Computer methods for performance prediction in fuel cells

Beale, Steven; Lin, Y.; Zhubrin, S. V.; Dong, Wei

doi:10.1016/s0378-7753(03)00065-x

Cited by 54 publications

(28 citation statements)

References 10 publications

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“…Previous DRA studies on SOFCs involved prediction of the flow fields only [5] and, more recently, the cell=stack temperature assuming a uniform heat source corresponding to an idealized case [6,7]. This article is the first publication in which the entire electrochemical process, including nonuniform heat generation and mass transfer, is considered.…”

Section: Problem Statementmentioning

confidence: 99%

A Distributed Resistance Analogy for Solid Oxide Fuel Cells

Beale

Zhubrin²

2005

Numerical Heat Transfer, Part B: Fundamentals

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A distributed resistance analogy for solid oxide fuel cells Beale, Steven; Zhubrin, S. V.Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en Numerical Heat Transfer, Part B: Fundamenals, 47, 6, 2005 Publisher's version / la version de l'éditeur: This article maybe used for research, teaching and private study purposes. Any substantial or systematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden.The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. The method of distributed resistances is outlined for performance calculations in solid oxide fuel cells. The domain is discretized using a multiply shared space method. Both potentiostatic and galvanostatic conditions are considered. Mass transfer effects on the heat transfer coefficients and the Nernst potential are taken into consideration. Calculations, for one cell and for a 10-cell stack, are compared to those obtained using a detailed numerical method. Agreement is very good. It is concluded that the distributed resistance analogy may be used to predict transport phenomena in fuel cell stacks at a fraction of the computational cost required for conventional means.

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Section: Problem Statementmentioning

confidence: 99%

A Distributed Resistance Analogy for Solid Oxide Fuel Cells

Beale

Zhubrin²

2005

Numerical Heat Transfer, Part B: Fundamentals

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“…Early researchers often wrote algorithms [1] in source-languages such as FORTRAN and C: More recently, fuel cell workers typically employed commercial computational fluid dynamics (CFD) packages such as PHOENICS [2,3], Fluent [2,[4][5][6][7][8][9], Star-CD [10,11], COMSOL multi-physics [12][13][14][15] and others. Extensive reviews of SOFC modeling activities may be found in Kakaç et al [16], Andersson et al [17], Wang et al [18], Hajimolana et al [19], and Grew and Chiu [20].…”

Section: Introductionmentioning

confidence: 99%

Validation of a Solid Oxide Fuel Cell Model on the International Energy Agency Benchmark Case with Hydrogen Fuel

Beale

Pharoah

2014

Fuel Cells

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A detailed model of a solid oxide fuel cell was developed with an object-oriented open-source computational fluid dynamics code based on a finite-volume method. The methodology is derived from a local Nernst equation with associated irreversible losses. Calculations were performed with the International Energy Agency benchmark case #1 with hydrogen as fuel, for co-flow, counter-flow, and cross-flow. While agreement with the results of previous workers was satisfactory, a number of shortcomings with the benchmark case were identified and highlighted. These include over-simplified electro-chemical kinetics, neglect of porous transport layers, and ambiguities associated with the very low flow rates prescribed for the benchmark case.

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“…Nevertheless, in order to provide a deeper understanding of all processes in a fuel cell, multi-component species transport and heat transfer phenomena should be modelled by solving the wellknown conservation equations for mass, momentum, species and electrical quantities [6,13,14,35]. With the introduction of commercial Computational Fluid Dynamic (CFD) codes, mainly based on finite volumes technique, it was possible to remove some of the approximations used in initial models [7,[36][37][38][39][40][41][42][43][44][45], by solving the above mentioned conservation equations [7,22,37,39,41,[45][46][47][48][49][50]. Nevertheless, commercial CFD programs had to be further developed in order to predict the electrochemical quantities of interest in fuel cells, often making it difficult to overcome some assumptions because of the reduced flexibility of these codes [7,9,36].…”

Section: Introductionmentioning

confidence: 99%