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The Study of Owl's Silent Flight and Noise Reduction on Fan Vane with Bionic Structure

2010· article· en· W1872877764 on OpenAlexvenueno aff
Guiqiang Liang, Jingchun Wang, Yu Chen, Changhai Zhou, Jie Liang, Luquan Ren

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsBionicsNoise (video)Noise reductionSawtooth waveWind tunnelEngineeringBoundary layerVortexReduction (mathematics)Mechanical fanAcousticsStructural engineeringMechanical engineeringComputer scienceMechanicsAerospace engineeringPhysicsGeometryMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

In light of the bionics engineering point of view, we use the biological non-smooth surface in the surface design of the fan blade in order to reduce the fan noise and improve air flow and efficiency .We design several kinds of Sawtooth-shaped distribution, and apply the non-smooth form on the fan model. We analyzed the acoustic performance of various non-smooth fan blades that was reflected when the fan was rotating. Through the wind tunnel test, we obtained the spectrum map, wind comparison chart, efficiency curve and other important parameters that came from the comparison between smooth and non-smooth models and the noise of the fan. This paper will reveal that the non-smooth shape practically is good for preventing formation of off-body vortex, which is caused by turbulent boundary layer on the vane surface, and it will have reference significance for exploring the mechanism of noise reduction on fan vane. Key words: Owl; Bionic; Noise Reduction; Optimization Test

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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.002
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.250
Teacher spread0.246 · 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 teacher head, 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

Citations13
Published2010
Admission routes1
Has abstractyes

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