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Record W3195886148 · doi:10.5383/ijtee.07.01.002

Heat Transfer Enhancement of Forced Convection in Horizontal Channel with Heated Block due to Oscillation of Incoming Flow

2018· article· en· W3195886148 on OpenAlexvenueno aff
Bouttout Abdelouahab

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsMechanicsNusselt numberHeat transferForced convectionFinite volume methodConvectionWork (physics)Control volumeConvective heat transferFlow (mathematics)Heat transfer enhancementSteady state (chemistry)ThermodynamicsMaterials sciencePhysicsHeat transfer coefficientChemistryReynolds number

Abstract

fetched live from OpenAlex

The study in question consists to amplify the hydrodynamic and thermal instabilities by imposed pulsation during forced convection of air cooling of nine identical heated blocks simulate electronic components mounted on horizontal channel. The finite volume method has been used to solve the governing equations of unsteady forced convection. This approach uses control volume for velocities that are staggered with respect to those for temperature and pressure. The numerical procedure called SIMPLER is used to handle the pressure-velocity coupling. The results show that the time averaged Nusselt number for each heated block depends on the pulsation frequencies and is always larger than in the steady-state case. The new feature in this work is that we obtained a short band of frequencies which the enhancement of heat transfer of all electronic components is greater than 20 % compared with steady non pulsation flow. In addition, the gain in heat transfer Emax attainted the maximum value for the central blocks. Our numerical results were compared with other investigations and found to agree well with experimental data.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.171
Teacher spread0.167 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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