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Record W1981164044 · doi:10.1017/s0022112004000497

Bounds on horizontal convection

2004· article· en· W1981164044 on OpenAlexaff
Jennifer H. Siggers, Rich R. Kerswell, Neil J. Balmforth

Bibliographic record

VenueJournal of Fluid Mechanics · 2004
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNusselt numberBoundary layerMechanicsEntropy productionBounded functionEntropy (arrow of time)Heat transferPhysicsThermodynamicsMathematical analysisMathematicsReynolds number

Abstract

fetched live from OpenAlex

For a fluid layer heated and cooled differentially at its surface, we use a variational approach to place bounds on the viscous dissipation rate and a horizontal Nusselt measure based on the entropy production. With a general temperature distribution imposed at the top of the layer and a variety of thermal boundary conditions at the base of the layer, the horizontal Nusselt number is bounded by $cR_H^{1/3}$ as the horizontal Rayleigh number $R_H \,{\rightarrow}\, \infty$ , for some constant $c.$ The analysis suggests that the ultimate regime for this so-called ‘horizontal convection’ is one in which the temperature field develops a boundary layer of width $O(R_H^{-1/3})$ at the surface, but has no variation in the interior. Although this scenario resonates with results of dimensional scaling theory and numerical computations, the bounds differ in the dependence of the Nusselt measure on $R_H$ . Numerical solutions for steady convection appear to confirm Rossby's result that the horizontal Nusselt number scales like $R_H^{1/5}$ , suggesting either that the bound is not tight or that the numerics have yet to reach the asymptotic regime.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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 designTheoretical or conceptual
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

Citations76
Published2004
Admission routes1
Has abstractyes

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