0611 Multiphase flows with non-Newtonian materials: simulation, experiment, and machine learning

  • Anselmo Pereira, PSL Research University, Mines Paristech
  • Rudy Valette
  • Elie Hachem
  • Manuel Alves
  • Alvaro Coutinho

Multiphase flows with non-Newtonian materials are of paramount importance in many fields of science, being directly related to extremely important biomedical, environmental, and industrial situations, such as: inkjet-based bioprinting of cells, tissues and organs; hemodynamics; pesticides deposition; impact crater formation; container filling processes; breakup of complex filaments; coating; motor jets; ceramic beads and non-spherical particles production; and encapsulation processes. These different multiphase flow scenarios can be deeply affected by non-Newtonian manifestations, which may include effects like shear-thinning or shear-thickening viscosity, extensional viscosity, normal stress differences, memory behaviour, elasticity, plasticity, and thixotropy, among others. Understand and control the physical mechanisms driving such flows through simulations, experiments, and/or machine learning techniques are then crucial.

This mini symposium brings together researchers, scientists and professional users working in the field of multiphase flows involving non-Newtonian materials, favouring the exchange of ideas, experiences, new concepts, and innovative approaches devoted not only to the physical understanding of different non-Newtonian multiphase flow problems, but also to the development of different numerical, experimental and machine learning methods devoted to them.
Physical analyses of non-Newtonian multiphase flows include (but are not limited to): manufacturing flows; benchmark non-Newtonian flows; complex fluid/solid interactions; instabilities influenced by non-Newtonian manifestations in multiphase scenarios; capillary affected non-Newtonian flows; surface tension formulations in non-Newtonian contexts; modelling of complex behaviour by constitutive equations; bubble, emulsion, and suspension dynamics/rheology; mold filling; dynamic polymer extrusion and mixing.
Methods include (but are not limited to): Level set and volume-of-fluid; arbitrary-Lagrangian-Eulerian (ALE) methods; immersed boundaries; sharp and diffuse modelling of interfacial zones; deformed geometry remeshing, embedded boundary conditions; cut mesh methods; multiphase flows visualisation technique; rheometry techniques; determination of fluid material parameters; supervised, unsupervised and reinforcement learning.
We seek submissions on all aspects of these topics: theory, formulation, analysis, and applications.

Keywords: multiphase flow; non-Newtonian material; simulation; experiment; machine learning; applications; methods

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