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Record W4385196885 · doi:10.32920/23737458.v1

The effects of tube Dimples-Protrusions on the thermo-fluidic properties of turbulent forced-convection

2023· preprint· en· W4385196885 on OpenAlexaff
Saeed Farsad, Mahmoud Mashayekhi, Mohammad Hossein Zolfagharnasab, Mohammad Lakhi, Foad Farhani, Kourosh Zareinia, Vahab Okati

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDimpleTurbulenceHeat transferHeat exchangerMechanicsBlankHeat transfer enhancementFlow (mathematics)Reynolds numberMechanical engineeringPhysicsMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

<p>Among the techniques offered to improve the efficiency of <a href="https://www-sciencedirect-com.myaccess.library.utoronto.ca/topics/engineering/heat-exchanger" target="_blank">heat exchangers</a>, the Dimpled surfaces were repeatedly reported as an applicable solution. In this regard, the current study is formed to evaluate the thermofluidic performance of Dimpled-Protruded tubes while following four novel investigations. Firstly, the impact of Protrusions next to the typical Dimple shape on the turbulence mixing and the forced-convection phenomenon was investigated. Secondly, a detailed comparison was carried out between the interfacial <a href="https://www-sciencedirect-com.myaccess.library.utoronto.ca/topics/engineering/heat-transfer" target="_blank">heat transfer</a> of the Dimpled-Protruded tube and the smooth equivalent. Thirdly, a novel Dimpled-Protruded arrangement was utilized, and its thermal performance was evaluated in various Reynolds (<em>Re</em>) numbers. Lastly, both zonal and interfacial heat transfer mechanisms intensified by using Dimpled-Protruded shapes were scrutinized.</p> <p>Based on the results, the small vorticities at the Dimpled-Protruded locations were responsible for increasing the interfacial heat transfer. Moreover, the <a href="https://www-sciencedirect-com.myaccess.library.utoronto.ca/topics/engineering/rough-tube" target="_blank">rough tube</a> prompted the <a href="https://www-sciencedirect-com.myaccess.library.utoronto.ca/topics/engineering/flow-turbulence" target="_blank">flow turbulence</a> at lower <em>Re</em>; thus, the heat transfer improved by 36.21% compared with the smooth type. Meanwhile, although the <a href="https://www-sciencedirect-com.myaccess.library.utoronto.ca/topics/engineering/heat-convection" target="_blank">convective heat transfer</a> improved up to 84.46%, the <a href="https://www-sciencedirect-com.myaccess.library.utoronto.ca/topics/engineering/friction-loss" target="_blank">friction losses</a> increased between 25% and 60% as the <em>Re</em> increased. Fortunately, however, the <a href="https://www-sciencedirect-com.myaccess.library.utoronto.ca/topics/engineering/friction-effect" target="_blank">friction effects</a> have produced insignificant pressure drops, proving that Dimpled-Protruded tubes effectively improve forced-convecting heat transfer.</p> <p> </p>

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 score0.030
Threshold uncertainty score0.621

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 integrity0.0000.001
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 head0.033
GPT teacher head0.215
Teacher spread0.182 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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