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Record W4231601714 · doi:10.5383/ijtee.08.01.004

A Study of Spark Ignition Engine Fueled with Methanol and Ethanol Fuel Blends with Iso-Octane

2014· article· en· W4231601714 on OpenAlexvenueno aff

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsOctane ratingSpark-ignition engineGasolineThermal efficiencyMean effective pressureIgnition systemBrake specific fuel consumptionMaterials scienceAutomotive engineeringOctaneWaste managementEnvironmental scienceChemistryInternal combustion engineCombustionCompression ratioThermodynamicsEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Alternative fuels are derived from resources other than petroleum. The benefit of these fuels is that they emit less air pollutant compare to gasoline and most of them are more economically beneficial compared to oil and they are renewable. In addition, ethanol has higher evaporation heat, octane number and flammability temperature therefore it has positive influence on engine performance and reduces exhaust emissions. In this study, the effects of unleaded iso-octane, unleaded isooctane–ethanol blend (E5) and iso-octane-methanol (M5) blends on engine performance are investigated experimentally in a single cylinder fourstroke spark-ignition engine at a constant 8 Kg load. The engine speed was changed from 1100 to 1800 rpm. The results of the engine test showed that ethanol addition to unleaded iso-octane increases the value of IP, FP and IMEP with E5 fuel. The results also showed that the indicated power, brake power, friction power, indicated mean effective pressure, torque, exhaust temperature, and thermal efficiency increases with the increase in engine speed at a constant load of 8 Kg for E5, M5 and isooctane fuels. Thermal efficiency was maximum for E5 fuel (38.13%) at a speed of 1750 rpm.

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 score0.244
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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