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Record W2206423201 · doi:10.1115/icnmm2015-48330

The Influence of Electroosmotic Flow on the Von Kármán Vortex Street in the Wake of a Cylinder Located in a Microchannel

2015· article· en· W2206423201 on OpenAlexaff
Mathias Scholz, Dominik P. J. Barz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsQueen's University
FundersDeutscher Akademischer Austauschdienst
KeywordsMicrochannelReynolds numberKármán vortex streetCylinderVortexWakeMechanicsPhysicsVortex sheddingFlow (mathematics)GeometryTurbulenceMathematics

Abstract

fetched live from OpenAlex

The von Kármán vortex street is a flow instability that is observed in the wake of a blunt body if a certain (cylinder) Reynolds number is exceeded. It is one of the classical problems in fluid mechanics and a vast amount of research has been dedicated to the investigation of the fundamentals of this phenomenon. The present study is concerned with the numerical simulation of the flow in a microchannel having a cylinder located in its channel center. A pressure driven flow is induced in the channel described by the channel Reynolds number. The cylinder is subjected to an externally-applied electric field that causes electroosmosis in the electrical double layer which is present around the cylinder surface. In this setup, two distinctions to the classical von Kármán vortex street can be noted. On the one hand, the presence of the microchannel walls confines the flow field in lateral direction. On the other hand, the electroosmotic slip velocity impacts the flow topology in the vicinity of the cylinder and, thus, may have an impact on the formation and the periodic nature of the von Kármán vortex street. Various numerical simulations are performed to investigate the influence of the cylinder-diameter-to-channel-width ratio and the direction of the electrical field.

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)0.001
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 score0.014
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.205
Teacher spread0.195 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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