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ANALYSIS OF FLUID-FLOW CHARACTERISTICS AND HEAT-TRANSFER PATTERNS AROUND A SQUARE BLUFF BODY

2024· article· en· W4399311379 on OpenAlexaff
Amin Etminan, Zambri Harun

Bibliographic record

VenueInternational journal of fluid mechanics research · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMechanicsSquare (algebra)CylinderHeat transferFlow (mathematics)Materials scienceThermodynamicsMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

In this study, we investigate a numerical simulation of the flow and heat transfer characteristics around a single square cylinder subjected to incidence in a two-dimensional plane. The Reynolds and Prandtl numbers are set at <i>Re</i> = 100 and <i>Pr</i> = 0.71, respectively. The cylinder's surface is subjected to pressure and viscous forces due to the passing flow, with the magnitude of these forces influenced by the cross-sectional shape of the bluff body, the angle of attack, and flow velocity. The angle of orientation for the square cylinder varies from 0° to 45° in 5° increments. The study begins with comprehensively examining the governing equations, simulation procedures, and grid generation, employing an efficient and robust in-house finite volume code. Subsequently, we present and discuss the instantaneous streamlines, velocity components, vorticity, and isotherm patterns for different angles of attack. Additionally, global quantities such as viscous, pressure, total lift, drag coefficients, their root-mean-square, Strouhal, and Nusselt numbers are analyzed for various angles of attack. It is observed that the frequency of vortex shedding decreases with an increasing angle of attack. Furthermore, the values of global quantities and flow and temperature patterns remain relatively constant for angles of attack in the range of θ = 30°-45°. The numerical results show good agreement with experimental and numerical data in the existing literature.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.024
GPT teacher head0.322
Teacher spread0.298 · 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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