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Record W1985555445 · doi:10.5539/jsd.v2n2p84

Operational Characteristics of Diesel Engine Run by Ester of Sunflower Oil and Compare with Diesel Fuel Operation

2009· article· en· W1985555445 on OpenAlexvenueno aff
Murugu Mohan Kumar Kandasamy, Thangavelu Mohanraj, Rajamohan Ganesan

Bibliographic record

VenueJournal of Sustainable Development · 2009
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsDiesel fuelDiesel engineSunflower oilEnvironmental scienceFuel oilWinter diesel fuelFuel efficiencyCylinderVegetable oil refiningThrust specific fuel consumptionAutomotive engineeringSunflowerHeat of combustionHeating oilPulp and paper industryWaste managementDiesel cycleBiodieselInternal combustion engineCombustionChemistryEngineeringCompression ratioAgronomyFood scienceBiologyMechanical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

A single cylinder direct injection diesel engine was first run with diesel fuel and then with Ester of sunflower oil. The operational characteristics of the engine run with both the fuel have been compared and the results obtained are shown in this paper. From the results obtained, it is understood that the thermal efficiency is slightly less and the specific fuel consumption is slightly higher with Ester of sunflower oil when compared with Diesel. This is due to the lower calorific value of the Ester of sunflower oil. All other parameters are also clearly discussed in this paper and finally it is concluded that the Ester of sunflower oil can be used as alternative fuel in the Diesel engine without any engine modifications.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.001

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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

Explore more

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