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Record W2552521134 · doi:10.18331/brj2016.3.4.6

Growth and characterization of deposits in the combustion chamber of a diesel engine fueled with B50 and Indonesian biodiesel fuel (IBF)

2016· article· en· W2552521134 on OpenAlexvenueno aff
Muchammad Taufiq Suryantoro, Bambang Sugiarto, Fariz Mulyadi

Bibliographic record

VenueBiofuel Research Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsBiodieselDiesel fuelPiston (optics)Diesel engineWinter diesel fuelEnvironmental scienceCommon railPiston ringCombustionMaterials scienceCombustion chamberContext (archaeology)Automotive engineeringEngineeringChemistryDiesel cycleGeologyOrganic chemistryRing (chemistry)Physics

Abstract

fetched live from OpenAlex

Although used since 1893, biodiesel still faces problems that must be overcome before it can fully replace petroleum diesel. Existing literature shows that continuous use of biodiesel could lead to higher growth of deposits on critical engine components, contributing to lots of problems that could ultimately decrease engine performance. In this context, endurance tests were performed to compare the impacts of B50 and Indonesian biodiesel fuel (IBF: diesel fuel containing 10% palm oil biodiesel) on engine durability. More specifically, deposits growth as well as deposits structure and composition in response to the application of the above-mentioned fuel blends were investigated over 200 h. The results revealed that B50 produced relatively larger amounts of deposits especially on the valves and injector tip while also increased the risk of ring sticking. In addition, the structure and the elemental composition of the deposits formed on engine important components, i.e., injector tips, piston crown, intake/exhaust valves, cylinder head, and piston grooves when B50 was used were quite different compared with the IBF. Overall, more deposits formation was observed by increasing biodiesel inclusion rate while deposits tended to be wet and brittle as well.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.028
GPT teacher head0.264
Teacher spread0.235 · 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 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

Citations31
Published2016
Admission routes1
Has abstractyes

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