Substantiation of the ratio of biofuel and kerosin in the mixture for its application as aviation fuel
Bibliographic record
Abstract
Today, technologies for the production of alternative fuels and for the development of engines on different operating principles are actively developing, due to both the tightening of the environmental requirements of ICAO (International Civil Aviation Organization) for harmful emissions into the atmosphere and the depletion of non-renewable resources, and the interests of the oil importing countries. Strict requirements are imposed on the quality of aviation fuels related to ensuring the reliability of aviation technology and flight safety. Requirement toughening for quality indicators will inevitably lead to higher fuel prices, so today we can observe some concessions in domestic and foreign regulatory documents to certain quality indicators of aviation fuels, for example, to indicators of low-temperature properties. It follows that the use of petroleum fuels will sooner or later become inappropriate. Technologies to produce synthetic and biological fuels from various types of raw materials make it possible to obtain fuel with close quality indicators to traditional kerosene, but it has not yet been completely replaced. Therefore, today we are considering the use of alternative fuels in a mixture with petroleum kerosene in various proportions. The question remains open: in what proportion is it possible to use mixtures of alternative fuel with kerosene on the aircraft without any negative consequences for their operation. Based on the known dependencies, a mathematical model is proposed for calculating some operational indicators of fuel, engine and aircraft depending on the proportion of mixing alternative fuel and kerosene. In accordance with the calculations, the most rational ratio of petroleum kerosene and SPK fuel is substantiated both from the point of view of the necessary operational properties and from the point of view of economic feasibility.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".