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Record W3175513569 · doi:10.18280/mmep.080307

2D Numerical Study of Heat Transfer Enhancement Using Fish-Tail Locomotion Vortex Generators

2021· article· en· W3175513569 on OpenAlexvenueno aff
Ahmed Hashim Yousif, Hakim T. Kadhim, Kadhim K. Idan Al‐Chlaihawi

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHeat transferMechanicsHeat transfer enhancementReynolds numberVortexVortex generatorMaterials scienceFlow (mathematics)Fish <Actinopterygii>ThermodynamicsHeat transfer coefficientPhysicsTurbulenceFisheryBiology

Abstract

fetched live from OpenAlex

In this paper, a numerical simulation is performed to study the effect of two types of concave vortex generators (VGs), arranged as fish-tail locomotion in a rectangular channel. The heat transfer and fluid flow characteristics with and without VGs are examined over the Reynolds number range 200≤Re≤2200.The two proposed types of the VGs are selected based on the speed of the fish movement which is arranged in different distances between them (d/H=0.6, 1, 1.3). The results show that the use of VGs can significantly enhance the heat transfer rate, but also increases the friction factor. The heat transfer performance is enhanced by (4-21.1%) reaching the maximum value by using the first type of the VGs at (d/H=1.3) due to better mixing of secondary flow and the new arrangement of the VGs which lead to decreasing the friction factor with an easy flow of fluid.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.214
Teacher spread0.187 · 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 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

Citations4
Published2021
Admission routes1
Has abstractyes

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