Bibliographic record
Abstract
Abstract. Soot from aircraft engines deteriorates air quality around airports and can contribute to climate change primarily by influencing cloud processes and contrail formation. Simultaneously, aircraft engines emit CO2, nitrogen oxides (NOx) and other pollutants which also negatively affect human health and the environment. While urgent action is needed to reduce all pollutants, strategies to reduce one pollutant may increase another, calling for a need to decrease, for example, the uncertainty associated with soot’s contribution to net Radiative Forcing (RF) in order to design targeted policies that minimize the formation and release of all pollutants. Aircraft soot is characterized by rather small median mobility diameters, dm = 8 – 60 nm, and at high thrust, low (< 25 %) organic carbon to total carbon (OC/TC) ratios while at low thrust the OC/TC can be quite high. Computational models could aid in the design of new aircraft combustors to reduce emissions, but current models struggle to capture the soot dm, and volume fraction, fv measured experimentally. This may be in part due to oversimplification of soot’s irregular morphology in models and a still poor understanding of soot inception. Nonetheless, combustor design can significantly reduce soot emissions through extensive oxidation or near-premixed, lean combustion. For example, lean premixed prevaporized combustors significantly reduce emissions at high thrust by allowing injected fuel to fully vaporize before ignition while low temperatures from very lean jet fuel combustion limit the formation of NOx. Alternative fuels can be used alongside improved combustor technologies to reduce soot emissions. However, current policies and low supply promote the blending of alternative fuels at low ratios (~1 %) for all flights, rather than using high ratios (> 30 %) in a few flights which could meaningfully reduce soot emissions. Here, existing technologies for reducing such emissions through combustor and fuel design will be reviewed to identify strategies that eliminate them.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.030 | 0.014 |
| Insufficient payload (model declined to judge) | 0.319 | 0.312 |
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".