Dispersed Two-Phase Flows TEST

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Objectives

The objective of the conference is to bring together researchers from different communities (academics and researchers from industrial research institutes in fluid mechanics, chemical engineering, …) working on fundamental problems involving dispersed flows.

Contributions of work in progress are welcome. This conference is international and will be held in english.

It will last three days (July 8-10th, 2024) and will consist in selected oral presentations (~15 to 20 minutes) and poster sessions, with several thematic sessions in series. Extra time outside the formal session will be dedicated to discussions between participants.

The event will be exclusively held in English / Cet évènement sera organisé entièrement en anglais.

Event presentation

In many industrial or environmental situations, particles, drops or bubbles are dispersed in a carrier fluid. Understanding and modeling dispersed flows is therefore a major issue for many applications including chemical engineering (bubble columns, water treatment, fluidized beds, oil refining), nuclear industry (boiling in steam generators, containment spray systems), environmental engineering (sediment transport, coastal erosion, river restauration), geophysics (volcanic processes, fluid migration in sedimentary basins), astrophysics (protoplanetary dust, planet formation) and combustion applications (atomization, spray combustion).

Experimental, numerical and theoretical studies will be presented on the following topics:

Dynamics and transfer around isolated particles
Interfacial dynamics (deformation, coalescence and rupture)
Hydrodynamics of dispersed flows (turbulence, dispersion, two-way coupling)
Mixing, transfers and phase-change in dispersed flows
Transport in dispersed flows at high volume fraction
Complex dispersed flows: density/viscosity stratification, granular & non-Newtonian flows
Development of experimental methods
Development of numerical methods
Multiscale, multiphysics modeling
Deep Learning for Multiphase Flow (Physics Informed Neural Networks, … etc)