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Record W2899171264 · doi:10.2514/1.j056836

Boundary-Layer Transition Prediction over Cavities and Its Morphing Skin Design Application

2018· article· en· W2899171264 on OpenAlexaff
Fadi Mishriky, Paul Walsh

Bibliographic record

VenueAIAA Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLaminar flowBoundary layerMechanicsBoundary layer thicknessReynolds numberBlasius boundary layerMorphingWakeExternal flowBoundary layer controlFlow separationFlow (mathematics)PhysicsMaterials scienceClassical mechanicsTurbulenceComputer science

Abstract

fetched live from OpenAlex

Flow over cavities can behave in one of three different modes, namely, the wake mode, the shear-layer mode, and the no-oscillations mode. In this study, the unsteady Reynolds-averaged Navier–Stokes equations ae used to numerically solve the flow over two-dimensional deep cavities with upstream laminar boundary layers. The numerical model successfully captures the three flow modes, and the results are validated against experimental data and semiempirical solutions. It is observed that, in some cases, the boundary layer maintains its laminar state while travelling over the cavity and, in other cases, the boundary layer experiences a transition over the cavity vicinity. To investigate the cavity parameters that influence the transition of the boundary layer, a parametric study is performed over a wide range of flow conditions and cavity dimensions. It is found that the boundary layer bypasses the cavity and maintains its laminar state when , where is the cavity length, is the momentum thickness of the boundary layer, and is the Reynolds momentum thickness at the cavity leading edge. Some aerodynamic applications of this finding are presented, with an emphasis on morphing wings and morphing skins design.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.729
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.213
Teacher spread0.204 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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