Trade-off between soot and NO emissions during enclosed spray combustion of jet fuel
Bibliographic record
Abstract
Abstract Aviation emissions of soot and nitrogen oxides are strictly regulated as they adversely impact human health and the environment. Jet fuel combustion conditions that decrease one pollutant concentration increase the other. Although it is not impossible to achieve both low soot and NOx through clever design, it is hard to simultaneously reduce both. Although it is difficult to study such conditions due to high temperatures and gas flowrates of aircraft engines, recently it was shown that Enclosed Spray Combustion (ESC) of jet fuel results in soot with similar characteristics to that from aircrafts making ESC an attractive unit for studying aviation-like emissions. Furthermore, judicious swirl-injection of air downstream of the ESC burner drastically reduces soot emissions. Here the trade-off between NO and soot emissions during combustion of jet fuel is studied for the first time, to the best of our knowledge, accounting for the detailed structure of soot. Injecting air shortly after the ESC burner decreases soot but increases NO emissions, while such injection further downstream has the inverse outcome. This interplay between soot and NO emissions was correlated quantitatively with the gas temperature shortly after air injection. Consequently, combustion conditions for an optimal trade-off between soot and NO emissions for the ESC conditions studied here are identified that are at or below the lowest NOx emissions per unit mass of fuel from existing aircraft engines.
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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 | 0.000 |
| 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".