Pore-network simulation of GDL in PEM Fuel cells

Pore-Network Modelling of Liquid Water Transport
Through The Gas Diﬀusion Layer (GDL)
In PEM Fuel Cells

Sreejith Pulloor Kuttanikkad
Research Engineer
Department of Energy Technologies
CEA Grenoble, France

S. Pulloor Kuttanikkad (CEA/IMFT) Pore Network Modelling of GDL 12.02.2010 1 / 26

Outline

1 Context: Global Energy Issues
2 Alternate Energy Sources: Fuel Cells
Challenges in Fuel Cell Technology
3 Objective of the Work
Main Issue: water management inside PEMFC
Pore-Network Modelling
4 Results
Model Validation (single-phase, two-phase)
Simulation of GDL Degradation
5 Conclusions
6 Recommendations for Future Work


Context: Global Energy Issues
Present situation and challenges

The world’s energy system is
based mainly on fossil fuels such
as oil, gas and coal (80%)
Only small contribution from
renewable and alternate sources
World demand for fossil fuels will continue to grow (source: EIA
world energy outlook 2008)

The major challenges:
Sustainable and secure energy solutions
Concerns over climate change, local pollution
Solution:
Develop alternate/renewable power sources (solar, wind, hydro, bio,
nuclear, hydrogen & fuel cells)!

Fuel Cells: An Alternate Energy Source
What is a fuel cell and Why this technology is important

An electrochemical device that combines hydrogen and oxygen to
produce electricity, with water and heat as its by-product
No combustion (clean, quiet, highly eﬃcient, low/zero emission)

Proton Exchange Membrane (PEM) Fuel cells
High eﬃciency and power density
low operating temperature and low noise

PEM fuel cells are promising power source for
automotive applications!


Challenges in PEM Fuel Cell Technology

Hy
Physical size, dr
gy

og
weight e
lo

n
de
no

liv
ch

er
te

y
n
ge
ro
d
Hy

Cost PEMFC Performance

Hy
dr on
og cti
e du
n
sto p ro
ra Durability en
og
ge dr
Hy Schematic cross-section of a PEM fuel cell showing membrane,
GDL, catalyst layer etc.

Many studies are on-going to improve PEMFCs eﬃciency, reliability and to
reduce its cost especially for transport applications

Nano-scale Micro-scale Macro-scale

Objectives
Focused study on Gas Diffusion Layer (GDL)

Study the degradation mechanisms in GDL (focusing on liquid water
interaction with GDL)
Specifically establish a link between degradation of a GDL at
micro-scale and its influence on the transport properties at
macro-scale using pore-scale modelling (pore-network approach)

Effective
Pe
el Properties r fo
od rm
r km at REV scale an
ce
wo
et m
-n od
re e
Po l

Pore-scale Performance
- Permeability (V,I,t)
- Effective diffusion
- Capillary pressure
- Relative permeability
- etc.

Inputs Comparison
- Structure
- Pore & throat size
with
- Wettability experiments


Main Issues
Role of GDL in water (by-product of the reaction) management

To uniformly distribute the reactant
gases to the surface of catalyst
Delivering water to the membrane and
removing the excess water
Enhanced gas diffusion through the
GDL to the CL is important
Too much product water can reduce
gas transport

Conflicting requirement for achieving higher performance
Water (sufficient) required for proton conductance and cooling
But more water reduces diffusive transport of reactants by flooding the electrodes


Main Issues
Loss of hydrophobic coating over time

To prevent the water ﬂooding, GDL is often treated with hydrophobic agents
(PTFE)
However, loss of PTFE over time is observed

Hydrophobic coating (PTFE)
Hydrophilic pore
Hydrophobic pore

Liquid water
Gas ﬂow

GDL

Polymer Electrolyte Membrane

Flooding and drying phenomena inside the core cell are closely linked to the pore-properties
(wettability characteristics, pore size distribution,..) and to the transport properties of
GDL


Pore Network Modelling, Why and How?
To link the degradation
mechanisms to the macroscopic
transport properties
Models based on continuum
theory (Darcy approach) does not
explicitly account for microscopic
properties
Pore (provides
volume for the flow) Poutlet

Ideal for studying single and two phase

Flow direction
ﬂow at the microscopic scale

Periodic

Periodic
Can model explicit pore scale with
L
minimum computational expense
Do not need to specify parameters
such as Permeability, Relative
Permeability, Diﬀusivity and Capillary Pinlet
Mass conservation in pores
Pressure Curve Throat (provides
resistance to the flow)

Local flux in each throat


Pore-Network Model (PNM) for GDL
Difficulties

PNMs have traditionally been used in geo-science and petroleum
Industrial porous materials are significantly different from soils (highly
porous, partially or fully hydrophobic, thin system, deformations are
significant)
For the modelling of industrial porous materials, the concepts and
algorithms from geo-sciences are employed


Liquid water movement inside GDL
Invasion percolation process

Water invasion in GDL is generally
dominated by capillary eﬀects
Invasion takes place via quasi-static
displacement of a ﬂuid
Invade the pores and throats based on their
Pc potential calculated by the
Young-Laplace equation
2σ cos θ
Pc = Pnw − Pw = −
r
Uniform pressure boundary condition on the
inlet, periodic boundary conditions on the A 3D Network made with pores and throats

lateral sides, and exit condition on outlet


Pore-Network Model Development
A pore-network model has been developed and validated by comparing to
literature data
-6
1 0m
2x
25
40 pores

Z

40
po
re s
s re
po
10

output = effective and relative parameters
Inputs = thickness, size, PSD,
(diffusion, permeability), capillary pressure,
wettability etc. 2-phase flow pattern etc.

Model Validation
Computation of absolute permeability and diﬀusivity

8

7 d = dÑ Ò
+ (dÑ Ü − dÑ Ò )[{−δ ln(z(1 − exp(−1.0/δ)) + exp(−1.0/δ))}1/γ ]
dÑ Ò
= 10µm¸ dÑ Ü = 25µm
6 δ = 0.2¸ γ = 6.0
z=Ö Ò (0, 1)
5

¹℄
4

%
3

2

1

0
0 5 10 15 20 25 30
µÑ℄
K Pinlet − Poutlet ÈÓÖ Ë Þ

Q = − A Pore size distribution for Toray 090, created based on Weibull
µ L distribution

Cinlet − Coutlet
J = −Deff A
L

Air permeability (Toray 090)
Validation: Comparison with literature results

Authors Type of Porosity Thickness K
Study [µm] [×10−12 m2 ]

This work PNM 0.77 252 9.6
Gostick, PNM 0.78 252 9.5
2007
Gostick, Expt. 0.78 290 9.0
2006
Sinha, PNM 0.62 275 5.6
2007
Koido, LBM 0.8 200 9.0
2008


Relative diﬀusivity (Toray 090)
Validation: Comparison with literature results

Deff
Authors Type of Porosity Thickness D0
Study

This work PNM 0.77 252 0.48
Gostick PNM 0.78 252 0.46
2007
Zamel, 2009 Expt. 0.78 370 0.275
Tomadakis Analytical – – 0.62
& Sotirchos,
1993
Bruggeman, Analytical – – 0.6875
1935
Nam & Ka- Analytical – – 0.559
viany, 2003


Two-phase ﬂow validation
Relative permeability, comparison with LBM simulation results

Ê Ð Ø Ú Ô ÖÑ Ð ØÝ
ÙÖÚ ×
1 ½
ÀÝ ÖÓÔ Ó
¸Kwr
ÀÝ ÖÓÔ Ó
¸Kgr
Kwr ´À Ó¸ ¾¼½¼µ
Kgr ´À Ó¸ ¾¼½¼µ
0.8 ¼º

0.6 ¼º
Kwr ¹℄

Kgr ¹℄
0.4 ¼º

0.2 ¼º¾

0 ¼
0 0.2 0.4 0.6 0.8 1
Ë ØÙÖ Ø ÓÒ¸ ËÛ ¹℄


Two-phase ﬂow validation
Pc(s) curve, comparison with experimental results

Ô ÐÐ ÖÝ ÔÖ ××ÙÖ × ÙÒ
Ø ÓÒ Ó Ð ÕÙ Û Ø Ö × ØÙÖ Ø ÓÒ
10000
Û Ø Ö¹ ÒØÖÙ× ÓÒ ´θ = 110, 80µ
Ö¹ ÒØÖÙ× ÓÒ ´θ = 80, 80µ
8000

6000
È ℄

4000
Ô ÐÐ ÖÝ ÈÖ ××ÙÖ

2000

0

−2000

−4000

−6000
0 0.2 0.4 0.6 0.8 1

Fairweather et al 2010


Simulation of degradation mechanism in GDL
One scenario: increase the fraction of hydrophilic pores

Degradation mechanism: One scenario is to increase the fraction of
hydrophilic pores in the network (loss of PTFE coating)
How to simulate such scenario?

Why choose this scenario?
Distribution of PTFE is not uniform
Model GDL with mix of hydrophilic and
within a GDL (manufacturing and
hydrophobic pores
operational eﬀects)

Percolation probability of the network

ÚÓÐÙØ ÓÒ Ó Ø Ô Ö
ÓÐ Ø ÓÒ ÔÖÓ Ð ØÝ

100

80
Ð ØÝ ±℄

60
È Ö
ÓÐ Ø ÓÒ ÔÖÓ

40

20

0

0 10 20 30 40 50 60 70 80 90
ÀÝ ÖÓÔ Ð
ÈÓÖ × ±℄

Let Pc be percolation threshold and p be the fraction of hydrophilic pores
For p > Pc : invasion takes place through a path of hydrophilic pores
For p < Pc : hydrophilic pores do not form a percolating cluster and
some hydrophobic pores are necessarily invaded

Result: Eﬀect of loss in hydrophobicity on saturation along the thickness

Ä ÕÙ Û Ø Ö × ØÙÖ Ø ÓÒ ÔÖÓ Ð ÐÓÒ Ø Ø
Ò ××
0.8
ÀÝ ÖÓÔ Ó
½¼± ÀÝ ÖÓÔ Ð
0.7 ¾¼±
¿¼±
¼±
0.6 ¼±
¼±
¼±
¼±

0.5

0.4

0.3

0.2

0.1

0
0 50 100 150 200 250
×Ø Ò
ÐÓÒ Ø ÓÛ Ö
Ø ÓÒ 10−6 Ñ℄


Result: Eﬀect of loss in hydrophobicity on Sw and Deff

Î Ö Ø ÓÒ Ó × Ù× ÓÒ
Ó
ÒØ Û Ø ÐÓ×× Ò Ý ÖÓÔ Ó
ØÝ
¼º 0.8
Ë´Ûµ
ÈÆÅ
ÖÙ Ñ Ò¸ ǫ1.5 · (1 − s)1.5

¹℄
¼º 0.7

Ø ÖÓÙ
0.6
¹℄

¼º
D0
D

0.5

Û Ø Ö × ØÙÖ Ø ÓÒ Ø Ö
Ù× Ú ØÝ¸

¼º
0.4
¼º¿
0.3

Ø Ú

¼º¾
0.2

Ä ÕÙ
¼º½ 0.1

¼ 0
0 20 40 60 80 100
ÀÝ ÖÓÔ Ð
ÈÓÖ × ±℄

Result: Eﬀect of loss in hydrophobicity on Pc-S curves

Ô ÐÐ ÖÝ ÔÖ ××ÙÖ × ÙÒ
Ø ÓÒ Ó Ð ÕÙ Û Ø Ö × ØÙÖ Ø ÓÒ
10000
ÀÝ ÖÓÔ Ó
¾¼±
8000 ¿¼±
¼±
¼±
6000 ¼±
¼±
¼±
Ô ÐÐ ÖÝ ÈÖ ××ÙÖ È ℄

¼±
ÀÝ ÖÓÔ Ð
4000

2000

0

−2000

−4000

−6000
0 0.2 0.4 0.6 0.8 1

Result: Eﬀect of loss in hydrophobicity on gas relative permeability

Ê Ð Ø Ú Ô ÖÑ Ð ØÝ Ó × × ÙÒ
Ø ÓÒ Ó Û Ø Ö × ØÙÖ Ø ÓÒ
ÀÝ ÖÓÔ Ó
1
¾¼±
¿¼±
¼±
0.8
¼±
¼±
¼±
¼±
¼±
0.6
ÀÝ ÖÓÔ Ð
¹℄Kgr

0.4

0.2

0
0 0.2 0.4 0.6 0.8 1

Major findings and conclusions

Developed a pore-network model to link between pore-scale
degradation mechanism and macro-scale transport properties
Calculated the evolution of effective properties (D,K,Pc,Kr) based on
the scenario that the fraction of hydrophilic pores increase over time
(i.e. loss of PTFE)
Initially no significant effects, but gas diffusion starts to decrease
dramatically as fraction of hydrophilic pores are above the percolation
threshold of the system
Gas diffusion is the most important process for the performance of
PEMFC
Degradation (loss of PTFE) induce reduction in gas diffusion and in
turn affects fuel cell performance
An optimal hydrophobic GDL can be designed based on the
simulation results


Future Work and Broader Implications

Study other degradation mechanisms (e.g, structural changes)
Coupling of the models at various scales and models of various
components

The model as a design tool for better GDL with optimal wettability
characteristics and structure, which in turn helps in improving the
performance of PEMFC
Fuel cell technology has the potential to increase the security of
energy supply (with substantial reduction in emissions)
Fuel cell technology is closely linked to the development of hydrogen
economy
The contributions from alternate and renewable energy sources will
increase rapidly over the years


Pore-network simulation of GDL in PEM Fuel cells

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