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Record W4296466552 · doi:10.18280/mmep.090420

Study of Baffles Arrangement Influence on the Natural Convection into a Heated Square Channel

2022· article· en· W4296466552 on OpenAlexvenueno aff
Saad Najeeb Shehab

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsnot available
FundersMustansiriyah University
KeywordsBaffleNusselt numberMechanicsMaterials scienceHeat transferHeat fluxHeat transfer coefficientChannel (broadcasting)ThermodynamicsNatural convectionReynolds numberEngineeringPhysicsElectrical engineeringTurbulence

Abstract

fetched live from OpenAlex

The present experimental paper is officiated to study the heat characteristics and performance of air flow through a square cross-sectional heated channel under natural heat convection conditions. The influence of baffles arrangement and perforated baffles on the rate of heat transfer through a channel are studied. Four cases of a heated channels are studied, one is a plain channel and other three channels with a baffle namely, three inline baffles, three staggered baffles and three staggered perforated baffles arrangements. The outer tested channels surfaces are electrically heated with a constant surface heat flux condition. All tested channels are fabricated by cold forming with constant dimensions using aluminum plate has thickness of 1.0 mm. The heat performance is assessed for all tested channels. The results of present paper are approved than the available data in the previous paper and a good convergence are noticed. Obtained experimental results appear that a three staggered perforated baffles arrangement is a best selection as it improves heat transfer rate in expression of Nusselt number, it higher about 22% to 27% than that the plain channel for same conditions.

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: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.546

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 head0.022
GPT teacher head0.201
Teacher spread0.178 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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