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Record W2621707500 · doi:10.4050/f-0071-2015-10133

Rotor Performance of a Full-Scale Heated Tail Rotor

2015· article· en· W2621707500 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid dynamics and aerodynamics studies
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsRotor (electric)Scale (ratio)Computer scienceAerospace engineeringPhysicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

A CFD approach using the NASA OVERFLOW code to predict the impact of ice accretion on a Bell 206 tail rotor is evaluated. The basis for the computational study is a test, conducted at the NASA Glenn Icing Research Wind Tunnel. The test acquired thrust and power measurements before, during, and after various ice accretion and blade heating cycles and also acquired ice shape geometric data from three-dimensional laser scans. For the CFD study, the measured ice shapes were utilized to develop computational grids and generate predicted results for thrust and power at various stages of ice accretion. It is shown that the computed results for the degraded performance are significantly different than the measurements. The lack of surface roughness modeling in the calculation is suspected to be a key contributor in the discrepancy.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.185
Teacher spread0.176 · 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

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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