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Record W1506152485 · doi:10.4271/2007-01-4019

Thermal Efficiency Analyses of Diesel Low Temperature Combustion Cycles

2007· article· en· W1506152485 on OpenAlexafffund
Ming Zheng, Yuyu Tan, Mwila C. Mulenga, Meiping Wang

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2007
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Windsor
FundersCanada Research ChairsUniversity of Windsor
KeywordsCombustionThermalMaterials scienceTemperature measurementDiesel fuelAutomotive engineeringEnvironmental scienceNuclear engineeringThermodynamicsChemistryEngineeringPhysics

Abstract

fetched live from OpenAlex

Thermal efficiency comparisons are made between the low temperature combustion and the conventional diesel cycles on a common-rail diesel engine with a conventional diesel fuel. Empirical studies have been conducted under independently controlled exhaust gas recirculation, intake boost, and exhaust backpressure. Up to 8 fuel injection pulses per cylinder per cycle have been applied to modulate the homogeneity history of the early injection diesel low temperature combustion operations in order to improve the phasing of the combustion process. The impact of heat release phasing, duration, shaping, and splitting on the thermal efficiency has been analyzed with zero-dimensional engine cycle simulations. This paper intends to identify the major parameters that affect diesel low temperature combustion engine thermal efficiency.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.277
Teacher spread0.263 · 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 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

Citations51
Published2007
Admission routes2
Has abstractyes

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