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Record W2081707086 · doi:10.5539/mas.v5n4p53

A Performance, Emission and Combustion Investigation on Hot Air Assisted Eucalyptus Oil Direct Injected Compression Ignition Engine

2011· article· en· W2081707086 on OpenAlexvenueno aff
D. Tamilvendhan, V. Ilangovan

Bibliographic record

VenueModern Applied Science · 2011
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsEucalyptus oilDiesel fuelCetane numberEnvironmental scienceEucalyptusThermal efficiencyDiesel engineNaturally aspirated engineIgnition systemNOxWaste managementCombustionPulp and paper industryAutomotive engineeringBiodieselExhaust gas recirculationInternal combustion engineChemistryEngineeringMedicineBotany

Abstract

fetched live from OpenAlex

A diesel engine modified for eucalyptus oil direct injection (EuDI) has been tested to study eucalyptus oil behavior. Since the eucalyptus oil possesses low cetane number fails to auto ignite, the test engine was modified to supply hot air during suction stroke which helps to auto-ignite the injected eucalyptus oil. The engine with this facility was operated using eucalyptus oil under various load conditions and at various intake temperatures. The results of the investigation were proved that eucalyptus oil could be direct injectable in a regular diesel engine after little engine modification. This method showed almost same brake thermal efficiency (BTE) at full load compared to standard diesel operation. Except NOx emission other emissions were found closer to diesel baseline operation. This mode offered almost 50% smoke free operation at all loads compared to standard diesel operation. Also this method successfully proved the complete replacement of diesel fuel by eucalyptus oil.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.043
GPT teacher head0.209
Teacher spread0.166 · 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

Citations26
Published2011
Admission routes1
Has abstractyes

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