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Record W2075060500 · doi:10.1002/fuce.200400054

Dynamic Modelling and Simulation of a Fuel Cell Generator

2004· article· en· W2075060500 on OpenAlexafffund
M.J. Khan, M. Tariq Iqbal

Bibliographic record

VenueFuel Cells · 2004
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)CapacitancePID controllerControl theory (sociology)Generator (circuit theory)InverterAutomotive engineeringPower (physics)VoltageMaterials scienceNuclear engineeringComputer scienceElectrical engineeringFuel cellsEngineeringMechanical engineeringChemistryChemical engineeringPhysicsThermodynamicsTemperature controlElectrode

Abstract

fetched live from OpenAlex

Abstract In this paper the dynamics of a polymer electrolyte membrane (PEM) fuel cell system and its associated power electronics are modelled and simulated. The fuel cell system model includes the dynamics of reactant flow, membrane resistance and charge, double layer capacitance as well as steady state equations. The DC Output of the 5 kW fuel cell stack is converted to 120 V, 60 Hz AC by a pulse width modulated inverter. The inverter output is held constant by a PID controller. Matlab‐Simulink™ and Power System Blockset (PSB) are used for the modelling and simulation of the fuel cell generator. The effects of load variation on output voltage, current, and fuel cell reactant flows are investigated. Simulation results indicate that variations in the systems electrical and physical parameters are within acceptable limits. Such a fuel cell generator could be used in grid connected and stand‐alone applications.

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)0.001
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: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.190
Teacher spread0.183 · 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

Citations45
Published2004
Admission routes2
Has abstractyes

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