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Record W3157337102 · doi:10.1080/01430750.2021.1922499

Heat transfer augmentation in a circular tube fitted with tri-partition flow splitters

2021· article· en· W3157337102 on OpenAlexaff
V. S. Chandratre, A. A. Keste, Narayan K. Sane, S. H. Gawande

Bibliographic record

VenueInternational Journal of Ambient Energy · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsTrinity College
Fundersnot available
KeywordsNusselt numberReynolds numberMechanicsMaterials scienceHeat transferSplitter plateSplitterForced convectionThermodynamicsAirflowFluid dynamicsComputational fluid dynamicsPhysicsOpticsTurbulence

Abstract

fetched live from OpenAlex

This paper focuses on the numerical and experimental study of a heated tubes heat transfer and flow characteristics with a novel tri-partition flow splitter under forced convection. A low thermal conducting fluid like air is considered to be a working fluid. Tri-partition flow splitters were manufactured with different thickness and arranged alternatively with 180° rotations to change the direction of airflow. Seven Reynolds number in the range of 5000–15,000 were considered as an operating parameter. The results of the plain tube are validated with the standard Dittus–Boelter and Gnielinski equations available in literature. In addition, the experimental results with tri-partition flow splitters are used to validate the computational fluid dynamics model used to examine the flow characteristics in the heated tube. The overall thermal performance enhancement is evaluated by the performance evaluation criteria (PEC). The results showed that the Nusselt number (Nu) is increased with an increase in Reynolds number. The maximum PEC 1.98 is obtained for 2 mm thickness splitters. The highest Nusselt number (Nu = 271.99) is found for 10 mm thickness with a higher friction drop. Friction factor values are found dropped by reducing the thickness of the splitters.

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 score0.387
Threshold uncertainty score0.478

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 head0.007
GPT teacher head0.204
Teacher spread0.196 · 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
Published2021
Admission routes1
Has abstractyes

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