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Record W2069409579 · doi:10.1115/1.2427087

Nanoscale Fluid Flow Over Two Side-by-Side Cylinders With Atomically Rough Surface

2006· article· en· W2069409579 on OpenAlexafffund
Ali S. Ziarani, A. A. Mohamad

Bibliographic record

VenueJournal of Fluids Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDragLift (data mining)Surface finishMaterials scienceMechanicsSurface roughnessAmplitudeFlow (mathematics)Lift-to-drag ratioOpticsPhysicsComposite material

Abstract

fetched live from OpenAlex

A molecular dynamics simulation of flow over two side-by-side cylinders with atomically rough surfaces is presented. The model is two-dimensional with 3×105 liquid argon atoms. The surface roughness is constructed by external protrusion of atoms on the surface of the cylinders with specified amplitude and width. Two cylinders, with diameters of d=79.44 (molecular units), are placed at a distance of D in a vertical line. The solids atoms are allowed to vibrate around their equilibrium coordinates to mimic the real solid structure. The influence of various parameters, such as roughness amplitude, topology, periodicity, and the gap between cylinders on the hydrodynamics of flow, especially drag and lift forces, is studied. It was noted that even very little surface roughness, with amplitude on the order of a few nanometers, can influence the drag forces. Both roughness texture and the number of roughening elements affects the drag and lift coefficients. The gap between the cylinders showed to be an effective parameter, especially on the lift force for flow over the nanoscale cylinders.

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 categoriesMeta-epidemiology (narrow)
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 score0.412
Threshold uncertainty score1.000

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 head0.003
GPT teacher head0.189
Teacher spread0.186 · 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.

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

Citations3
Published2006
Admission routes2
Has abstractyes

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