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Record W2466926818 · doi:10.1016/j.egypro.2015.07.655

Numerical Investigation of Heat Transfer Performance of Various Coiled Square Tubes for Heat Exchanger Application

2015· article· en· W2466926818 on OpenAlexaff
Jundika C. Kurnia, Agus P. Sasmito, Saad Akhtar, Tariq Shamim, Arun S. Mujumdar

Bibliographic record

VenueEnergy Procedia · 2015
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsMcGill University
Fundersnot available
KeywordsHeat transferHeat exchangerSecondary flowMechanicsMaterials scienceMicro heat exchangerConcentric tube heat exchangerPlate fin heat exchangerShell and tube heat exchangerPlate heat exchangerHeat transfer enhancementHeat spreaderDynamic scraped surface heat exchangerCurvatureHeat transfer coefficientTube (container)ThermodynamicsCritical heat fluxComposite materialGeometryPhysicsMathematicsTurbulence

Abstract

fetched live from OpenAlex

In heat exchanger application, working fluid inside the tubes is subjected to considerable temperature changes. In order to improve heat transfer performance, various strategies are proposed and evaluated; one of them is the application of coiled tubes. Coiled tubes have been used widely in heat exchanger application mainly due to the presence of secondary flow which enhances heat transfer considerably. This study addresses heat transfer performance of three configurations of coiled tubes with square cross-section, i.e. in-plane, helical and conical coiled tubes, subjected to large temperature difference. Their heat transfer performance is numerically evaluated and compared with that of a straight tube with identical cross-section and length. A concept of Figure of Merit (FoM) is introduced and utilized to fairly asses the heat transfer performance of the coiled tube configurations. The results indicate that FoM increase as the wall temperature increase. In addition, combination of temperature-induced buoyancy flow and curvature-induced secondary flow considerably affect the flow behavior and heat transfer performance inside the tubes.

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 score0.167
Threshold uncertainty score0.516

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 head0.014
GPT teacher head0.202
Teacher spread0.187 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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