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Priming dose in distributed injection systems of overheated alcohol fuel

2019· article· en· W2969705646 on OpenAlexaboutno aff
А В Егоров, A V Lysyannikov, Yu F Kaizer, В Г Шрам, N N Lysyannikova, Alexander Kuznetsov, V L Tyukanov, O A Kaizer

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsGasolineAlcoholIsopropyl alcoholCombustionFuel injectionAlcohol fuelEthanolEnvironmental scienceWaste managementAutomotive engineeringChemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The expansion of the use of alcohol-containing fuels is evidenced by the release to the market of vehicles operating on benzoethanol mixtures E85 and E90, as well as on pure fuel ethanol E100. For regions with a temperate and cold climate (Russia, USA, Canada, Western Europe), the lack of fuel systems, which make it possible to launch and operate engines on pure alcohols (n-propyl, isopropyl, n-butyl, sec-butyl, iso-butyl, tert-butyl), restrains, among other reasons, the widespread use of alcohols as motor fuels. According to numerous studies, an engine operating on pure alcohols can be started at temperatures above 10 ° C, at lower temperatures it becomes impossible to start and for its implementation at least 10-15% of the priming dose of gasoline is introduced into alcohols. The priming dose of gasoline in gas-alcohol mixtures can be replaced by a priming dose of alcohol injected into the intake manifold in a superheated state. This technical solution is proposed in patents for inventions of the Russian Federation of the authors. This article is devoted to the scientific and technical rationale for the efficiency of the use of pure superheated alcohols in injection systems and their impact on the effective performance of internal combustion engines with forced ignition.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.427

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 communication0.0000.001
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 head0.011
GPT teacher head0.206
Teacher spread0.195 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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