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Record W4401136038 · doi:10.18280/mmep.110701

Numerical Investigation of Mass and Heat Transfer in a New Coaxial with Shell-and-Tube Heat Exchanger

2024· article· en· W4401136038 on OpenAlexvenueno aff
Chabane Medjdoub, Abdelhakim Benslimane, Djamel Sadaoui

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsShell and tube heat exchangerCoaxialHeat exchangerTube (container)MechanicsMaterials scienceShell (structure)BaffleConcentric tube heat exchangerHeat transferMechanical engineeringPhysicsComposite materialEngineering

Abstract

fetched live from OpenAlex

In the present article, a novel coaxial with shell-and-tube (CWST) heat exchanger is developed and simulated using Ansys-Fluent®, and its results are compared with those of the shell-and-tube heat exchanger from which it is derived.The geometry of this new exchanger was given, specifying the different types of fluids it can contain and their circulations, and a theoretical calculation based on the NTU method (number of transfer units method) is used to validate the simulations.In order to be able to analyse the phenomena occurring inside this exchanger, the fluid temperature, pressure, and velocity distribution figures are given with the evolutionary curve of some performance parameters (the heat, the pressure losses, the ratio between heat and pressure losses, the heat flux and the overall heat transfer coefficient) as a function of the cold fluid volume rate.At the end the various advantages it can give to enhance the efficiency of heat transfer, to reduce manufacturing and operating costs, as well as its potential for further research on the improvement of the design were explained.

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 score0.776
Threshold uncertainty score0.544

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 head0.018
GPT teacher head0.189
Teacher spread0.171 · 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
Published2024
Admission routes1
Has abstractyes

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