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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Electrochimica Actaarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Electrochimica Acta
Article . 2011 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Recent trends and developments in polymer electrolyte membrane fuel cell modelling

Authors: Shah, A.A.; Luo, K.H.; Ralph, T.R.; Walsh, F.C.;

Recent trends and developments in polymer electrolyte membrane fuel cell modelling

Abstract

Modelling and simulation are well-established tools for investigating the physical processes inside a polymer electrolyte membrane (PEM) fuel cell. The early literature paid great attention to steady-state transport phenomena in the main components, which continues to be a focus of ongoing activities. There is, on the other hand, a growing interest in modelling other aspects of fuel cell operation, such as transient performance and degradation phenomena. There has also been a growth in the number of molecular and pore-level studies of transport phenomena in fuel cell components, enabled by developments in simulation techniques and enhancements in computer hardware. Such approaches are capable of representing important small-scale phenomena more faithfully than traditional macroscopic models. This review summarises recent activity in PEM fuel cell modelling, with a focus on detailed physical models, and considers its potential significance. An industrial perspective is also provided, highlighting the current use of modelling in the test and design cycles, and outlining future requirements.

Country
United Kingdom
Keywords

620

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    citations
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    118
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
118
Top 10%
Top 10%
Top 10%