• français
    • English
    français
  • Login
Help
View Item 
  •   Home
  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)
  • View Item
  • Home
  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Viscous drag and rod orientation kinematics in an orthotropic fluid

Article dans une revue avec comité de lecture
Author
GILORMINI, Pierre
ccCHINESTA SORIA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/16600
DOI
10.1016/j.jnnfm.2019.07.006
Date
2019
Journal
Journal of Non-Newtonian Fluid Mechanics

Abstract

A model is proposed, where the evolution of the orientation of each fiber is coupled to the orientations of the surrounding fibers in the flow of a fiber-filled fluid and includes the effects of the fiber volume fraction and aspect ratio. This is performed by accounting for the effective behavior of the fiber-filled fluid, which is anisotropic although the fibers are embedded in an isotropic Newtonian fluid. The rotation of a fiber in these conditions is predicted by a dumbbell model, which allows an extension of Jeffery's equation to anisotropic cases. This involves the numerical evaluation of the drag force applied on a sphere in an orthotropic incompressible fluid, which is evaluated by finite element simulations. A simple fit is proposed for the practical use of the coupled model, which is applied finally to the orientation kinematics of a population of fibers in a simple shear flow, and the results are compared with the ones given by the standard uncoupled approach.

Files in this item

Name:
PIMM_JNFM_2019_GILORMINI.pdf
Size:
1.400Mb
Format:
PDF
Embargoed until:
2020-02-01
View/Open

Collections

  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)

Related items

Showing items related by title, author, creator and subject.

  • Numerical experiments on unsupervised manifold learning applied to mechanical modeling of materials and structures 
    Article dans une revue avec comité de lecture
    IBANEZ, Ruben; GILORMINI, Pierre; ccCUETO, Elias; ccCHINESTA SORIA, Francisco (Elsevier Masson, 2020)
    The present work aims at analyzing issues related to the data manifold dimensionality. The interest of the study is twofold: (i) first, when too many measurable variables are considered, manifold learning is expected to ...
  • Parametric inverse impulse response based on reduced order modeling and randomized excitations 
    Article dans une revue avec comité de lecture
    MONTAGUD, Santiago; AGUADO, José Vicente; JOYOT, Pierre; ccCHINESTA SORIA, Francisco (Elsevier, 2020)
    This paper is concerned with the computation of the inverse impulse response of a parametrized structural dynamics problem using reduced-order modeling and randomized excitations. A two-stages approach is proposed, involving ...
  • Learning the macroscopic flow model of short fiber suspensions from fine-scale simulated data 
    Article dans une revue avec comité de lecture
    YUN, Minyoung; ARGERICH MARTIN, Clara; GIORMINI, Pierre; ADVANI, Suresh G.; ccCHINESTA SORIA, Francisco (MDPI, 2020)
    Fiber-fiber interaction plays an important role in the evolution of fiber orientation in semi-concentrated suspensions. Flow induced orientation in short-fiber reinforced composites determines the anisotropic properties ...
  • Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks 
    Article dans une revue avec comité de lecture
    ccHEMBERT, Pierre; ccGHNATIOS, Chady; COTTON, Julien; ccCHINESTA SORIA, Francisco (MDPI AG, 2024-02)
    A deep geological repository for radioactive waste, such as Andra’s Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to ...
  • Foreword 
    Article dans une revue avec comité de lecture
    ccCUETO, Elias; LADEVÈZE, Pierre; ccCHINESTA SORIA, Francisco (Elsevier BV, 2019)
    No abstract

Browse

All SAMCommunities & CollectionsAuthorsIssue DateCenter / InstitutionThis CollectionAuthorsIssue DateCenter / Institution

Newsletter

Latest newsletterPrevious newsletters

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales