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Record W4380589273 · doi:10.4271/2023-01-1426

Development of a New Ice Crystal Icing Shear Adhesion Rotary Test Rig and Method

2023· article· en· W4380589273 on OpenAlexaff
Dan Fuleki, Philip Hagerman

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2023
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsIcingAdhesionAccretion (finance)Ice crystalsMaterials scienceEnvironmental scienceComposite materialMeteorologyPhysics

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">This paper discusses the development and implementation of a new ice crystal icing (ICI) ice adhesion test system and technique. It is based on the state of the art rotary shear adhesion test rig developed at the NRC altitude icing wind tunnel (AIWT) used for supercooled liquid water (SLW) ice accretion adhesion testing. This rig was modified to use strain gauged arms for shed force measurements and implemented into the NRC ICI cascade rig at its research altitude test facility (RATFac). This permitted the exposure to a wide range of ICI conditions both in supercooled and wetbulb below freezing ICI conditions. The result is a standalone system that can be run remotely thus improving the testing efficiency by avoiding the need for accretion calibration points previously required to correlate accreted mass to icing exposure time. This technique was shown to be very repeatable with adhesion results for repeat points generally being within ±4 kPa and being highly sensitive in being able to measure shear adhesion strengths in the range of 8 to 200 kPa, with adhesion strengths down to 2 kPa measured but with increased noise levels. ICI accretion adhesion shear strengths are lower than typical SLW adhesion strengths and this new method proved to be effective in covering the full range.</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)0.001
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 score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.019
GPT teacher head0.260
Teacher spread0.241 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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