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Record W3123495701 · doi:10.1088/1748-3190/abdf31

On the influence of biomimetic shark skin in dynamic flow separation

2021· article· en· W3123495701 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

VenueBioinspiration & Biomimetics · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsQueen's University
Fundersnot available
KeywordsParticle image velocimetryBoundary layerFlow separationMechanicsAccelerationPressure gradientMaterials scienceFlow (mathematics)FOIL methodWakeParasitic dragBoundary layer thicknessClassical mechanicsComposite materialPhysics

Abstract

fetched live from OpenAlex

The effect of shark skin on the boundary-layer separation process under dynamic conditions (maneuvers) has been studied experimentally. We use a foil covered with biomimetic shark skin to explore how this type of surface impacts boundary-layer dynamics in both steady and accelerating conditions. The effect of denticles is assessed via particle image velocimetry in the wake. It is shown that dynamic conditions and small-scale disturbances can mitigate boundary-layer separation through instantaneous modification of the local pressure-gradient distribution. For instance, the region of favourable pressure gradient can be extended by accelerating the foil. The acceleration results in a thinner separated shear layer on the foil surface when compared to the steady reference case. This remarkable difference indicates that local roughness (introduced through for instance biomimetic shark skin) may trigger an interaction with relatively large-scale structures in the boundary layer for effective boundary-layer control during unsteady propulsion and maneuvering.

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 score0.164
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.224
Teacher spread0.217 · 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