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Record W2944435764 · doi:10.4271/2019-01-1943

Icing Test and Measurement Capabilities of the NRC’s Gas Turbine Laboratory

2019· article· en· W2944435764 on OpenAlexaffabout
Jennifer L. Chalmers, Craig R. Davison, James MacLeod, Martin Neuteboom, Dan Fuleki

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2019
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsIcingGas turbinesEnvironmental scienceNuclear engineeringAerospace engineeringComputer scienceEngineeringAutomotive engineeringMechanical engineeringMeteorologyPhysics

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">The National Research Council’s Gas Turbine Laboratory provides industry leading icing facilities that allow manufacturers to develop, validate and certify new products for flight in adverse conditions. This paper shows how NRC measurement techniques are used across the facilities, and presents a literature-review of recently developed capabilities. The overview includes new details on some facilities, and future capabilities that are in development or planned for the near future.</div><div class="htmlview paragraph">Methods developed at the NRC for characterizing inclement conditions are discussed and include the Isokinetic Probe, Particle Shadow Velocimetry, the Particle Detection Probe, and a size-binned real-time thermodynamic evaporation model. These are used to deliver accurate icing water content in facilities like the sea-level ice crystal generator, the Rotating Icing Rig, the Research Altitude Testing Facility, and liquid water test facilities in Ottawa and in Thompson, Manitoba at the Global Aerospace Centre for Icing and Environmental Research (GLACIER).</div></div>

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 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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 teacher head, not a consensus.

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

Citations7
Published2019
Admission routes2
Has abstractyes

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