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Record W4285737418 · doi:10.18280/rcma.320307

Effect of Spacing Between Baffles on the Dynamic and Thermal Behavior of Water in a Shell-and-Tube Heat Exchanger

2022· article· en· W4285737418 on OpenAlexvenueno aff
Ahmed Youcef, Rachid Saïm

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsBaffleMechanicsPressure dropShell and tube heat exchangerTurbulenceHeat transferConcentric tube heat exchangerDynamic scraped surface heat exchangerReynolds numberHeat transfer coefficientThermodynamicsHeat exchangerMaterials scienceChemistryPhysicsCritical heat flux

Abstract

fetched live from OpenAlex

A numerical simulation of a turbulent flow of water in three shell and tube heat exchangers equipped with 6, 8, 10 segmental baffles as the working fluid at Reynolds numbers ranging from 24327, 33356, 42569 are performed. The conservation equations of mass, momentum and energy are solved by the finite volume method based on the SIMPLE algorithm for coupling velocity-pressure, the mathematical model k-ε within the Fluent software is used in the different cases presented. The temperature, the velocity, the friction factor, the heat transfer coefficient, the pressure drop in the shell, the total heat transfer rate between the tubes and the fluid, the overall performance factor are studied for the three spacing between baffles. This work contributes largely to the understanding of turbulent flows and also shows the effect of the baffles number on the heat transfer in the heat exchangers. The velocity of flow in the shell increases by 1.49% and 1.62% and 1.73% of the reference velocity. The results show an increase in the outlet temperature, heat transfer coefficient 1.09% and 1.28%, pressure drop 1.7% and 2.82%, the total heat transfer rate 1.08% and 1.18% because of the increase in number of baffles. The results obtained in this study are in good agreement and correspond to the results given by the literature.

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 score0.046
Threshold uncertainty score0.385

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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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