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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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 score0.540
Threshold uncertainty score0.801

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 head0.214
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