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Simulation Study of Bionic Jetting Direction Influence on Drag Reduction Effect

2010· article· en· W1544613550 on OpenAlexvenueno aff
Gang Zhao, Hua-lin Zhao, Hai-sheng Shu, Dan Zhao, Yun-qing Gu, Dong-lai Xia

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsDragParasitic dragMechanicsReduction (mathematics)Flow (mathematics)Computer simulationDrag coefficientMaterials scienceEngineeringPhysicsGeometryMathematics

Abstract

fetched live from OpenAlex

By imitating the jetting flow feature of shark gills,a bionic jetting model is established by arranging round outlets on a flat plate. The drag reduction effect under different jetting directions are explored using numerical simulation method when the main flow field velocity was 20m/s and the reasons of drag reduction are analyzed simultaneously. The results of simulation showed that:different jetting directions affecte the wall shear stress and static pressure of flow field which result in the changing of viscous resistance and pressure drag; the bionic jetting has the best drag reduction effect when jetting direction angle is 30 degrees; under different jetting direction conditions, the main reason of drag reduction is the substantially decreased pressure drag. Key words:bionics; drag reduction; jetting flow; numerical simulation; viscous resistance; pressure drag

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)0.002
metaresearch head score (Gemma)0.001
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 score0.100
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.001
Scholarly communication0.0000.006
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 head0.287
Teacher spread0.284 · 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 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

Citations8
Published2010
Admission routes1
Has abstractyes

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