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Record W2034216618 · doi:10.1115/imece2005-80134

A Numerical Study of the Natural Convective Heat Transfer From a Partially Covered Recessed Isothermal Vertical Surface

2005· article· en· W2034216618 on OpenAlexaff
Patrick H. Oosthuizen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsAdiabatic processIsothermal processMechanicsLaminar flowNatural convectionHeat transferMaterials scienceAdiabatic wallPrandtl numberThermodynamicsPhysics

Abstract

fetched live from OpenAlex

A numerical study of natural convective heat transfer from a heated isothermal vertical plane surface has been considered. There are relatively short horizontal adiabatic surfaces normal to the isothermal surface at the top and bottom of this isothermal surface these horizontal adiabatic wall surfaces then being joined to vertical adiabatic surfaces. There is a thin surface that offers no resistance to heat transfer that is parallel to the vertical isothermal surface and which partly covers this surface. The situation considered is a simplified model of a window, which is represented by the vertical isothermal wall section, that is recessed in a frame, which is represented by the horizontal adiabatic surfaces, which is mounted in a vertical wall, which is represented by the vertical adiabatic surfaces, and which is exposed to a large surrounding room. The window is covered by a partially open plane blind which is represented by the vertical thin surface that offers no resistance to heat transfer. The flow has been assumed to be laminar and two-dimensional. Fluid properties have been assumed constant except for the density change with temperature that gives rise to the buoyancy forces. The governing equations, written in dimensionless form, have been solved using a commercial finite-element based code. Results have only been obtained for a Prandtl number of 0.7.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.592

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 head0.011
GPT teacher head0.223
Teacher spread0.212 · 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
Published2005
Admission routes1
Has abstractyes

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