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Record W3134368795 · doi:10.19206/ce-2019-322

Evaluation of the suitability of synthetic polymer fuels in self-ignition engines

2019· article· en· W3134368795 on OpenAlexfundno aff
O. Klyus, Paweł Krause, В. А. Марков, A. Skarbek-Żabkin, Bowen Sa

Bibliographic record

VenueSilniki Spalinowe/Combustion Engines · 2019
Typearticle
Languageen
FieldEngineering
TopicEngine and Fuel Emissions
Canadian institutionsnot available
FundersIndependent Electricity System Operator
KeywordsIgnition systemSauter mean diameterDiesel fuelViscositySurface tensionSynthetic fuelProcess engineeringMaterials scienceEnvironmental sciencePetroleumNuclear engineeringVolume (thermodynamics)Mechanical engineeringWaste managementThermodynamicsComposite materialEngineeringChemistryChemical reactionOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

The article presents a method for determining the quality of spraying a mixture of oil and synthetic fuels obtained from the pro-cessing of polymer materials. Laboratory tests of physical parameters of such a mixture were carried out, which made it possible to determine the limit values for the volume fraction of synthetic fuels. The method of determining the suitability of this type of fuel takes into account the criterion numbers Re and Oh, which include physical parameters such as viscosity, density, and surface tension. The experimental part concerning the distribution of droplets of injected fuel and determination of Sauter Mean Diameter using laser diffrac-tion confirmed the usefulness of the developed method for the assessment of the possibility of using a mixture of petroleum-based and synthetic fuels in self-ignition engines.

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)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.236
Teacher spread0.221 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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