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Record W2071628616 · doi:10.1002/fuce.200400072

Modelling and Analysis of Electro‐chemical, Thermal, and Reactant Flow Dynamics for a PEM Fuel Cell System

2005· article· en· W2071628616 on OpenAlexafffund
Mohammad Junaid Khan, M. Tariq Iqbal

Bibliographic record

VenueFuel Cells · 2005
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProton exchange membrane fuel cellStack (abstract data type)ElectrolyteMATLABComputer scienceComputationSystem dynamicsFuel cellsFlow (mathematics)ThermalTransport phenomenaTransient (computer programming)Steady state (chemistry)Energy balanceMechanicsBiological systemNuclear engineeringChemistryThermodynamicsChemical engineeringAlgorithmElectrodeEngineeringPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract In this paper an approach for the dynamic modelling of polymer electrolyte membrane fuel cells is presented. A mathematical formulation based on empirical equations is discussed and several features, exhibiting dynamic phenomena, are investigated. A generalized steady state fuel cell model is extended for the development of a method for dynamic electrochemical analysis. Energy balance and reactant flow dynamics are also explained through physical and empirical relationships. A well‐researched system (Ballard MK5‐E stack based PGS‐105B system) is considered in order to understand the operation of a practical fuel cell unit. Matlab‐SIMULINKTMhas been used in simulating the models. The proposed method appears to be relatively simple and consequently requires less computation time. Simulation results are compared with available experimental findings and a good match has been observed.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.175
Teacher spread0.170 · 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

Citations171
Published2005
Admission routes2
Has abstractyes

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