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Record W2021557999 · doi:10.4271/2011-38-0079

Development and Commissioning of a Linear Compressor Cascade Rig for Ice Crystal Research

2011· article· en· W2021557999 on OpenAlexafffund
D. C. Knezevici, Dan Fuleki, James MacLeod

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2011
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council Canada
FundersTransport Canada
KeywordsCascadeGas compressorProject commissioningEngineeringMechanical engineeringMaterials scienceComputer scienceEnvironmental scienceMarine engineeringPublishing

Abstract

fetched live from OpenAlex

This paper describes the commissioning of a linear compressor cascade rig for ice crystal research. The rig is located in an altitude chamber so the test section stagnation pressure, temperature and Mach number can be varied independently. The facility is open-circuit which eliminates the possibility of recirculating ice crystals reentering the test section and modifying the median mass diameter and total water content in time. As this is an innovative facility, the operating procedures and instrumentation used are discussed. Sample flow quality data are presented showing the distribution of velocity, temperature, turbulence intensity and ice water concentration in the test section. The control and repeatability of experimental parameters is also discussed. Although the rig was designed to investigate the ice crystal phenomenon in an environment representative of a compressor stator row, the test section can be reconfigured to assess the performance of aircraft air data sensors as well.

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)0.002
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.062
GPT teacher head0.299
Teacher spread0.236 · 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

Citations40
Published2011
Admission routes2
Has abstractyes

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