MétaCan
Menu
Back to cohort
Record W1983247380 · doi:10.1016/j.egypro.2012.09.012

Numerical Investigation of Safety Aspects of Hydrogen/Carbon Monoxide Mixture Release

2012· article· en· W1983247380 on OpenAlexafffund
Boris Chernyavsky, Pierre Bénard, Jake DeVaal, Benjamin Angers

Bibliographic record

VenueEnergy Procedia · 2012
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsBallard Power Systems (Canada)Université du Québec à Trois-Rivières
FundersBallard Power Systems
KeywordsCarbon monoxideHydrogenChemistryChemical engineeringMaterials scienceEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Research into the performance of proton exchange membrane fuel cell (PEMFC) and its degradation (“poisoning”) by the presence of carbon monoxide, a common byproduct of most common hydrogen production methods, requires storage of a large quantities of hydrogen/carbon monoxide mixture in high pressure tanks. The possibility of unintended release of the gas calls for a safety study of H2/CO mixture leaks, as well as potential higher rate releases. This presentation covers the safety aspects of the numerical study of a H2/CO mixture release at a wide range of release parameters, including release velocity, orientation, initial diameter, and initial gases fraction ratio. The study provides a simulation of a) the extent of flammable concentrations of H2, corrected for the presence of CO; b) the extent of CO concentration exceeding OSHA recommended health safety limits. The presence of CO in the mixture required reassessment of hydrogen flammability limits, although the correction proved to be relatively small for the CO fractions used in the present. The maximum extents and evolution of H2 and CO envelops had been modeled and compared. An important conclusion is the possibility of using hydrogen detectors to predict CO concentration levels with accuracy sufficient for practical purposes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.411

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 head0.190
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueEnergy ProcediaSame topicCombustion and Detonation ProcessesFrench-language works237,207