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Record W2273888141 · doi:10.1149/ma2015-01/28/1662

Improvement of the Process Model for the Ohmic Loss of the Proton Exchange Membrane Fuel Cell

2015· article· en· W2273888141 on OpenAlexaff
Seyed Mohammad Rezaei Niya, Ryan K. Phillips, Mina Hoorfar

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProton exchange membrane fuel cellOhmic contactProcess (computing)CatalysisWater transportElectrical impedanceMembraneChemistryFlux (metallurgy)Layer (electronics)Equivalent circuitMechanicsMaterials scienceThermodynamicsChemical engineeringControl theory (sociology)EngineeringNanotechnologyComputer sciencePhysicsVoltageElectrical engineeringMetallurgyEnvironmental engineering

Abstract

fetched live from OpenAlex

The process model is defined as an analytical solution for the governing equations of an electrochemical system which is used to predict the impedance characteristics of the system and identify the relation between different parts of the measured impedances and physicochemical properties and operating conditions [1, 2]. Despite various attempts made towards presenting a process model for proton exchange membrane (PEM) fuel cells, no complete process model is introduced yet [1, 3]. We have focused on developing such a process model by separately modeling the three losses: ohmic, activation, and mass transport. Our first process model of the ohmic loss [3] required an estimation of the water concentration in the catalyst layer which cannot be readily determined experimentally. Moreover, while the water transport in the catalyst layer was considered in the governing equations of the membrane, the water flux from the membrane towards the catalyst layer was not considered in the water flux equation of the catalyst layer. In this study, the ohmic-loss model presented before is improved by calculating the water concentration in the catalyst layer from other inputs as well as considering the water flux from the membrane in the catalyst layer equation. In the new model, the equivalent circuit extracted from the theoretical relation determined for the impedance of the ohmic loss still has the same format as the model presented before [3] (see Figure 1). However, the elements of the equivalent circuit have different theoretical definitions. In the following equations, subscripts m, cl, and GDL represent the membrane, catalyst layer and cathode gas diffusion layer properties, respectively. l, D, M, F, T and j denote the thickness, diffusion coefficient, molecular mass, Faraday’s constant, temperature and current density, respectively. ρ, σ and λpresent the density, electrical conductivity and water content, respectively. Also, a = 0.5193 exp(1268(1/303-1/T)) b = 0.326 exp(1268(1/303-1/T)) α = 2.5/(22F) β = ρmDm/Mm The model is compared against the measured impedances obtained for a 7.98-cm2cell operated at the 90% relative humidities for the anode and cathode, three different temperatures (65 °C, 70 °C and 75 °C), and two potentials (0.7 V and 0.75 V). The measured impedances are reported in the frequency span of 10-0.01 Hz as the ohmic loss is dominant in this range [3]. The results in Figure 2 show an excellent agreement between the theoretical predictions and the measured impedances. References S.M. Rezaei Niya, M. Hoorfar, J. Power Sources, 240, 281 (2013). P. Agarwal, M. Orazem, J. Electrochem. Soc., 139, 1917 (1992) S.M. Rezaei Niya, M. Hoorfar, Electrochimica Acta, 120, 193 (2014). Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.222
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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