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Record W4392125085 · doi:10.1063/5.0189379

Three-dimensional numerical investigation of a suspension flow in an eccentric Couette flow geometry

2024· article· en· W4392125085 on OpenAlexaff
Ayoub Badia, Enzo d’Ambrosio, Yves D’Angelo, François Peters, Laurent Lobry

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversité de Montréal
FundersAgence Nationale de la Recherche
KeywordsEccentricity (behavior)Couette flowPhysicsMechanicsNewtonian fluidTaylor–Couette flowNon-Newtonian fluidFlow (mathematics)Classical mechanicsSuspension (topology)GeometryMathematics

Abstract

fetched live from OpenAlex

This paper investigates the influence of eccentricity on flow characteristics and particle migration in Couette geometries. The study involves numerical simulations using the recent frame-invariant model developed by Badia et al. [J. Non-Newtonian Fluid Mech. 309, 104904 (2022)]. The study begins with a two-dimensional analysis, focusing first on the Newtonian fluid in order to thoroughly characterize the specific properties of this flow configuration. Next, the impact of eccentricity on particle migration in an isodense suspension is examined by numerical simulations based on the experiments conducted by Subia et al. [J. Fluid Mech. 373, 193–219 (1998)]. Furthermore, the study is extended to include a full three-dimensional analysis of a dense suspension flow in an eccentric Couette geometry based on resuspension experiments conducted by Saint-Michel et al. [Phys. Fluids 31, 103301 (2019)] and D'Ambrosio et al.[J. Fluid Mech. 911, A22 (2021)]. The main objective of the latter study is to investigate the influence of eccentricity on the resuspension height and on the calculation of the particle normal stress in the vertical direction through the volume fraction profile analysis. Our results show that even minimal eccentricity can lead to significant changes compared to the centered case.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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