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Record W2155005415 · doi:10.1115/1.1581894

Combustion in Gas Fueled Compression: Ignition Engines of the Dual Fuel Type

2003· article· en· W2155005415 on OpenAlexaff
G. A. Karim

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2003
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHomogeneous charge compression ignitionIgnition systemCombustionDiesel fuelAutomotive engineeringSPARK (programming language)Engine knockingCarbureted compression ignition model engineDual (grammatical number)Diesel cycleFuel injectionCompression ratioInternal combustion engineNuclear engineeringEnvironmental scienceCombustion chamberComputer scienceEngineeringChemistryAerospace engineering

Abstract

fetched live from OpenAlex

In the dual fuel engine much of the energy release comes from the combustion of the gaseous fuel while only a small amount of diesel liquid fuel provides ignition through timed cylinder injection. Such operation with optimum conversion methods has the potential to provide operational characteristics that are comparable or superior to those of the corresponding diesel or spark ignition engines. These characteristics may be realized only if sufficiently effective measures can be ensured both for the avoidance of knock, usually at high loads, and incomplete gaseous fuel utilization at relatively light loads. An objective of this contribution is to demonstrate that the main effort needed to overcome the problems associated with the operation of gas fueled dual fuel engines is via a better control of the relatively complex processes of combustion. Both experimental and analytical modeling procedures for effecting optimum improvement to the combustion process are described.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations281
Published2003
Admission routes1
Has abstractyes

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