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Record W2902993298 · doi:10.1139/tcsme-2017-511

FORCED CONVECTION BASED HEAT TRANSFER ANALYSIS OF SPHERICAL DIMPLE AND PROTRUSION SURFACE IN TURBULENT FLOW

2017· article· en· W2902993298 on OpenAlexvenueno aff
Ashif Perwez, Shreyak Shende, Rakesh Kumar

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsDimpleNusselt numberMechanicsTurbulenceHeat transferReynolds numberMaterials scienceHeat transfer enhancementFlow (mathematics)VortexPhysicsComposite material

Abstract

fetched live from OpenAlex

An experimental and numerical investigation is performed to study the effect of dimple and protrusion geometry on the heat transfer enhancement and the friction factor of surfaces with dimples and protrusions subjected to turbulent flow. The parameters used to compare the spherical dimples and protrusions are Nusselt Number, friction factor, and flow pattern. These parameters are obtained for a Reynolds number of 10500-60900. The spherical dimple results showed the greater heat transfer, which is about 6.97% higher and pressure loss which is 5.07% lower than the spherical protrusion. The realistic heat transfer augmentation capabilities of channels with dimples and protrusions can be studied from the experimental results. The comparison is made with respect to the smooth rectangular channel under the same flow and thermal boundary conditions. The numerical analysis is performed which shows the different vortex flow structures of the spherical dimples and protrusions channel.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.664
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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 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

Citations8
Published2017
Admission routes1
Has abstractyes

Explore more

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