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Record W1993217142 · doi:10.1017/s0022112010006403

Drag and lift forces on random assemblies of wall-attached spheres in low-Reynolds-number shear flow

2011· article· en· W1993217142 on OpenAlexaff
J.J. Derksen, R. A. LARSEN

Bibliographic record

VenueJournal of Fluid Mechanics · 2011
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDragSPHERESReynolds numberMechanicsLift (data mining)Lattice Boltzmann methodsDrag coefficientPhysicsDrag equationClassical mechanicsLift-to-drag ratioShear flowMaterials scienceTurbulenceDrag divergence Mach number

Abstract

fetched live from OpenAlex

Direct numerical simulations of the shear flow over assemblies of uniformly sized, solid spheres attached to a flat wall have been performed using the lattice-Boltzmann method. The random sphere assemblies comprised monolayers, double layers and triple layers. The Reynolds number based on the sphere radius and the overall shear rate was much smaller than 1. The results were interpreted in terms of the drag force (the force in the streamwise direction) and lift force (the force in the wall-normal direction) experienced by the spheres as a function of the denseness of the bed and the depth of the spheres in the bed. The average drag and lift forces decay monotonically as a function of the surface coverage of the spheres in the top layer of the bed. The sphere-to-sphere variation of the drag and lift forces is significant due to interactions between spheres via the interstitial fluid flow.

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)0.001
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.247
Teacher spread0.225 · 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

Citations43
Published2011
Admission routes1
Has abstractyes

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