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Record W2054623129 · doi:10.1115/ht2012-58168

A Numerical Study of Natural Convective Flow Through a Vertical Symmetrically Heated Channel With an Unheated Upper Section

2012· article· en· W2054623129 on OpenAlexaff
Patrick H. Oosthuizen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsQueen's University
Fundersnot available
KeywordsPrandtl numberMechanicsHeat transferAdiabatic processThermodynamicsNatural convectionRayleigh numberAdiabatic wallMaterials scienceVolumetric flow ratePressure dropEnergy–depth relationship in a rectangular channelFlow (mathematics)Open-channel flowPhysicsChézy formula

Abstract

fetched live from OpenAlex

Natural convective flow through a vertical plane channel has been considered. The walls of the lower portion of the channel are heated to a uniform temperature, both of the heated walls being at the same temperature. The walls of the upper part of the channel are unheated, i.e., are adiabatic. The reason for undertaking the study arose from the fact that using an adiabatic upper wall section can, through the so-called chimney effect, increase the flow rate through the channel and therefore increase the heat transfer rate from the lower heated wall section. However if the upper adiabatic wall section is too long the increased pressure drop due to viscous effects can lead to a reduced flow rate through the channel and to a reduced heat transfer rate. Therefore a need existed to examine in more detail the effect that the height of the upper adiabatic wall section has on the heat transfer rate. The flow has been assumed to be steady and the Boussinesq approximation has been adopted. The solutions have been obtained using the commercial CFD code FLUENT©. The solution has the Rayleigh number, the Prandtl number, the ratio of the channel width to the height of the heated channel wall section, and the ratio of the height of the adiabatic channel wall section to the height of the heated wall section as parameters. Results have only been obtained for a Prandtl number of 0.74 (the value for air at temperatures near ambient temperature). Results have been obtained for a wide range of values of the remaining parameters and the effects of these parameters on the mean Nusselt number have been studied.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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
Published2012
Admission routes1
Has abstractyes

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