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Record W2017635379 · doi:10.1115/1.2266267

The Operational Mixture Limits in Engines Fueled With Alternative Gaseous Fuels

2006· article· en· W2017635379 on OpenAlexaff
Sudhir Shrestha, Ghazi A. Karim

Bibliographic record

VenueJournal of Energy Resources Technology · 2006
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIgnition systemSPARK (programming language)Range (aeronautics)MethaneHomogeneous charge compression ignitionNatural gasPropaneAutomotive engineeringNuclear engineeringEnvironmental scienceSpark-ignition engineFuel gasProcess engineeringComputer scienceWaste managementEngineeringCombustionThermodynamicsCombustion chamberChemistryAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

The operation of engines whether spark ignition or compression ignition on a wide range of alternative gaseous fuels when using lean mixtures can offer in principle distinct advantages. These include better economy, reduced emissions, and improved engine operational life. However, there are distinct operational mixture limits below which acceptable steady engine performance cannot be sustained. These mixture limits are usually described as the “lean operational limits,” or loosely as the ignition limits which are a function of various operational and design parameters for the engine and fuel used. Relatively simple approximate procedures are described for predicting the operational mixture limits for both spark ignition and dual fuel compression ignition engines when using a range of common gaseous fuels such as natural gas/methane, propane, hydrogen, and some of their mixtures. It is shown that good agreement between predicted and corresponding experimental values can be obtained for a range of operating conditions for both types of engines.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.004
GPT teacher head0.203
Teacher spread0.199 · 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

Citations13
Published2006
Admission routes1
Has abstractyes

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