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Record W4387975000 · doi:10.5194/ar-2023-15

Opinion: Eliminating aircraft soot emissions

2023· preprint· en· W4387975000 on OpenAlex
Una Trivanovic, Sotiris E. Pratsinis

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaEidgenössische Technische Hochschule ZürichSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsSootNOxCombustionPollutantEnvironmental scienceCombustorParticulatesChemistry

Abstract

fetched live from OpenAlex

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.647
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.049
GPT teacher head0.301
Teacher spread0.251 · 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