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Record W4380589217 · doi:10.4271/2023-01-1439

Design, Characterization and Initial Testing of a Vertical Stabilizer Common Research Model for Aircraft Ground Icing Testing

2023· article· en· W4380589217 on OpenAlexaffabout
Catherine Clark, Marco Ruggi

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
KeywordsIcingWind tunnelEnvironmental scienceAerodynamicsSnowAirflowMarine engineeringMeteorologyEngineeringAerospace engineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Under contract to Transport Canada (TC) and with joint funding support from the Federal Aviation Administration (FAA), a vertical stabilizer common research model (VS-CRM) has been designed and built by the National Research Council of Canada (NRC). This model is a realistic, scaled representation of modern vertical stabilizer designs without being specific to a particular aircraft. The model was installed and tested in the NRC 3 m × 6 m Icing Wind Tunnel in late 2021/early 2022. Testing was led by APS Aviation Inc., with support from NRC and NASA, in order to observe the anti-icing fluids flow-off behavior with and without freezing or frozen precipitation during simulated take-off velocity profiles. The model dry-air aerodynamic properties were characterized using flow visualization tufts and boundary layer rakes. Using this data, a target baseline configuration was selected with a yaw angle equal to 0° and rudder deflection angle equal to -10°. Testing with fluids and precipitation showed that some amount of fluid and contamination was present at the end of each run, with the amount dependent on the fluid type, contamination type, air temperature, run profile and model configuration. The tests with snow and freezing rain showed that exposure of the model to the full holdover time resulted in adhered contamination to the model surface, as the fluid flowed down and off the model due to the near-vertical orientation of the model surface. Other test profiles included dynamic motion of the model to simulate one-engine inoperative conditions, as well as non-standard applications to better understand the fluid behaviour over the model surface. The analysis also includes fluid thickness measurements after fluid application, after precipitation application, and at the end of a simulated take-off run. The results from this research campaign have been presented to the SAE G12 Aerodynamics Working Group, and further testing with the VS-CRM was completed in early 2023.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.108
GPT teacher head0.324
Teacher spread0.216 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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