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Record W4367171776 · doi:10.18280/mmep.100215

Numerical Implementation of Direct and Reverse Flow for Plate Heat Exchanger

2023· article· en· W4367171776 on OpenAlexvenueno aff
Hasan Sh. Majdi, Wajeeh K. Hasan, Mohammed Ali Mahmood Hussein, Laith Jaafer Habeeb

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHeat exchangerFlow (mathematics)Plate heat exchangerMechanicsMechanical engineeringMaterials scienceComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

One of the best solutions for the heat exchange is the plate heat exchangers, which are characterized by high thermal efficiency.Where in this paper work has been done on a plate heat exchanger with dimensions appropriate to its practical application and changing the fluid exit areas to increase the surface area of exchange.And the use of more than one class to see the improvements and changes that can be analyzed and benefited from the first part represents the heat exchanger with a direct direction and the change in the number of plates.Where two sets of models were designed, the first representing the change in the number of plates, where 10, 20 and 30 plates were used to irrigate the real rate of increasing the number of plates.As for the second part, it represents the heat exchanger with a reverse path and compare them with each other.The result show that when increasing the number of plates in the plate heat exchanger, it increases the transfer area and thus increases the value of the transferred heat energy.In the case where the best transfer of heat energy is compared to 20 and 10 blades, where the exit temperature reached 308.1 K, it is noticing an increase in the transfer of thermal energy.

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: Methods · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.409

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 head0.027
GPT teacher head0.235
Teacher spread0.209 · 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
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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