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Record W2737892866 · doi:10.1063/1.4992192

Lattice-Boltzmann simulation of multi-phase phenomena related to fuel cells

2017· article· en· W2737892866 on OpenAlexaff
Alireza Akhgar, Behnam Khalili, Belaid Moa, Mohammad Rahnama, Ned Djilali

Bibliographic record

VenueAIP conference proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLattice Boltzmann methodsMaterials scienceParticle (ecology)CatalysisPorosityChemical engineeringEconomies of agglomerationParticle sizeProcess engineeringChemistryComposite materialMechanicsEngineeringOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Fuel cells are devices that allow conversion of the chemical potential of a fuel and oxidant to produce electricity. A key component of a fuel cell is the catalyst layer, which facilitates the electrochemical reaction and where transport of reactants, charge, and byproduct heat and water take place. The structure and morphology of the catalyst layer determine its effectiveness and, in turn, strongly impact the overall performance and cost of a fuel cell. This paper discusses two central issue related to catalyst layers involving two-phase flow: liquid water transport in the catalyst layer during fuel cell operation, and fabrication of the catalyst layer from colloidal inks where a process of particle agglomeration takes place and eventually determines the final catalyst layer structure. Insight into these two issues are obtained using lattice-Botzmann based multi-phase simulations with formulations tailored to deal with features including high density ratio gas-liquid flow in complex porous media, and particle-particle and particle-hydrodynamic interactions.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.047
GPT teacher head0.312
Teacher spread0.265 · 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.

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

Citations3
Published2017
Admission routes1
Has abstractyes

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