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Record W2109898176 · doi:10.2514/1.j053716

Stability of Boundary Layers over Porous Walls with Suction

2015· article· en· W2109898176 on OpenAlexaff
Nils Tilton, Luca Cortelezzi

Bibliographic record

VenueAIAA Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsMcGill University
Fundersnot available
KeywordsMechanicsBoundary layerSuctionIsotropyMaterials scienceBoundary layer suctionLinear stabilityPorosityFreestreamClassical mechanicsBoundary layer thicknessBoundary layer controlInstabilityPhysicsReynolds numberThermodynamicsComposite materialTurbulenceOptics

Abstract

fetched live from OpenAlex

Although there is considerable interest in using wall suction to increase boundary-layer stability, stability analyses suggest that porous walls are inherently destabilizing. We explore this contradiction by performing a spatial linear stability analysis of the asymptotic suction boundary layer using a realistic model of wall suction. The porous wall is modelled as a layer of rigid, homogeneous, isotropic, porous material of small permeability, in which inertial effects may be neglected. The porous layer is bounded above by a semi-infinite region in which a boundary layer is driven by a constant freestream velocity. The wall suction is created by applying a suction pressure to a semi-infinite region below the porous layer. Our stability analysis takes account of the full coupling between the flowfields in the boundary-layer and suction regions, governed by the Navier–Stokes equations, and the flow in the porous layer, governed by the volume-averaged Navier–Stokes equations. We find that small amounts of wall permeability destabilize the Tollmien–Schlichting wave and cause a substantial broadening of the unstable region. As a result, the stabilization of boundary layers by wall suction is substantially less effective and more expensive than what is predicted by classical boundary-layer theory.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.289

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 head0.019
GPT teacher head0.220
Teacher spread0.202 · 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 designObservational
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

Citations21
Published2015
Admission routes1
Has abstractyes

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