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Record W3035401587 · doi:10.2514/6.2020-2763

Effect of Leading-Edge Tubercles on Rotor Blades

2020· article· en· W3035401587 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 AVIATION 2020 FORUM · 2020
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsStall (fluid mechanics)Mach numberLeading edgeDragAirfoilTransonicPhysicsTrailing edgeAerodynamicsMechanics

Abstract

fetched live from OpenAlex

Current rotor blade designs are limited by retreating blade stall, influenced by local airfoil stall performance in root regions, as well as compressible flow effects and drag divergence of the advancing blade in the tip regions. The application of leading edge tubercles to lifting surfaces have shown to improve post-stall performance in the subsonic regime, and to delay shock-wave formation and improve drag divergence Mach numbers in the transonic regime. This paper explores the effects of leading edge tubercles applied to a canonical rotor. Various tubercle configurations were analyzed using computational fluid dynamic simulations using Euler and RANS equations. Improvements in Figure of Merit of 9.5% were found over the baseline rotor for specific tubercle configurations when operating at a pitch angle of 2 degrees and tip Mach number of 0.794. An increase in thrust coefficients and reduction in power coefficients over the baseline rotor were both attributed to alteration of flow behaviour in different regions of the rotor due to the presence of leading edge tubercles.

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 score0.119
Threshold uncertainty score0.489

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 head0.004
GPT teacher head0.211
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