MétaCan
Menu
Back to cohort
Record W3030141532 · doi:10.1063/5.0008952

Wake topology and dynamics over a slender body at a high incidence and their relation to structural loading

2020· article· en· W3030141532 on OpenAlexafffund
Qihang Yuan, Serhiy Yarusevych

Bibliographic record

VenuePhysics of Fluids · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Waterloo
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsWakePhysicsMechanicsReynolds numberVortexVortex sheddingFlow (mathematics)Classical mechanicsTurbulence

Abstract

fetched live from OpenAlex

The flow over a slender cylindrical body with a hemisphere end was studied experimentally using a combination of force balance and time-resolved particle image velocity measurements. The investigation was performed at a subcritical Reynolds number (Re = 11 000) over a range of high incidence angles from 30° to 90°. The results show that significant cross-flow loading occurs for a range of incidence angles from 50° to 70°, with maximum mean and fluctuating loads taking place at 60°. Within this range of incidence angles, the loading has a bimodal nature, with intermittent switching between two states associated with the positive and negative cross-flow loading direction. The analysis of simultaneous force and wake measurements reveals that the two loading regimes are produced by two distinct wake topologies defined by strongly asymmetric vortex dynamics near the tip of the model. The results provide insight into salient features of the wake development and vortex dynamics and show that transient changes in the cross-flow force direction progress through a consistent change in the wake structure between two bounding quasi-steady states.

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 score0.857
Threshold uncertainty score0.394

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 head0.007
GPT teacher head0.213
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 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
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuePhysics of FluidsSame topicFluid Dynamics and Vibration AnalysisFrench-language works237,207