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Record W4402936998 · doi:10.1038/s41598-024-73484-8

Trade-off between soot and NO emissions during enclosed spray combustion of jet fuel

2024· article· en· W4402936998 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsInstitute of Particle Physics
FundersNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsSootCombustionCombustorEnvironmental scienceNOxJet fuelJet (fluid)Fuel injectionWaste managementChemistryAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

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 NO x 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.241
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it