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Record W4298137613 · doi:10.32920/16645339.v2

Low Reynolds Number Scaling Effects For Small-Scale Rotors

2022· preprint· en· W4298137613 on OpenAlexaff
Ammar Jessa

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsReynolds numberChord (peer-to-peer)MechanicsWind tunnelScalingAerodynamicsMathematicsLift coefficientPitching momentPhysicsThrustGeometryAngle of attackTurbulenceComputer scienceThermodynamics

Abstract

fetched live from OpenAlex

Three T-Motor rotors with different diameters but otherwise identical relative geometries were tested in fully edgewise flow at different advance ratios and Reynolds numbers. The objective was to verify whether the existing scaling relationships between rotor size and the aerodynamic forces are applicable to small scale rotors that operate at relatively low chord-Reynolds numbers. The rotors were mounted onto a test stand housed inside a closed loop wind-tunnel where the air speed of the tunnel was varied to achieve different advance ratios. The chord-Reynolds umber at 75% of the radius of each blade were matched for ranges from 39,000 to 117,000. The experimental data was also compared to computational results from a blade element momentum theory-based method. The results showed that the existing coefficient based scaling laws can be used to predict the performance parameters for the thrust coefficient, power coefficient, longitudinal force coefficient, side force coefficient and, rolling moment coefficient for the full range of Reynolds numbers tested. Although for the pitching moment coefficient, a coefficient approach became less applicable for chord-Reynolds number of less than 100,000.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.244
Teacher spread0.233 · 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 designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

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