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Record W4410964959 · doi:10.1063/5.0264818

Numerical investigation on aerodynamic characteristics of two rotating cylinders in tandem

2025· article· en· W4410964959 on OpenAlexaff
Jialin Wang, Xiaopeng Xue

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsPhysicsAerodynamicsMechanicsTandemComputational fluid dynamicsAerospace engineeringClassical mechanics

Abstract

fetched live from OpenAlex

To solve the problem of unsteady wake and high drag in the flow around a cylinder, combined with the advantages of two cylinders in tandem in drag reduction and rotating cylinder in restraining wake oscillation, the k−ω Shear Stress Transport turbulence model was used to explore the aerodynamic characteristics and flow field structures of two rotating cylinders in tandem at different speed ratios under the condition of Re = 200, gap ratio L/D = 1.5, 2.5, and 3.5, respectively. The effectiveness of the method is verified by comparing the results with the available experiments. The streamline, periodic averaged lift and drag coefficient and the pressure coefficient on the cylinder surface are given and analyzed in detail. It was found that for the rotating single cylinder, when the speed ratio is 4.1, it has best the aerodynamic performance. For the rotating double cylinders, the higher gap ratio can increase the combined lift coefficient and reduce the combined drag coefficient under the condition of bigger speed ratios of cylinder 1. When the gap ratio is 3.5, the rear cylinder is stationary or has a higher speed ratio, it can restrain the wake oscillation of the front cylinder. When the front to rear cylinder speed ratio is 2 and 1, respectively, the aerodynamic characteristics of the rotating double cylinder system have best performances.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.413

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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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