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Record W2801933899 · doi:10.1063/1.5034807

Numerical evaluation of a single ellipsoid motion in Newtonian and power-law fluids

2018· article· en· W2801933899 on OpenAlexaff
Julien Férec, Gilles Ausias, Giovanniantonio Natale

Bibliographic record

VenueAIP conference proceedings · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEllipsoidNewtonian fluidCentroidMechanicsPhysicsShear flowSimple shearPower lawClassical mechanicsNon-Newtonian fluidGeneralized Newtonian fluidFinite element methodShear rateMathematicsRheologyGeometryShear stressThermodynamics

Abstract

fetched live from OpenAlex

A computational model is developed for simulating the motion of a single ellipsoid suspended in a Newtonian and power-law fluid, respectively. Based on a finite element method (FEM), the approach consists in seeking solutions for the linear and angular particle velocities using a minimization algorithm, such that the net hydrodynamic force and torque acting on the ellipsoid are zero. For a Newtonian fluid subjected to a simple shear flow, the Jeffery’s predictions are recovered at any aspect ratios. The motion of a single ellipsoidal fiber is found to be slightly disturbed by the shear-thinning character of the suspending fluid, when compared with the Jeffery’s solutions. Surprisingly, the perturbation can be completely neglected for a particle with a large aspect ratio. Furthermore, the particle centroid is also found to translate with the same linear velocity as the undisturbed simple shear flow evaluated at particle centroid. This is confirmed by recent works based on experimental investigations and modeling approach (1-2).

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 score0.960
Threshold uncertainty score0.460

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 head0.026
GPT teacher head0.261
Teacher spread0.234 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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