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Record W2030206185 · doi:10.1063/1.1361092

The continuous spectrum for a boundary layer in a streamwise pressure gradient

2001· article· en· W2030206185 on OpenAlexaff
S. A. Maslowe, Raymond J. Spiteri

Bibliographic record

VenuePhysics of Fluids · 2001
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsAcadia UniversityMcGill University
Fundersnot available
KeywordsPressure gradientPhysicsAdverse pressure gradientBoundary layerBlasius boundary layerEigenfunctionMechanicsTurbulenceReynolds numberWavenumberContinuous spectrumBoundary (topology)Flow separationClassical mechanicsLeading edgeOpticsMathematical analysisEigenvalues and eigenvectors

Abstract

fetched live from OpenAlex

Solutions of the Orr–Sommerfeld equation belonging to the continuous spectrum are presented for boundary layers developing in the presence of a streamwise pressure gradient. Although the continuous spectrum has received considerable attention in the Blasius case, in most engineering applications transition to turbulence occurs in a region where there is a pressure gradient. This investigation, so far as we know, is the first to examine what effect this has on the eigenfunctions. Our results show that when there is a pressure gradient the magnitude of the eigenfunctions near the edge of the boundary layer can be much larger than it is for Blasius flow. This is particularly true when the pressure gradient is adverse, but such is the case even when it is favorable. We also investigate the effect of Reynolds number and frequency on the penetration depth; the latter term refers to one of the properties of these modes that distinguishes them from Tollmien–Schlichting waves, namely, that their magnitude is largest near the edge of the boundary layer, but much smaller inside.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.212
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 source (direct Gemma or distilled Codex), 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

Citations13
Published2001
Admission routes1
Has abstractyes

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