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Record W4206962047 · doi:10.5194/amt-2021-320-rc1

Comment on amt-2021-320

2022· peer-review· en· W4206962047 on OpenAlexafffund

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsNational Research Council Canada
FundersU.S. Air ForceTransport CanadaU.S. Environmental Protection AgencyFederal Aviation AdministrationAeronautics Research Mission DirectorateNational Aeronautics and Space Administration
KeywordsSpan (engineering)Jet fuelCombustionAnalytical Chemistry (journal)ParticulatesChemistryMaterials scienceEnvironmental scienceMeteorologyPhysicsEnvironmental chemistryOrganic chemistryEngineeringStructural engineering

Abstract

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Abstract. Sustainable aviation fuels (SAFs) have different compositions compared to conventional petroleum jet fuels, particularly in terms of fuel sulfur and hydrocarbon content. These differences may change the amount and physicochemical properties of volatile and non-volatile particulate matter (nvPM) emitted by aircraft engines. In this study, we evaluate whether comparable nvPM measurement techniques respond similarly to nvPM produced by three blends of SAFs compared to three conventional fuels. Multiple SAF blends and conventional (Jet A-1) jet fuels were combusted in a V2527-A5 engine, while an additional conventional fuel (JP-8) was combusted in a CFM56-2C1 engine. We evaluated nvPM mass concentration measured by three real-time measurement techniques: photoacoustic spectroscopy, laser-induced incandescence, and the extinction-minus-scattering technique. Various commercial instruments were tested, including three laser-induced incandescence (LII) 300s, one photoacoustic extinctiometer (PAX), one micro soot sensor (MSS+), and two cavity-attenuated phase shift PMSSA (CAPS PMSSA) instruments. Mass-based emission indices (EIm) reported by these techniques were similar, falling within 30 % of their geometric mean for EIm above 100 mg per kg fuel (approximately 10 µg PM m−3 at the instrument); this geometric mean was therefore used as a reference value. Additionally, two integrative measurement techniques were evaluated: filter photometry and particle size distribution (PSD) integration. The commercial instruments used were one tricolor absorption photometer (TAP), one particle soot absorption photometer (PSAP), and two scanning mobility particle sizers (SMPSs). The TAP and PSAP were operated at 5 % and 10 % of their nominal flow rates, respectively, to extend the life of their filters. These techniques are used in specific applications, such as on board research aircraft to determine particulate matter (PM) emissions at cruise. EIm reported by the alternative techniques fell within approximately 50 % of the mean aerosol-phase EIm. In addition, we measured PM-number-based emission indices using PSDs and condensation particle counters (CPCs). The commercial instruments used included TSI SMPSs, a Cambustion differential mobility spectrometer (DMS500), and an AVL particle counter (APC), and the data also fell within approximately 50 % of their geometric mean. The number-based emission indices were highly sensitive to the accuracy of the sampling-line penetration functions applied as corrections. In contrast, the EIm data were less sensitive to those corrections since a smaller volume fraction fell within the size range where corrections were substantial. A separate, dedicated experiment also showed that the operating laser fluence used in the LII 300 laser-induced incandescence instrument for aircraft-engine nvPM measurement is adequate for a range of SAF blends investigated in this study. Overall, we conclude that all tested instruments are suitable for the measurement of nvPM emissions from the combustion of SAF blends in 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.220
Threshold uncertainty score0.735

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0260.014
Insufficient payload (model declined to judge)0.2200.192

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 head0.029
GPT teacher head0.293
Teacher spread0.265 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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