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Record W4311356897 · doi:10.18280/mmep.090532

Augmentation of Plate-Fin Heat Exchanger Performance with Support of Various Types of Fin Configurations

2022· article· en· W4311356897 on OpenAlexvenueno aff
Ali Abbas, Ayad Ali Mohammed

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHeat transferNusselt numberLaminar flowMechanicsFinHeat transfer coefficientHeat exchangerPlate fin heat exchangerMaterials scienceAnnular finMicro heat exchangerHeat transfer enhancementHeat sinkPlate heat exchangerThermodynamicsTurbulenceReynolds numberPhysicsComposite material

Abstract

fetched live from OpenAlex

A plate fin heat exchanger PFHE is a form of compact heat exchanger CHE containing of a block of alternating layers of fins and flat separators known as parting sheets. In this paper, 3-dimensional incompressible laminar flow and heat transfer in a (PFHE) has been investigated numerically. The influences using different fin configurations and the effect of the offset of these fins, on thermal and hydro-dynamic fields are presented. The cases of study for the PFHE are established, by using the fins with different configurations (plain rectangular (PR) and it's offset (OR), plain triangular (PT) and it's offset (OT) which is a novel shape, and a novel combination of a two fin shapes, between rectangular and triangular (ORT)). The upper and lower plates are exposed to a constant heat flux and the working fluid is air where chosen under a laminar range of (Re) number (600 to 1400). The laminar flow and heat transfer is governed by continuity, momentum and energy equations. ANSYS FLUENT (2021 R1) is used to get the numerical results, based on finite volume method. One of the most utilized fins for a PFHE is the offset fin arrangement due to its higher heat change amount, adjustable structure, and enhances flow turbulence and heat transfer coefficient by interrupting the thermal boundary layer. The obtained results of using of the offset configuration increases the Nusselt number by 20.32% and 17.82% for OSF rectangular and OSF triangular, respectively as compared with the with plain configurations, as a result, a significant heat transfer enhancement is observed. And significant increase in the friction-factor is found to be 79.8% and 48.7% for OT and ORT, as compared with the PT. It can be observed that use of the OSF configuration leads to greater f-factor for triangular configuration values. In addition, the temperature fields for the primary and secondary flows were showed in a contour diagram.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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)0.0030.000

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 head0.017
GPT teacher head0.191
Teacher spread0.174 · 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 designBench or experimental
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

Citations10
Published2022
Admission routes1
Has abstractyes

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