computational fluid dynamics simulation of the flow field...

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:15 No:05 114

151505-9393-IJMME-IJENS © October 2015 IJENS I J E N S

Computational Fluid Dynamics Simulation of the

Flow Field of Direct Methanol Fuel Cells

N. H. Maslan1, M. I. Rosli

1,2*, C. W. Goh

2, M. S. Masdar

1,2

1 Fuel Cell Institute, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia

2 Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan

Malaysia, 43600 UKM Bangi, Selangor, Malaysia

*Corresponding author: [email protected]

Abstract-- Direct methanol fuel cell (DMFC) is a technology

that converts the chemical energy of methanol to electrical

energy. Experiments on DMFC performance are costly and time

consuming. Thus, computational fluid dynamics (CFD)

simulations of DMFC were carried out in this study. The flow

fields of parallel, serpentine, and zigzag were investigated to

visualize the distributions of velocity, pressure, and methanol

mole fraction at the anode and to study the DMFC performance.

DMFC CFD simulations were conducted using ESI CFD-ACE+

software package that includes CFD-GEOM, CFD-ACE-GUI,

and CFD-VIEW. The simulations were then validated by

comparing the power density curve obtained from a literature

review. Physical parameters and dimensions of the model were

also determined based on a literature review. Results show that

the flow field channels exhibited uniform distributions of velocity

and methanol mole fraction, as well as high pressure drop and

improved DMFC performance. The flow field channels with

widths of 1.0, 1.5, and 2 mm were also investigated. The obtained

results indicate that the serpentine flow field with a flow channel

width of 2 mm showed the best performance of DMFC based on

the distributions of velocity, pressure, and methanol mole

fraction.

Index Term-- Direct methanol fuel cell (DMFC); flow field;

methanol mole fraction; velocity; pressure

1. INTRODUCTION

In a direct methanol fuel cell (DMFC), the anode flow field

has two functions. The first function is to provide a channel

for methanol to flow on the membrane electrode assembly

(MEA) surface. Continuous supply of methanol to cell and

uniform methanol distribution on the MEA surface are

important for DMFC efficiency [1]. The design of flow field

plays an important role in meeting both of these requirements.

The second function is to provide a passage for the removal of

CO2 produced from the reaction [2]. The efficient removal of

CO2 is essential in DMFC design [3]. A number of studies

demonstrated that the geometry of the flow field affects the

mass transport of methanol to the diffusion layer and DMFC

performance [1, 4-6]. CO2 gas bubbles and pressure drop are

also affected by the geometry of the flow field. Thus,

optimizing the anode flow field is significant to achieve an

optimal design of DMFC. In this study, five different flow

field geometries, namely, a zigzag flow, a parallel flow, and

three different serpentine flows with different flow channels,

were investigated.

Computational fluid dynamics (CFD) is a fluid

mechanic branch that uses numerical methods and algorithms

to solve and analyze problems related to fluid flow [7]. CFD is

used in fuel cell development to investigate the physical and

chemical processes that occur in a fuel cell numerically,

particularly the efficiency of multi-component transport in

reactants and oxidants and its effects on the electrochemistry

kinetics and performance of a fuel cell. CFD analysis can

provide the performance characteristic of fuel cells under

various operating conditions, catalysts, and membranes,

among others. This analysis reduces the development cost by

reducing the operating cost.

This study focused on the CFD simulation

development of DMFC by using ESI CFD-ACE+ software.

The flow field design that can optimize DMFC performance

was determined based on the distributions of velocity, pressure,

and methanol concentration in DMFC. The flow field patterns

used were serpentine, zigzag, and parallel. The effects of

anode channel width on DMFC performance were also

investigated to determine its optimal performance. The widths

of serpentine flow field channels used were 1.0, 1.5, and 2.0

mm. CFD simulations of DMFC were conducted using ESI

CFD-ACE+ software to describe and analyze the flow pattern

distributions of velocity, pressure, and methanol mole fraction

by changing the anode flow field patterns and channel widths.

2. METHODOLOGY

CFD simulation in DMFC was developed by conducting three

main steps, namely, pre-processing using CFD-GEOM,

solutions and calculations using CFD-ACE-GUI, and post-

processing using CFD-VIEW.

2.1. Pre-processing: CFD-GEOM software

A geometry was initially created based on the dimension

determined using CFD-GEOM. The serpentine, zigzag, and

parallel (PFF) flow fields that have different channel widths

were also generated using CFD-GEOM. A high-quality

geometry produces exact dimensions, which are then used for

the calculation in the subsequent step. Figure 1 shows the

configuration of the layers in DMFC geometry. Table I

presents the geometry dimensions of DMFC. A zigzag flow

field, a PFF, and three serpentine flow fields with different

widths were created using CFD-GEOM for CFD simulation.



NOMENCLATURE Abbreviations

J Current density, A m-2

CFD Computational fluid dynamics

J0 Reference exchange current density, A m-3

DMFC Direct methanol fuel cell

p Pressure, Pa IGL Ideal gas law

S/V Surface to volume ratio, m2 m

3 MEA Membrane electrode assembly

T Temperature, K MKT Mixed kinetic theory

Greek letters MOR Methanol oxidation reaction

α Transfer coefficient ORR Oxygen reduction reaction

Γ Mass diffusivity, kg m-1

s-1

PFF Parallel flow field

γ Concentration parameter Sc Schmidt number

ɛ Porosity SSFF Serpentine flow field

κ Permeability, m2 Zigzag Zigzag flow field

μ Viscosity, kg m-1

s-1

Subscripts

ρ Density, kg m-3

a Anode side of the membrane

σ Electrical conductivity, Ω-1

m-1

c Cathode side of the membrane

τ Bruggeman factor CH3OH Methanol

O2 Oxygen

Fig. 1. Layers available in a DMFC geometry

Table II shows the depth of each layer. All five

DMFC geometries were created. A triangular mesh was used

for mesh generation (meshing). The dimension of each

geometry was 40 mm × 40 mm. The operating parameters

used are shown in Table III.

2.2. Solution and calculation: CFD-ACE-GUI software

After DMFC geometry was created, CFD-ACE-GUI was used

to complete the calculation based on operating conditions and

the reaction of chemical species in DMFC. Flow, chemistry,

and electric modules were activated in CFD-ACE-GUI

software to begin the calculation based on the selected

modules. The chemical species available in DMFC were

inserted from the software database. Hydrogen ions were

modeled as “Bulk Species.” The reactions and parameters

incorporated are as follows.

Anode:

(1)

Anode reference exchange current density, J0 = 1.2×10

6 A m

-3

[8]

Anode transfer coefficient, αa = 0.5 [8]

Cathode:

(2)

Cathode reference exchange current density, J0 = 1407 A m

-3[8]

Cathode transfer coefficient, αc = 1.55

The parameter settings for each volume, porous

medium volume, boundary, and initial conditions are shown in

Tables IV, V, VI, and VII, respectively. When all parameters

were set, the simulation was run using CFD-ACE-GUI.

Table I

Geometry dimensions of selected anode flow fields

Flow Field SSFF1 SSFF2 SSFF3 PFF Zigzag

Channel width (mm) 2.00 1.50 1.00 2.00 1.50

Channel depth (mm) 2.00 2.00 2.00 2.00 2.00

Cross section area (mm2) 4.00 3.00 2.00 4.00 3.00

Channel length (mm) 425.00 569.00 843.40 422.40 562.50

Value of exposed channel to

membrane area (mm2)

850.00 858.00 847.40 844.80 851.25

Open ratio (%) 53.13 53.63 52.96 52.80 53.20



Table II

List of thickness for each layers [8]

Layer Thickness (mm)

Anode current collector 0.500

Anode channel 2.000

Anode diffusion layer 0.190

Anode catalyst layer 0.030

Membrane 0.127

Cathode catalyst layer 0.030

Cathode diffusion layer 0.190

Cathode channel 2.000

Cathode current collector 0.500

Table III

Operating parameter used in DMFC [1]

Operating parameter Value

Methanol concentration 1 M

Operating temperature 333 K

Active area dimension 4.0×4.0 cm

Methanol inlet flow rate 2.0 ml min-1

2.3. Post-processing: CFD-VIEW software

In the last stage, CFD-VIEW was used to visualize and

analyze CFD images. The distributions of velocity, pressure,

and methanol mole fraction in the anode channel were

determined.

Table IV

List of parameter setting for each volume conditions [9, 10]

Volume Name ρ (kg/m3) μ (kg/m.s) σ (Ω

-1 m

-1) Γ (kgm

-1s

-1)

Anode catalyst layer IGL MKT 4.2 Sc = 0.7

Anode channel 960 3.49×10-4

1.0×10-20

Sc = 0.7

Anode collector 2698.9 - 3703 -

Anode diffusion layer IGL MKT 1.0×10-20

Sc = 0.7

Cathode catalyst layer IGL MKT 1.0×10-20

Sc = 0.7

Cathode channel IGL MKT 1.0×10-20

Sc = 0.7

Cathode collector 2698.9 - 3703 -

Cathode diffusion layer IGL MKT 1.0×10-20

Sc = 0.7

Membrane IGL MKT Membrane model Sc = 0.7

Table V

List of porous media setting for each volume condition [9, 11]

Volume Name ε κ Reaction S/V Pore Diffusivity σ

Anode catalyst layer 0.3 1.0×10-14

MOR 1000 1.5×10-6

Bruggeman (1.5) 53

Anode diffusion layer 0.7 2.0×10-12

- - 1.0×10-6

Bruggeman (1.5) 53

Cathode catalyst layer 0.3 1.0×10-14

ORR 1000 1.5×10-6

Bruggeman (1.5) 53

Cathode diffusion layer 0.7 2.0×10-12

- - 1.0×10-6

Bruggeman (1.5) 53

Membrane 0.3 2.0×10-18

- - 1.0×10-6

Bruggeman (5) 0.7

Table VI

List of parameter setting for boundary conditions

Boundary

Condition Flow Chemistry Electric

Anode channel

inlet

y-direction velocity = 0.008333 m s-1

Pressure, P = 0 Pa

Temperature, T = 333 K

Mixture =

methanol –

Cathode channel

inlet

y-direction velocity = 0.10 m s-1

Pressure, P = 0 Pa


Mixture =

humid air –

Anode channel

outlet

Fixed pressure, P = 0 Pa

Temperature T = 333 K

Mixture =

methanol –

Cathode channel

outlet

Fixed pressure, P = 0 Pa


Mixture =

humid air –

Anode wall

collector – –

Fluid phase: fixed current density, J = 0 Am-2

Porous phase: fixed potential, Voltage = 0 V

Cathode wall

collector – –

Fluid phase: fixed current density, J = 0 Am-2

Porous phase: fixed potential, Voltage = - 0.6 V

(adjustable in order to get the power density curve)



Table VII

List of parameter setting for initial conditions

Initial Volume

Conditions Flow Chemistry Heat

Anode catalyst layer Pressure = 90 000 Pa Mixture = humid air Temperature = 333 K

Cathode channel Pressure = 90 000 Pa Mixture = humid air Temperature = 333 K

Anode channnel – Mixture = methanol Temperature = 333 K

Anode catalyst layer – Mixture = nitrogen Temperature = 333 K

Anode diffusion layer – Mixture = nitrogen Temperature = 333 K

Cathode diffusion layer – Mixture = nitrogen Temperature = 333 K

Membrane – Mixture = nitrogen Temperature = 333 K

3. RESULTS AND DISCUSSION

3.1 Comparisons of power density curve between

experiment and simulation

The power density curve was plotted for comparison by using

the simulation data of single-serpentine flow field (SSFF) 1

geometry and the experimental data by Yang and Zhao [1]

(Figure 2). The simulation results and experimental data in

Figure 2 present the same patterns in power density curve

from cell voltages of 0 V to 0.5 V. According to Yang and

Zhao [1], DMFC has a maximum power density of 54 mW

cm−2

at a cell voltage of 0.27 V. In SSFF1 simulation, a

maximum power density of 45 mW cm−2

was reached at a cell

voltage of 0.55 V. The maximum power density difference

was 9 mW cm−2

. The simulation results were not 100%

consistent with the experimental data. However, Figure 2

shows that the simulation results are similar to the

experimental data in a fuel cell operating at low cell voltages

from 0 to 0.5 V. Therefore, the DMFCs operated at a cell

voltage of 0.261 V were used to visualize the velocity,

pressure, and methanol mole fraction distributions in CFD.

The same parameters were used in all simulated geometries.

Fig. 2. Comparison between simulation and experiment of SSFF1

3.2 Effects of anode flow field design on DMFC

performance

In this study, three different flow fields with the same open

ratio, that is, 53%, were simulated at a voltage cell of 0.261 V

to investigate the velocity, pressure, and methanol mole

fraction distributions by using CFD.

3.2.1 Velocity distribution of different flow fields

Figures 3, 4 and 5 show that the velocity distribution of SSFF1

was uniform and had a high magnitude along the channel

while at PFF showed highly non-uniform velocity distribution.

These conditions at SSFF1 benefit the removal of produced

CO2 and increase the mass transport of methanol from the

flow channel to the diffusion layer [4], which increases DMFC

performance. Meanwhile, the velocity in PFF shows a

stagnant zone in the central regions but high values at lateral

channels [3]. This affects the collected CO2 gas in the PFF

anode channel. Thus, the effective contact area between

methanol and the diffusion layer becomes small [1]. The flow

velocity of PFF decreases drastically and differs in each

channel because of the free excess methanol. This

phenomenon affects DMFC performance. The zigzag flow

field is a combination of serpentine flow field and PFF. The

results obtained were similar to PFF because the velocity

distribution was not uniform in the zigzag flow field.

Fig. 3. Velocity distribution of SSFF1



Fig. 4. Velocity distribution of PFF

Fig. 5. Velocity distribution of zigzag

3.2.2 Pressure distribution of different flow fields

Figures 6, 7, and 8 present the pressure distributions of SSFF1,

PFF and zigzag flow field, respectively. The SSFF1 design

showed a uniform pressure distribution. SSFF1 exhibited the

highest pressure drop (7.506 Pa). The pressure drop values for

the zigzag flow field and PFF were 1.554 and 0.4961 Pa,

respectively. These results show that PFF is only 1/15 of

SSFF1. SSFF1 and zigzag flow fields require high pressure

drop but not the PFF [12]. SSFF1 is expected to have a better

DMFC performance because its higher pressure drop

contributes to increased efficiency of methanol transport.

Hence, the removal of CO2 gas becomes easier. Its higher

pressure drop also contributes to a uniform fluid velocity

distribution, which leads to increased DMFC performance.

The pressure drop in the zigzag flow field was higher than that

in PFF; thus, the former is expected to perform better than the

latter.

Fig. 6. Pressure distribution of SSFF1

Fig. 7. Pressure distribution of PFF



Fig. 8. Pressure distribution of zigzag

3.2.3 Methanol mole fraction distribution

The methanol mole fraction distribution along the channel is

given in Figures 9, 10 and 11. As shown in Figures 9, the

methanol mole fraction at SSFF1 decreased from 1 to 0.864 at

the anode channel outlet, whereas that along the PFF channel

was considered high because it decreased from 1 to 0.9281

only (Figure 10). Hence, only a small amount of methanol

reacted to generate electricity. The zigzag flow field exhibited

the highest drop of methanol mole fraction, which is from 1 to

0.6518. This result may be due to the methanol crossover. The

methanol concentration is declined along the channel due to

the electrochemical reaction [3].

Fig. 9. Methanol mole fraction distribution of SSFF1

Fig. 10. Methanol mole fraction distribution of PFF



Fig. 11. Methanol mole fraction distribution of zigzag

3.2.4 Comparisons of SSFF1, PFF and zigzag flow field

Based on the comparisons of the velocity, pressure, and

methanol mole fraction distributions in SSFF1, PFF, and

zigzag flow field, SSFF1 showed the most uniform velocity

distribution, the highest pressure drop, and the most uniform

methanol mole fraction distribution. Thus, SSFF1 is assumed

to have the highest DMFC performance. The power densities

simulated from all flow fields are shown in Figure 12. SSFF1

had the highest power density, which is 44.76 mW cm−2

(Figure 12). The zigzag flow field produced a power density

of 38.41 mW cm−2

, and PFF showed the lowest performance

of 37.81 mW cm−2

. These results fit with the expected results

based on the CFD simulation indicated earlier.

Fig. 12. Power density comparisons of SSFF1, PFF and Zigzag at voltage

of 0.261 V

3.3 Effects of channel width

The serpentine flow field exhibited the best DMFC

performance among other flow fields. For the subsequent

simulation, three serpentine flow field designs with different

channel widths (i.e., 2.0, 1.5, and 1.0 mm) were studied. All of

these designs have an open ratio of 53%, which indicates that

the total contact area between methanol and the anode

diffusion layer is similar.

3.3.1 Velocity distribution

Figures 13, 14, and 15 show that the SSFF1 design with the

highest width of 2.0 mm had the most uniform fluid velocity

distribution. The fluid velocity of the SSFF2 and SSFF3

rapidly decreased along the channel. This phenomenon may be

due to the channel length, which increased the friction

between the liquid and wall. The high flow velocity in the

wide channel increased the methanol potential to penetrate the

anode diffusion layer effectively and hence increased the

overall DMFC efficiency. Even velocity distribution is

proportional to the performance of fuel cell [2].

Fig. 13. Velocity distribution of SSFF1 (channel width = 2.0 mm)





3.3.2 Pressure distribution

The pressure drop and its distribution are significant in order

to decide the pump capacity in DMFC system [2]. Figures 16,

17, and 18 show that the pressure distributions in SSFF1 and

SSFF2 were uniform and had pressure drop values of 7.506

and 6.354 Pa, respectively. The pressure drop for SSFF3 was

17.21 Pa. The pressure distribution of SSFF3 was not constant,

and the pressure dropped significantly before reaching the

anode outlet. This phenomenon caused an ineffective removal

of CO2 gas. Both SSFF2 and SSFF3 had high pressure values

near the anode channel inlet. A high pressure drop and a

uniform pressure distribution ensure a high and uniform

distribution of flow velocity along the channel to maintain

DMFC performance. Hence, SSFF1, which has a constant

pressure distribution and a moderate pressure drop, that is,

7.506 Pa, is expected to perform well.

Fig. 16. Pressure distribution of SSFF1 (channel width = 2.0 mm)





3.3.3 Methanol mole fraction distribution

SSFF1 had a constant distribution of methanol mole fraction

of 1 to 0.864 at the anode channel outlet (Figures 19, 20, and

21). This result shows that methanol reacted to generate

electric current. The methanol mole fraction distributions of

SSFF2 and SSFF3 were not uniform. The mole fraction of

methanol dropped significantly before reaching the anode

channel outlet. Analysis of pressure and velocity distributions

showed that both SSFF2 and SSFF3 had a non-uniform

distribution. Based on this situation, a high possibility exists

that SSFF2 and SSFF3 encountered methanol crossover

because of the poor methanol transport along the anode

channel. Therefore, the SSFF1 design with a channel width of

2.0 mm has the best DMFC performance. The presence of the

CO2 in the channel also lead to the limited diffusion rate of

methanol [8] and hence contributes to the non-uniform

distribution in the anode channel.

Fig. 19. Methanol mole fraction distribution of SSFF1 (channel width = 2.0

mm)

Fig. 20. Methanol mole fraction distribution of SSFF2 (channel width = 1.5

mm)



Fig. 21. Methanol mole fraction distribution of SSFF3 (channel width = 1.0 mm)

3.3.4 Power density comparisons

Comparison of power density shows that SSFF1 had uniform

velocity, pressure, and methanol mole fraction distributions.

Therefore, SSFF1 is expected to have the highest performance

of DMFC. The simulated power densities for all flow fields

are shown in Figure 22. In this figure, SSFF1 had the highest

value of power density (44.76 mW cm−2

), followed by SSFF3

(40.26 mW cm−2

) and then SSFF2 (38.19 mW cm−2

). These

results fit with the expectations based on the CFD indicated

earlier, that is, SSFF1 with an optimum channel width of 2.0

mm exhibited a better performance than those with channel

widths of 1.0 and 1.5 mm.

Fig. 22. Power density comparisons of SSFF1, SSFF2 and SSFF3 at

voltage of 0.261 V

3.3.5 Comparisons of methanol mole fraction distribution

at anode catalyst layer

The methanol mole fractions in the anode catalyst layer were

compared among SSFF1, SSFF2, and SSFF3 to investigate the

cause of high power density production in small channel width

of SSFF3 compared to SSFF2. Figures 23, 24, and 25 show

the methanol mole fractions in the anode catalyst layers for

SSFF1, SSFF2, and SSFF3, respectively. The methanol mole

fraction values in SSFF1 were the highest among the three

flow fields (Figure 23). This result indicates that a

considerable amount of methanol react to generate electricity;

thus, the power density was significantly high (Figure 23). The

methanol mole fraction of SSFF2 (Figure 24) was lower than

that of SSFF3 (Figure 25), which implies that SSFF3 has a

higher power density than SSFF2. Compared to Figures 19, 20

and 21, methanol mole fraction distributions at flow field is

much higher than in catalyst layer of Figures 23, 24 and 25.

Fig. 23. Methanol mole fraction at anode catalyst layer of SSFF1





3.4 Comparisons of power density for all simulated

geometries

Based on the previous analysis, the simulation results showed

that SSFF1 with an optimum channel width of 2 mm had the

best DMFC performance. The power densities of all simulated

geometries were compared. Figure 26 shows the simulation

data for the comparisons. SSFF1 obtained the highest power

density of 44.76 mW cm−2

, followed by SSFF3. The power

densities produced in the zigzag flow field and SSFF2 were

38.42 and 38.19 mW cm−2

, respectively. PFF exhibited the

least power density of 37.81 mW cm−2

. These simulation

results reveal that PFF is not appropriate for DMFC flow field.

The designs developed in this study are suitable for the

fundamental understanding of the flow field in DMFC and its

visualization of velocity, pressure and methanol mole fraction

distributions.

Fig. 26. Power density comparisons of different flow fields at a voltage of

0.261 V

4. CONCLUSIONS

The results show that serpentine flow fields had uniform

velocity and methanol mole fraction distributions and a high

pressure drop. Such flow fields exhibited the best DMFC

performance compared with PFF and zigzag flow field. The

serpentine flow field with 2 mm channel width had the best

DMFC performance based on its velocity, pressure, and

methanol mole fraction distributions, and it is the best flow

field simulated in this study. Furthermore, non-uniform

distribution of velocity, pressure and methanol mole fraction

in PFF and zigzag flow fields confirm the importance of the

flow-field in a DMFC design.

ACKNOWLEDGMENT

The authors gratefully acknowledge the financial support of

this work by Dana Lonjakan Penerbitan of Universiti

Kebangsaan Malaysia (DLP-2014-007) and Fundamental

Research Grant Scheme of Ministry of Higher Education

Malaysia (FRGS/1/2013/TK07/UKM/02/1).

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