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Record W4293833802 · doi:10.1016/j.jlp.2022.104861

Evolution characteristics of vented gaseous ethanol-gasoline air explosions in a small cuboid channel

2022· article· en· W4293833802 on OpenAlexaff
Chuanyu Pan, G. Ciccarelli, Jiangyue Zhao, Xiaolong Zhu, Xishi Wang

Bibliographic record

VenueJournal of Loss Prevention in the Process Industries · 2022
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsQueen's University
Fundersnot available
KeywordsCuboidGasolineEnvironmental scienceChannel (broadcasting)EthanolWaste managementChemistryEngineeringMechanical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Vented explosion experiments with commercial E10 (10 vol% ethanol - 90 vol% gasoline) were carried out in an optically accessible vented 10 L, 100 × 100 mm 2 cross-section channel. Liquid E10 was added to a channel preheated to 60 °C, and a plastic diaphragm initially covered the vent area located on the top wall near the end wall, at the opposite end of the spark plug . The measured peak overpressure and flame propagation phenomena were analyzed for different E10 liquid volumes and vent areas. As the volume of E10 added increased, both the inner and external peak pressure increased, but then decreased for larger amounts of E10 added. For a given amount of fuel, the pressure time-history was relatively smooth for the smallest vents, and highlighted by strong pressure oscillations for the largest vent openings. The peak channel pressure decreased monotonically with increased vent area because more of the fuel discharged and burned as a flame jet out the vent opening before it could combust inside the channel. The peak pressure and flame acceleration generated for gasoline only, measured for a single vent area, was only slightly lower than that produced with E10. From a process, storage and handling safety standpoint, this implies E10 can be treated similar to gasoline at 60 °C.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.266
Teacher spread0.238 · 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
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

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

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