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Record W3027788265 · doi:10.1615/tfec2020.fnd.032194

A NUMERICAL STUDY OF ASSISTING MIXED CONVECTIVE HEAT TRANSFER FROM NARROW ISOTHERMAL VERTICAL FLAT PLATES

2020· article· en· W3027788265 on OpenAlexaff
Mohamed Elkhmri, Patrick H. Oosthuizen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsIsothermal processConvectionHeat transferMechanicsMaterials scienceConvective heat transferCombined forced and natural convectionNatural convectionThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Many studies of mixed convective heat transfer from heated flat plates with the forced flow parallel to the plate surface are available, most of these studies dealing with wide plates. However, practical situations exist in which there is effectively mixed convective heat transfer from narrow plates and in such cases the relative plate width is expected to have a significant influence on the heat transfer rate. The present study numerically investigates how the relative width of the plate affects the mixed convective heat transfer rate from a thin narrow vertical plate. It has been assumed that the plate surfaces are isothermal. The Boussinesq approach has been adopted. The solution has been obtained using ANSYS FLUENT . The mean heat transfer rate from the heated surface of the plate has been expressed in terms of a Nusselt number based on the length of the plate, this Nusselt numbers being dependent on the Rayleigh number and the Reynolds number based on the plate length, on the ratio of the plate width to the plate length, and on the Prandtl number. Results have only been obtained for a Prandtl number of 0.74. Variations of the Nusselt numbers with Rayleigh number and with Reynolds number for various dimensionless plate widths have been obtained, the results showing that the effect of the plate width on the heat transfer rate can be significant.

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 score0.199
Threshold uncertainty score0.502

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 head0.017
GPT teacher head0.212
Teacher spread0.194 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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