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DNS study of turbulent flow in a spanwise rotating square duct at high rotation numbers

2015· article· en· W2583845633 on OpenAlexaff
Xingjun Fang, Zixuan Yang, Bing-Chen Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTurbulencePhysicsMechanicsVortexRotation (mathematics)Duct (anatomy)Rotation numberSecondary flowClassical mechanicsReynolds numberMean flowAngular velocityRotational speedGeometryMathematics

Abstract

fetched live from OpenAlex

In this paper, direct numerical simulations (DNS) have been performed to study the effects of Coriolis forces on the turbulent field confined within a square duct subjected to spanwise system rotations at very high rotation numbers. The first- and second-order flow statistics, two-point correlations and spectra of velocity fluctuations are analyzed. In comparison with the non-rotating case, secondary flows are induced by system rotations and appear as streamwise counter-rotating vortices. It is observed that at sufficiently high rotation numbers, a Taylor-Proudman region (mean streamwise velocity does not vary along the axis of rotation) appears and complete laminarization is almost reached near the top and side walls. The impact of large organized secondary flows on the production rate and re-distribution of turbulent kinetic energy has been investigated through a spectral analysis.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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)0.0000.000

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 head0.014
GPT teacher head0.220
Teacher spread0.206 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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