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Record W2016569308 · doi:10.1115/1.1758266

Analysis and Modeling of Pressure Recovery for Separated Reattaching Flows

2004· article· en· W2016569308 on OpenAlexaff
W. W. H. Yeung, G. V. Parkinson

Bibliographic record

VenueJournal of Fluids Engineering · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVortexMechanicsInviscid flowFlow separationAdverse pressure gradientGeometryFlow (mathematics)PhysicsPerpendicularTotal pressureMathematicsGeologyTurbulence

Abstract

fetched live from OpenAlex

Analyses have been carried out on the mean pressure data for separated reattaching flows downstream of a variety of 2-D bluff-bodies to reveal some similarity features. The step height has been identified as an important parameter in relationships such as the correlation between the reattachment length xr and the initial shear-layer angle. The separation velocity (deduced from separation pressure cps) in the direction perpendicular to the upstream flow increases linearly with the reattachment length at fixed step heights. The streamwise location of the vortex center xv (deduced from mean streamline plots) correlates with the location of minimum pressure xm and each varies linearly with the reattachment length. Pressure force, moment and center of pressure induced by the standing vortex also increase with the reattachment length. An inviscid flow model of a rectilinear stationary vortex above a flat wall leads to a general form of the pressure recovery cp−cp min/cp max−cp min)=8/9x^2x^2+1/x^2+1/32xm<,<xr where 0⩽x^1=Xm/Xr and cp max and cp min are respectively the maximum and minimum pressure coefficients. It is demonstrated that the present analyses allow the pressure distributions downstream of various fore-bodies to be realistically predicted.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.255

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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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