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Record W2081631165 · doi:10.1115/ihtc14-22082

Numerical Analysis of Laminar Forced Convection in Sinusoidal-Wavy-, Rounded-Ellipse-, and Rounded-Vee-Shaped Corrugated-Plate Channels

2010· article· en· W2081631165 on OpenAlexafffund
Dean Ferley, Scott J. Ormiston

Bibliographic record

Venue2010 14th International Heat Transfer Conference, Volume 2 · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNusselt numberPrandtl numberReynolds numberLaminar flowMechanicsHeat transferMaterials scienceForced convectionGeometryThermodynamicsPhysicsMathematicsTurbulence

Abstract

fetched live from OpenAlex

Numerical analysis of steady, two-dimensional, laminar forced convection in corrugated-plate channels is performed using a commercial CFD code: ANSYS CFX. The flow domain consists of six modules in each of three wall corrugations: sinusoidal-wavy-shaped (SWS), rounded-ellipse-shaped (RES), and rounded-vee-shaped (RVS). One ratio of minimum-to-maximum plate spacings and one module length-to-height ratio is considered. Fluid flow and heat transfer are repeating in the modules and the results are examined in a typical module in the fully-developed region for Reynolds numbers in the range of 25 to 300 for Prandtl numbers of 0.7 (air), 2.29 (water), and 34.6 (ethylene glycol). The RES corrugation produced the highest peak value of local Nusselt number as well as the highest friction factor. The SWS corrugation produced the highest average Nusselt number, except at a Prandtl number of 34.6 at higher Reynolds number where the RES corrugation had the highest value. The RVS corrugation had the lowest friction factor for the geometric configuration considered. The highest heat transfer rate per unit pumping power was found at the highest Prandtl number for the RES corrugation.

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), Insufficient payload (model declined to judge)
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 score0.566
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.218
Teacher spread0.206 · 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 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
Published2010
Admission routes2
Has abstractyes

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Same venue2010 14th International Heat Transfer Conference, Volume 2Same topicFluid Dynamics and Turbulent FlowsFrench-language works237,207