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Record W4308411110 · doi:10.1016/j.ijft.2022.100244

CFD analysis of turbulent forced convection over a spinning cylinder in an enclosed channel

2022· article· en· W4308411110 on OpenAlexfundno aff
Md. Rakib Hossain, Mohammad Arif Hasan Mamun, Sumon Saha

Bibliographic record

VenueInternational Journal of Thermofluids · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsnot available
FundersDepartment of Mechanical Engineering, University of Alberta
KeywordsReynolds numberMechanicsNusselt numberTurbulenceDragDrag coefficientPotential flow around a circular cylinderCylinderPhysicsForced convectionHeat transferMaterials scienceThermodynamicsGeometryMathematics

Abstract

fetched live from OpenAlex

A CFD approach has been used to analyze fully developed turbulent flow and heat transport over a spinning hot circular solid cylinder inside an enclosed channel. The finite element approach is used to solve the Reynolds-Averaged Navier-Stokes and energy equations and the shear stress transport model. The flow is explored for various Reynolds numbers ranging from 3 × 10 3 to 10 7 with the blockage ratios of 0.05, 0.1, and 0.15 along with the variation of speed ratios of the rotating cylinder. This study found that the direction and speed of the cylinder have an insignificant effect on thermo-fluid characteristics. On the other hand, the drag coefficient decreases while the average Nusselt number increases to a specific value of Reynolds number and then becomes almost constant. Effectiveness increases for a lower Reynolds number and then becomes stable. The heat performance of the rotating cylinder for the stationary one and the contributions of pressure drag are presented here.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2022
Admission routes1
Has abstractyes

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