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Record W4389584939 · doi:10.17118/11143/21008

Effect of length scale on expressing the results of unsteady naturalconvective heat transfer from thin horizontal plates of simple and complexshapes

2023· article· en· W4389584939 on OpenAlexaff
Koustav Bandyopadhyay, Patrick H. Oosthuizen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsSimple (philosophy)Heat transferScale (ratio)ConvectionMechanicsNatural convectionLength scaleMeteorologyComputer scienceOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract: This investigation focuses on the use of a length scale on presenting the results of numerical studies of the natural convective heat transfer from thin horizontal plates of simple and complex shapes in both steady and unsteady states. Commercial CFD software ANSYS FLUENT was used to obtain the results presented in this study. To incorporate the effect of temperature on fluid density, the Boussinesq approximation was used. The Rayleigh number was varied between laminar values at 102 and transitional flow values at the Rayleigh number of 105. Two characteristic length scales were used for expressing the variation of Nusselt number and the Rayleigh number. These length scales were square root of surface area (Root Area) of a single side and 4*Total surface area/Total perimeter (4A/P). From the transient Nusselt number variations obtained for simple and complex shapes, it was noted that when Root Area was used as the length scale, all shapes had distinctly different Nusselt number variations with dimensionless time at all Rayleigh numbers. However, when 4A/P was used as length scale, a good overlap was obtained for the results for all shapes in the steady and unsteady state for lower Rayleigh numbers. At higher Rayleigh numbers, the Nusselt number variations again diverged for various shapes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.967

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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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 designBench or experimental
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

Citations0
Published2023
Admission routes1
Has abstractyes

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