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Record W6910115481 · doi:10.4224/40002680

Civil Aviation Alternate Fuels Contrails and Optical Measurements Research (CAAFCOMR) – particulate emissions and contrails analysis

2020· report· en· W6910115481 on OpenAlexafffundvenueabout

Bibliographic record

VenueNPARC · 2020
Typereport
Languageen
Field
Topic
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaTransport Canada
KeywordsCruiseCivil aviationParticulatesAltitude (triangle)Ice cloudOptical depth

Abstract

fetched live from OpenAlex

Over the period August 2019 to March 2020, the project Civil Aviation Alternate Fuels Contrails Optical Measurements Research (CAAFCOMR) was undertaken by the NRC, under sponsorship of Environment & Climate Change Canada, Transport Canada, LanzaTech and the NRC. The NRC Falcon undertook flight operations on Jet A1 and unblended LTPNNL ATJ-derived SPK bio-jet fuels. High altitude cruising flight contrails at typical air transport cruise Mach Numbers of 0.75-0.84 were sought for each flight, with engines operating at maximum continuous thrust (MCT). In cruise, young contrails and emissions data was measured by the NRC CT-133 research aircraft, out to 1.2 minutes age. This was sufficient length to identify the state of contrails, persistent or sublimating, as dictated by background atmospheric conditions – both states were encountered upon CAAFCOMR flights. CT-133 measurements consisted of contrail optical thickness, ice particle size and number, particulate and gaseous emissions from the Falcon in cruise. The project was a follow-on from project CAAFCEB, for which the jet fuels were Jet A1, JP-5 and 92% LTPNNL blended with 8% petroleum-derived aromatics. The objectives of CAAFCOMR were the measurement of contrails under a contemporary limit-state (100%, no sulphur nor aromatics and maximum hydrogen content, 15.3%) of biofuel technology and to obtain contrail optical thickness measurement comparisons. All contrails were generated by the NRC Falcon, with the same engines. Individual engine emissions and ice particle formation were not measured. Rather, the wake and contrail cross-sections were measured holistically, over the full vertical and lateral extents. A number of lateral/vertical transects, typically nine, were concatenated in the line-of-flight (contrail axis) direction to form re-constructed contrails, integration of which enabled the autonomous derivation of contrail ice particle number Apparent Emission Indices (EIn). In addition and for the first time for NRC contrail flight research, CO₂ concentration was used as a species intermediary, for the derivation of contrail ice particle EIn. Thus, the cross-accuracy of the two methods were inter-compared. The mean difference between methods was approximately 20%. Three ATJ SPK//Jet A1 flights were conducted, with one sublimating contrail and two persistent contrails achieved for each fuel. The 100% ATJ SPK persistent contrails were generated at significantly greater initial growth rates than those for Jet A1, as sensed by the FSSP-100 for >0.5 μm ice particle sizes, in ice-mass EIm, overall optical depth EIo, zenithal optical obscurity, and median ice particle size, whilst ice particle number EIn, were similar, at a contrail age of approximately 0.05 minutes. Thereafter the ATJ SPK >0.5 μm contrails grew at slow temporal rates, with the ice particle size reducing as EIn increased slowly. Jet A1 contrail >0.5 μm ice particle size, EIn, EIm, EIo, and zenithal obscurity all grew at greater rates, so that by an extrapolated contrail age of three minutes, the ATJ SPK contrails were 71-96% thinner in EIn and EIo, zenithal obscurity was 20-50% lower and median ice particle size 0-30% lower. With similar uncertainty margining, overall PM emissions from ATJ SPK c.f. with Jet A1 were 72-93% lower in number. nvPM, i.e. soot, mass was 37-93% lower, soot particle number a mean 35% lower and soot mean size approximately 50% lower.

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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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.206
GPT teacher head0.405
Teacher spread0.198 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2020
Admission routes4
Has abstractyes

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