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Record W4395676163 · doi:10.18280/mmep.110416

Heat Transfer Enhancement in a Circular Tube with Novel Geometric Turbulator Inserts

2024· article· en· W4395676163 on OpenAlexvenueno aff
Hisham A. Hoshi, Akram H. Abed, Huda A. Al-Salihi, Farhan Lafta Rashid, Awesar A. Hussain

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTurbulatorHeat transfer enhancementTube (container)Heat transferMechanicsMaterials scienceMechanical engineeringComposite materialEngineeringReynolds numberTurbulencePhysicsHeat transfer coefficient

Abstract

fetched live from OpenAlex

The characteristics of heat transmission in a cylindrical tube equipped with novel geometric turbulators inserts were investigated experimentally. The effects of two geometric parameters (ratio of pitch P=L/D and interior angle) on fiction factor, Nusselt number, the thermal executions were tested and compared with a smooth tube for the same conditions. Reynolds number ranged from 4293 to 14310, with interior angles of 20, 60, and 95, and pitch ratios between 4.44 and 5.83. The relation between friction factor and Nusselt number for practical applications have successfully predicted. It was revealed that friction factor, Nusselt number, and thermal performance enhanced with reducing the pitch ratio and the interior angle. Nusselt number raised by 91,117 and 154%, friction factor enhanced by 82.7, 105.3 and 136.1 compared with smooth tube at ratio pf pitch L/D=4.44 and interior angle =20. The factor of thermal performance was discovered to be larger than the unity for all arrangements and the maximum value obtained at =2.2.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.187
Teacher spread0.172 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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