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Record W4206561090 · doi:10.2514/6.2022-1966

Experimental Characterization of Leading Edge Tubercles on Rotor Blades

2022· article· en· W4206561090 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

VenueAIAA SCITECH 2022 Forum · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsThrustRotor (electric)Noise reductionWakeAcousticsReduction (mathematics)Blade (archaeology)Structural engineeringNoise (video)Sound powerPhysicsEngineeringMechanicsGeometryComputer scienceMathematicsAerospace engineeringMechanical engineeringSound (geography)

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2022-1966.vid This paper presents the first experimental measurements of leading edge tubercles applied to rotor blades. Using the Royal Military College Rotor Rig, the effect of applying uniform SinA03L12.5 and SinA06L25 tubercles was characterized by comparing thrust, power, and acoustic measurements to a baseline NACA 0014 blade. Tubercles showed an improvement in overall sound pressure levels at low to moderate pitch angles attributed to a reduction in blade wake interaction noise. Such improvement appears to come with a reduction in peak Figure of Merit at higher thrust coefficient with larger loss in performance for the SinA06L25 shape than the SinA03L12.5. Non-linear deflections induced via elasticity of the blade affect the performance interpretation of the obtained results and further experimentation will be required. Results indicate that improvements in performance and reduction in noise is possible if proper selection of tubercles shape is done at specific span-wise locations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.931

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)0.0010.000

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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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