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Record W2586714122 · doi:10.1115/imece2016-65716

A Numerical Study of the Effect of Triangular Waves on Natural Convective Heat Transfer From an Upward Facing Heated Horizontal Isothermal Surface

2016· article· en· W2586714122 on OpenAlexafffund
Patrick H. Oosthuizen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNusselt numberPrandtl numberMechanicsNatural convectionRayleigh numberHeat transferLaminar flowFilm temperatureThermodynamicsBuoyancyTurbulenceMaterials sciencePhysicsReynolds number

Abstract

fetched live from OpenAlex

A numerical study of natural convective heat transfer from an upward facing, heated horizontal isothermal surface imbedded in a large flat adiabatic surface has been undertaken. On the heated surface is a series of triangular shaped waves. Laminar, transitional, and turbulent flow conditions have been considered. The flow has been assumed to be two-dimensional and steady. The fluid properties have been assumed constant except for the density change with temperature giving rise to the buoyancy forces. This was with treated using the Boussinesq approach. The numerical solution has been obtained using the commercial CFD solver ANSYS FLUENT©. The k-epsilon turbulence model with full account being taken of buoyancy force effects has been employed. The heat transfer rate from the heated surface expressed in terms of a Nusselt number is dependent on the Rayleigh number, the number of waves, the height of the waves relative to the width of the heated surface, and the Prandtl number. This study obtained results for a Prandtl number of 0.74 which is effectively the value for air. An investigation of the effect of the Rayleigh number, the dimensionless height of the surface waves, and the number of surface waves on the Nusselt number has been undertaken.

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 score0.050
Threshold uncertainty score0.485

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 head0.005
GPT teacher head0.219
Teacher spread0.213 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

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