚𝑎𝑥

𝐷𝑡−𝐷𝑝+1

𝐿0𝐷𝑡−1 𝐷𝑡−𝐷𝑝+1 1−

𝜆𝑚𝑖𝑛𝜆𝑚𝑎𝑥

2−𝐷𝑝

+ 0.22𝑘𝑠1 + 0.78− 휀 𝑘𝑠2 (6)

Where kg, ks1 and ks2 are the thermal conductivities of the occupied gas carbon

fiber and PTFE, respectively. ε donate the specifies porosity and λmin and λmax denote

the minimum and maximum pore diameters respectively.

The modified equations for parallel and series claimed by Shi et al.,(2008) are

suitable for the prediction of the effective thermal conductivity of a GDL. The

proposed of combination with the contribution of parallel and series tubes by a series

scheme based on the equation (4) will reach the effective thermal conductivity (Shi

et al., 2008). Such series scheme is suitable to determine through plane conductivity.

The equation (7) is suitable to determine of in-plane thermal conductivity

contribution with combination of parallel scheme.

𝑘𝑒𝑓𝑓 = 1− 𝜉𝑖𝑛 𝑘𝑒𝑓𝑓 ,𝑝 + 𝜉𝑖𝑛𝑘𝑒𝑓𝑓 ,𝑠 (7)

Where, ξin is the ratio of the number of channels perpendicular to the in-plane

flow direction to the total number of channels. The ξin, is related in the through plane

heat conduction, ξ. As on the geometrical parallel–perpendicular tubes scheme

presented previously (Figure 2.4), the equation (8) is the ratio of the number of

channels:

𝜉𝑖𝑛 = 1 − 𝜉 (8)

17

By using simplifying assumption on equation (8) to obtained suitable

configuration of channels in through-plane condition. Based on Nikooee et al.,

(2011) this simplifying assumption and for practical applications is suitable used for

two parameters can be related using a more general function, f(ξ)as equation (9)

below:

𝜉𝑖𝑛 = 𝑓 (9)

Where f(ξ) represents to the effects of anisotropy and structural differences in

the in-plane and through plane directions. To get the length of Lo can be calculated in

the mathematical equation (11) as proposed by Nikooee et al., (2011). The A can be

expressed as the total area of a structural size and determine using equation (10). The

pore area fractal dimension Dp can be calculated if the parameter such as ε, λmin and

λmax are determined by the equation (12) (Zheng et al., 2012). The distribution size

can be measured as the solid part as well as of all pores of different size between λmin

and λmax. It is can be describes using fractal geometry as equation (12) below:

𝐿𝑜 = 𝐴 (10)

𝐴 =𝜋𝐷𝑝𝑑

2𝑚𝑎𝑥

4휀 2−𝐷𝑝 1−

𝜆𝑚𝑖𝑛

𝜆𝑚𝑎𝑥

2−𝐷𝑝 (11)

And the general equation of Lo can be estimated as below:

𝐿𝑜 = 𝜋𝐷𝑝𝑑2

𝑚𝑎𝑥

4휀 2−𝐷𝑝 1−

𝜆𝑚𝑖𝑛

𝜆𝑚𝑎𝑥

2−𝐷𝑝 (12)

In the in-plane condition, the equation (12) can be used for considering of Lo as GDL

thickness.

18

2.5.2 Fractal prediction of effective thermal conductivities

Shi et al., (2008) using the effective thermal conductivities keff by modification of fhe

fractal parallel model equation used air through the samples GDL: TGP-H-060

carbon paper „a‟ and TGP-H-060 carbon paper treated with PTFE „b‟(Shi et al.,

2008). A comparison is made between different theoretical models. In this study, the

parameters of GDL used are listed in Table 2.5; the values of fractal dimension are

derived from scanning electron microscopic micrographs of the two samples.

Comparison between the results of both samples showed that the sample keff 'b' is

greater than the sample 'a‟ according to Table 2.5. The increase is due to the filling of

keff PTFE, which has high thermal conductivity, in the pore space of carbon paper.

Figure 2.6 shows the comparison of the effective thermal conductivity between the

fractal parallel models, series model and the series-parallel layer models of thermal

conductivity. It was showed that for each model giving different results.

Table 2.5: Microstructure and material parameters of the sample „a‟ and „b‟

(Shi et al., 2008)

Parameter Sample „a‟ Sample „b‟ Description

λmax 8 x 10-5

m 7 x 10-5

m Maximum pore diameter

λmin 3.079 x 10-8

m 1.487 x 10-8

m Minimum pore diameter

Ø 0.78 0.55 Porosity

Lo 1.9 x 10-4

m 1.9 x 10-4

m Thickness of the GDL

Ks1 8 Wm-1

K-1

8 Wm-1

K-1

Thermal conductivity of

carbon fiber

Kg 0.02624 Wm-1

K-1

0.02624 Wm-1

K-1


gas

Ks2 0.25 Wm-1

K-1

0.25 Wm-1

K-1


PTFE

Dp 1.9669 1.9276 Pore area dimension

Dt 1.1447 1.1447 Tortuous dimension

19

2.6 Determination of the required fractal dimension

To define the pore fractal dimension, it can be calculated by various techniques

which are obtained from GDL pore size distribution by using equation (3). Mercury

porosimetry are recommended by Nikooee et al., (2011) to obtain the pore size

distribution. The other techniques is using Scanning Electron Microscope (SEM)

images of diffusion media and fractal image processing conventional methods which

is counting the box can be utilized in the determine of pore fractal dimension (Rama

et al., 2008). The last technique is measured the GDL capillary pressure is curve by

using this equation (13):

𝐼𝑛 𝑑𝑉𝑝

𝑑𝑃 = 𝐷𝑝 − 4 𝐼𝑛𝑃 + 𝐶 (13)

Figure 2.6: A comparison of the effective thermal conductivity between

the fractal parallel models, series model and the series-parallel layer

models of thermal conductivity (Shi et al., 2008)

20

Where Vp can be defined as pore volume related with a certain pressure, P

and C is intercept of the line constant to the data in a log-log plot.

The fractal dimensions of pore size distribution of the GDL material have

been introduced by using a porosimetry technique. In this method, it can be

determine the hydrophilic and hydrophobic pore size distribution (Nikooee et al.,

2011). The equation (3) and SEM method are been used for this fractal analysis.

Figure 2.7 shows the SEM image of a carbon fiber without any coating.

2.7 Determination of tortuous fractal dimension

In the previous study by Nikooee et al., (2011) introduced a new method in the

determination of tortuous fractal dimension in the absence of SEM images. In this

method, the ideas generated by the most representative medium based on the

available data on the pore size distribution and porosity of GDL layer. In advance for

(a) (c)

(b) (d)

Figure 2.7: Scanning electron microscope picture of typical carbon fiber

paper sheets use in fuel cell (a) Toray TGPH-060 CFP with no PTFE;

(b) close-up view of the TGPH-060 CFP with no PTFE; (c) Toray

TGPH-060 CFP with 20% PTFE ; (d) close-up view of the TGPH-060

CFP with 20% (David et al., 2010)

21

construction, the selection of the type of artificial medium must be selected. The

multi-fractal has been used as the representative of the real porous media. A proper

depiction of the real GDL layer will come out from the random multi-fractal model

which can be considered as generalization of simple mono-fractal models. The

similar to the real porous medium in basic characteristics of the generated medium

such as pore size distribution and porosity can produce the most representative multi-

fractal medium. Though, the common algorithms for the generation of multi-fractal

media do not usually match the required pore size distribution and/or porosity, they

have recently been used to shed light into physical characteristics of porous media

such as hydraulic permeability and water retention characteristics (Shi et al., 2012).

Nikooee et al., (2011) used the counting box technique for determination of

tortuous fractal dimension. In the description, it will use the different size of boxes to

cover the tortuous curves. The non-empty boxes needed to accommodate curves

which are then calculated. Finally, the numbers of numbers of boxes versus to the

size of the box are drawn on a log-log and the slope of the line that best fitted to give

the tortuous fractal dimension (Yu et al., 2004) (Nikooee, et al., 2011).

2.8 The modified algorithm for the generation of random multi-fractal

media

At the beginning, the platform on which the pores and solid parts distributed, divided

into cells of different sizes based on known samples GDL pore size. In descending

order, the pore sizes produced. For example, in the first step, the largest pores in the

solid parts of the pores with smaller sizes produced. A similar procedure by Nikooee

et al., (2011) is followed to make the pores of each size (Zamel et al., 2012)

(Nikooee et al., 2011).

The iteration step is the best way for the matching threshold probability so

that the number of the generated pores can reach the actual number of pores in

determining of pore size distribution. The initial value for threshold probability is

considered to be equal to the porosity of GDL.

22

In the next step, the solid parts are divided into cells with specific size smaller

pore size equal to certain smaller are present in the GDL medium. This algorithm is

repeated to generate pore size of the GDL and to match the pore size distribution as

much as possible. Due to the number of pores and solid parts together with the

porosity that can be used to determine the field of initiator, the final generated

medium will have a porosity and pore size distribution in real samples.

Nikooee et al., (2011) mention that to get the distribution of tortuous fractal

dimension, the algorithm was coded and run 100 times for each of three used of

samples which is pure GDL, 12.8% PTFE coated GDL and 32.2% PTFE coated

GDL (Nikooee et al., 2011).

2.9 Measurement the through plane thermal conductivity of the PEM and

GDL

In better understanding of heat transfer in proton exchange membrane fuel cells, the

thermal conductivity of the PEM and GDL must be accurately estimated in both

direction, namely the in-plane and through-plane direction. The steady state method

using through-plane directions for measured the thermal conductivity of gas

diffusion layers GDLs. The temperature measured by using thermocouple in different

level in fuel cell which is the temperature gradient can be define and plotted in graph.

Alhazmi et. al., (2014) have conducted the experiment setup using steady state

method, developing the measure the through-plane thermal conductivity of the

components in PEM and GDL at the different operating temperature. The thermal

conductivities of the GDLs are investigated as a function of the PTFE loading,

temperature and compression pressure (Alhazmi et al., 2014).

23

2.9.1 Effect of compression pressure on the through-plane thermal

conductivity of the gas diffusion layer

When applying the compression pressure, it will have the reduction in the thickness

on the GDL and PTFE. Then, the variation in thickness is almost negligible after

increasing the pressure which indicates that the deformation of the GDL has

„saturated‟. Alhazmi et al., (2014) claimed that there is a hysteresis effect in the

compression curves which signals that there has been a permanent deformation in the

compressed GDL sample. In Figure 2.8, shows the investigated effect of the applied

load on the thermal resistance. It is clear that the thermal resistance values of the

treated GDLs are higher than that of the untreated GDL. This is due to the increase in

the contact resistance between the fibers of the GDL after adding the PTFE

(Khandelwal et al., 2006) (Alhazmi et al., 2014).

Figure 2.8: Measured thermal conductivity of the GDLs at different

compression loads (Alhazmi et al., 2014)

24

2.9.2 Effect of the temperature on the through-plane thermal conductivity in

the PEM

Figure 2.9 shows that the thermal conductivity of Nafion® decreases with increasing

temperature, where the test temperature is defined as the average temperature across

the test specimen. Although the downward trend in thermal conductivity is nearly

within the error bar limit, this behavior of Nafion® may be explained on the basis of

its structure and morphology (Chun et al., 1998) (Zhang et al., 2006).

2.9.3 Effect of the temperature on the through-plane thermal conductivity of

the GDL

The through-plane thermal conductivity of the GDL was found to be significantly

lower than its in-plane thermal conductivity. The through-plane thermal conductivity

of the GDL increasing with the temperature (Nikooee et al., 2011). Figure 2.10

shows the variation of the thermal conductivity for Toray carbon paper (TGP-H-060)

as a function with increasing of temperature

Figure 2.9. Measured thermal conductivity of the membrane as a

function of the temperature for dry Nafion ® membrane (N112 and

N117), error bar calculated at 15% (Alhazmi et al., 2014)

62

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