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Record W1850771495 · doi:10.1016/j.egypro.2015.07.267

Optimization of Design Parameters for an Open-cathode Polymer Electrolyte Fuel Cells Stack Utilizing Taguchi Method

2015· article· en· W1850771495 on OpenAlexaff
Agus P. Sasmito, Jundika C. Kurnia, Tariq Shamim, Arun S. Mujumdar

Bibliographic record

VenueEnergy Procedia · 2015
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsStack (abstract data type)Taguchi methodsElectrolyteCathodeFuel cellsMaterials sciencePolymerChemical engineeringComputer scienceNuclear engineeringMechanical engineeringProcess engineeringAutomotive engineeringEngineeringChemistryElectrical engineeringComposite materialElectrodeOperating system

Abstract

fetched live from OpenAlex

The design of open-cathode polymer electrolyte fuel cells (PEFC) stacks with forced-convection requires a careful consideration on the geometrical and operating conditions as well as the characteristic of PEFC stacks and fan used. The operating point should be located at the intersection point between the fan and the stack characteristic curve. This paper evaluates the effect of key parameters and operating conditions on the system characteristic and stack performance of forced convection PEFC by utilizing computational fluid dynamic approach and Taguchi statistical method. A validated three dimensional open-cathode PEFC stack model with fan and immediate ambient were employed together with an L 27 orthogonal array (OA) of Taguchi matrix of six factors and three level designs to determine the optimum combination of parameters as well as their interactions. The result indicates that fuel cell length plays important role on determining the fuel cell performance in term of current density and net power. Optimum combination of design and operating parameters were obtained with the objective function of maximizing net power generated by stack by taking into account the parasitic loads.

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 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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.459
Threshold uncertainty score0.731

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 head0.054
GPT teacher head0.276
Teacher spread0.221 · 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 teacher head, not a consensus.

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

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

Citations10
Published2015
Admission routes1
Has abstractyes

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