ZA novel carbon-reducing aviation fuel and mechanism for small gas turbine
Bibliographic record
Abstract
This study targets the critical carbon emission reduction requirement of industrial fixed-wing drones equipped with small gas turbines, a key issue amid the urgent demand for green aviation and energy restructuring in the drone sector, we developed low-carbon fuels via physical blending of ethanol (0–30%, E0–E30) with diesel, established experimentally validated formulas for oxygen consumption, air flow, and CO2 emissions, and tested them on a Xuanyun P160-RXi-B engine at 38,000–120,000 rpm, with notable results showing E0–E15 fuels performed stably under all conditions while E20–E30 caused high-speed vibrations, and at equivalent thrust E0–E15 reduced CO2 by 6.04–14.42% and NOₓ by 9.91–23.79% (E15 optimal), driven by ethanol’s oxygen enrichment, carbon reduction, and an 18°C exhaust temperature drop; its novelty lies in integrating theoretical calculations with empirical testing – unlike prior research, this work applies physically blended ethanol-diesel to small gas turbines, conducts comprehensive emission analysis, and provides direct empirical validation for previously inferred CO2 reduction, bridging theoretical predictions with experimental evidence to advance low-carbon fuel frameworks for industrial drones.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".