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Record W3212140349 · doi:10.3390/electronics10222834

A Novel Opposition-Based Arithmetic Optimization Algorithm for Parameter Extraction of PEM Fuel Cell

2021· article· en· W3212140349 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueElectronics · 2021
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsProton exchange membrane fuel cellAlgorithmBenchmark (surveying)Computer scienceFuel cellsMathematical optimizationMathematicsEngineering

Abstract

fetched live from OpenAlex

The model-identification and parameter extraction are a well-defined method for modeling and development purposes of a proton exchange membrane fuel cell (PEMFC) to improve the performance. This paper introduces a novel opposition-based arithmetic optimization algorithm (OBAOA) for identifying the unspecified parameters of PEMFCs. The cost function is defined as the sum of the square deviations between the experimentally measured values and the optimal achieved values from the algorithm. Ballard Mark V PEM fuel cell is employed and analyzed to demonstrate the capability of the proposed algorithm. To demonstrate system efficiency, simulation results are compared to those of other optimizers under the same conditions. Furthermore, the proposed algorithm is validated through benchmark functions. The final results revealed that the proposed opposition-based arithmetic optimization algorithm can accurately retrieve the parameters of a PEMFC model.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.199
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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