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Record W3034937672 · doi:10.2514/6.2020-2823

Ice Crystal Environment - Modular Axial Compressor Rig: Overview of Altitude Icing Commissioning

2020· article· en· W3034937672 on OpenAlexaffabout
Jennifer L. Chalmers, Martin Neuteboom, Craig R. Davison

Bibliographic record

VenueAIAA AVIATION 2020 FORUM · 2020
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsIcingGas compressorAltitude (triangle)EngineeringEnvironmental scienceMarine engineeringProject commissioningIce crystalsAerospace engineeringMeteorologyMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

The National Research Council of Canada (NRC) has developed the Ice-Crystal Environment Modular Axial Compressor Rig (ICE-MACR) for simulating altitude ice crystal icing of aircraft engines in altitude facilities. Commissioning of the rig under altitude icing conditions was conducted in the NRC’s altitude icing wind tunnel (AIWT) in May-June 2019. An overview of the commissioning tests including some of the key results and observations are presented. Commissioning tests demonstrated the ability of the rig to create ice accretion downstream of a working compressor stage. Embedded borescope cameras allowed qualitative observation of the ice accretions as they developed. The rig instrumentation scheme enabled measurement of the particle break-up, melt-fraction and centrifuging of particles within an operating compressor.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.227
Teacher spread0.204 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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Same venueAIAA AVIATION 2020 FORUMSame topicIcing and De-icing TechnologiesFrench-language works237,207