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Record W4323045238 · doi:10.1063/5.0141058

Time-averaged flow field behind a transversely spinning sphere: An experimental study

2023· article· en· W4323045238 on OpenAlexaff
Zhuoyue Li, Di Zhang, Yakun Liu, Nan Gao

Bibliographic record

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of New Brunswick
FundersNational Natural Science Foundation of China
KeywordsPhysicsDownwashLaminar flowReynolds numberTurbulenceBoundary layerDragMechanicsSpinningParticle image velocimetryLift (data mining)Classical mechanicsVortexMaterials science

Abstract

fetched live from OpenAlex

The aerodynamic forces on a sphere with a rough surface were measured in a water tunnel at a Reynolds number of 7930 and for a range of spinning ratios (α) from 0 to 6.0. The time-averaged flow fields were also measured using particle image velocimetry. The effect of the spinning ratio α on the flow was found to show distinct trends in different regimes, including α≤0.25; 0.25<α≤0.75; 0.75<α≤2.0; 2.0<α≤3.0; and 3.0<α≤6.0. The study identified two critical spinning ratios, where the flow underwent significant changes. The first change occurred in regime II, where the boundary layer over one side of the sphere transitioned from laminar to turbulent, leading to a significant modification in the lift force on the sphere. The second significant change took place across regimes II and III, where the boundary flow in the vicinity of the entire sphere became turbulent. Beyond this range, with α≥3.0, the high spinning rate disturbed the incoming flow, resulting in less-efficient downwash production. The lift increased with α at a slower rate compared to other regimes, and the less-efficient downwash production caused a decrease in drag as more momentum was directed downstream in the horizontal direction.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.606

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 head0.015
GPT teacher head0.252
Teacher spread0.238 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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