Priming dose in distributed injection systems of overheated alcohol fuel
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".