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Record W3002805762 · doi:10.1115/imece2019-11864

A Numerical Study of Natural Convective Heat Transfer From Two-Sided Inclined Square Plates Having a Finite Thickness

2019· article· en· W3002805762 on OpenAlexaff
Rafiq Manna, Patrick H. Oosthuizen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsQueen's University
Fundersnot available
KeywordsNusselt numberMechanicsMaterials scienceLaminar flowHeat transferDimensionless quantityNatural convectionTurbulenceGeometryReynolds numberPhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract Simultaneous natural convective heat transfer from the top, bottom and side surfaces of two-sided inclined square plates having various thicknesses has been numerically investigated. The aim of this work is to determine whether the plate thickness has a significant influence on the heat transfer rates from the plate surfaces when the plate is inclined to the horizontal and to determine how the heat transfer rate varies with this angle of inclination. The upper, lower and side surfaces of the plate have been assumed to be isothermal and at the same temperature which is higher than that of the surrounding fluid. The range of conditions considered is such that laminar, transitional, and turbulent flow occur over the plate. The numerical solution has been obtained using the commercial CFD solver ANSYS FLUENT©. In this study, results have only been obtained for the case where the plate is exposed to air. Inclination angles of between 0 and 40 degrees from the horizontal and plate dimensionless thicknesses (thickness-to-side length ratios) of between 0 and 0.3 have been considered. Variations of the mean Nusselt number with Rayleigh number for the top surface, bottom surface, side surface and that averaged over all heated surfaces of the plate for various inclination angles and for various plate dimensionless thicknesses have been obtained.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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.

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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