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Record W1994798716 · doi:10.1115/1.1852471

Flow Past a Spinning Sphere With Surface Blowing and Heat Transfer

2005· article· en· W1994798716 on OpenAlexafffund
Hamid Niazmand, Metin Renksizbulut

Bibliographic record

VenueJournal of Fluids Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDragMechanicsHeat transferReynolds numberLift (data mining)WakeSpinningFlow (mathematics)PhysicsClassical mechanicsMaterials scienceTurbulence

Abstract

fetched live from OpenAlex

Computations are performed to determine the transient three-dimensional heat transfer rates and fluid forces acting on a stream-wise spinning sphere for Reynolds numbers in the range 10⩽Re⩽300 and angular velocities Ωx⩽2. In this Re range, classical flow past a solid sphere develops four different flow regimes, and the effects of particle spin are studied in each regime. Furthermore, the combined effects of particle spin and surface blowing are examined. Sphere spin increases drag in all flow regimes, while lift shows a nonmonotonic behavior. Heat transfer rates are not influenced by spin up to a certain Ωx but increase monotonically thereafter. An interesting feature associated with sphere spin is the development of a special wake regime such that the wake simply spins without temporal variations in its shape. For this flow condition, the magnitudes of the lift, drag, and heat transfer coefficients remain constant in time. Correlations are provided for drag and heat transfer.

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)0.002
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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

Citations17
Published2005
Admission routes2
Has abstractyes

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