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Record W2072554361 · doi:10.5539/mas.v9n5p269

Cavitation Treatment of High-Viscosity Marine Fuels for Medium-Speed Diesel Engines

2015· article· en· W2072554361 on OpenAlexvenueno aff
Сергій Вікторович Сагін, Valerii Grigorovich Solodovnikov

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCavitationDiesel fuelPiston (optics)CylinderDiesel enginePiston ringEnvironmental scienceFuel injectionFuel oilViscosityAutomotive engineeringBushingMaterials sciencePetroleum engineeringMechanical engineeringWaste managementEngineeringMechanicsChemistryComposite materialPhysicsRing (chemistry)

Abstract

fetched live from OpenAlex

The paper considers processes of treatment for fuels with high sulphur content, when applied in marine medium-speed diesel engines. The paper describes features of operating marine medium-speed diesel engine fuel systems with high-viscosity fuels. The paper offers an option for the cavitation fuel treatment to disrupt the sulphur-carbon bonds in the marine fuels. The authors developed a scheme of the experimental installation, which allows performing cavitation fuel treatment. The results of the analysis are provided in the paper, as well as the fuel cavitation treatment effect on sulphur wear of diesel engine cylinder and piston assembly parts (bushing and top piston ring) is analysed, as well as its operational parameters (maximum cylinder pressure, gases temperature in the exhaust manifold).

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 score0.001
Threshold uncertainty score0.003

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 head0.047
GPT teacher head0.274
Teacher spread0.226 · 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

Citations36
Published2015
Admission routes1
Has abstractyes

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