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Analysis of the Aerodynamic Character of Bionic Wingspan on the Basis of Frigate Wing Structure

2010· article· en· W1890087076 on OpenAlexvenueno aff
Yi Ma, Yurong Liu, Jingfu Jin, Qian Cong

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsWingspanWingAerodynamicsAirfoilEngineeringAirflowLeading edgeDragStructural engineeringAerospace engineeringMechanical engineering

Abstract

fetched live from OpenAlex

This paper extracts the frigate bird’s leading edge contour by CATIA on the basis of the obverse and side pictures when frigate bird glides. Then three bionic wingspans of bending update, forward and integrating the two are built combining NACA 4412 airfoil. Compared with simple wingspan, the drag force of testing wingspan can be suppressed significantly, because the wingspan bending forward can form airflow of S style which declines the slope of the path-line of airflow and reduces the leading edge’s pressures. As a result the working condition is improved because the bending forward wingspan reduces air’s velocity. The lift efficiency of bending update wingspan may be improved because of increasing the leading edge’s pressure resulting in high speed airflow. Key words: leading edge contour; bionic wingspans; NACA 4412; aerodynamic performance

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 categoriesScience and technology studies
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 score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.010
Science and technology studies0.0000.003
Scholarly communication0.0000.002
Open science0.0020.000
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 head0.004
GPT teacher head0.243
Teacher spread0.239 · 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.

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

Citations4
Published2010
Admission routes1
Has abstractyes

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