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Assessment of a regulatory measurement system for the determination of the non-volatile particulate matter emissions from commercial aircraft engines

2020· article· en· W3113084141 on OpenAlexaff
John S. Kinsey, Robert Giannelli, Robert Howard, Brandon Hoffman, Richard Frazee, Michael Aldridge, Cullen Leggett, Katherine Stevens, David B. Kittelson, William M. Silvis, Jeffrey Stevens, Prem Lobo, Steven Achterberg, Jacob Swanson, Kevin A. Thomson, Timothy McArthur, Donald E. Hagen, Max B. Trueblood, Lindsay Wolff, David S. Liscinsky, Russell Arey, K. M. Cerully, Richard C. Miake‐Lye, T. B. Onasch, Andrew Freedman, W. D. Bachalo, Grégory Payne, Mikal Durlicki

Bibliographic record

VenueJournal of Aerosol Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsNational Research Council Canada
FundersFederal Aviation AdministrationU.S. Environmental Protection Agency
KeywordsParticulatesEnvironmental scienceSampling (signal processing)CombustionEngineeringAir pollutionAerospaceMeteorologyAutomotive engineeringAerospace engineeringElectrical engineeringChemistryPhysics

Abstract

fetched live from OpenAlex

The SAE International has published Aerospace Information Report (AIR) 6241 which outlined the design and operation of a standardized measurement system for measuring non-volatile particulate matter (nvPM) mass and number emissions from commercial aircraft engines. Prior to this research, evaluation of this system by various investigators revealed differences in nvPM mass emissions measurement on the order of 15–30% both within a single sampling system and between two systems operating in parallel and measuring nvPM mass emissions from the same source. To investigate this issue, the U. S. Environmental Protection Agency in collaboration with the U. S. Air Force's Arnold Engineering Development Complex initiated the VAriable Response In Aircraft nvPM Testing (VARIAnT) research program to compare nvPM measurements within and between AIR-compliant sampling systems used for measuring combustion aerosols generated both by a 5201 Mini-CAST soot generator and a J85-GE-5 turbojet engine burning multiple fuels. The VARIAnT research program has conducted four test campaigns to date. The first campaign (VARIAnT 1) compared two essentially identical commercial versions of the sampling system while the second campaign (VARIAnT 2) compared a commercial system to the custom-designed Missouri University of Science and Technology's North American Reference System (NARS) built to the same specifications. Comparisons of nvPM particle mass (i.e., black carbon), number, and size were conducted in both campaigns. Additionally, the sensitivity to variation in system operational parameters was evaluated in VARIAnT 1. Results from both campaigns revealed agreement of about 12% between the two sampling systems, irrespective of manufacturer, in all aspects except for black carbon determination. The major source of measurement differences (20–70%) was due to low BC mass measurements made by the Artium Technologies LII-300 as compared to the AVL 483 Micro-Soot Sensor, the Aerodyne Cavity Attenuated Phase Shift (CAPS PMSSA) monitor, and the thermal-optical reference method for elemental carbon (EC) determination, which was used as the BC reference.

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)0.040
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.040
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0040.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.260
Teacher spread0.235 · 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 designBench or experimental
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".

Quick stats

Citations27
Published2020
Admission routes1
Has abstractyes

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