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The European High Performance Computing Joint Undertaking (EuroHPC JU)

Anisotropy in Turbulent Emulsions

Awarded Resources (in core hours)
MeluXina GPU
System Partition
1 July 2022 - 30 June 2023
Allocation Period

Emulsions, i.e. matching densities immiscible fluids, are extremely common in industrial and environmental applications. The interface separating the two phases modifies significantly the carrier phase, severely affecting the flow motion.

When it interacts with surrounding turbulence induced by large-scale agitation, a complex multiscale phenomenon arises, where eddies and droplets interact across all scales. Even in the simplest configuration, i.e. homogeneous and isotropic turbulence, these interactions lead to significant alterations of energy transport and to the formation of a poly-dispersed droplet size distribution. However, realistic flows are often inhomogeneous and anisotropic.

In these conditions, the flow behavior is expected to be significantly different and the implications on the current understanding of the multiphase turbulent flows are still unclear.In this framework, this project aims to understand how emulsions behave in anisotropic turbulent conditions, by using a setup known as Kolmogorov flow, where a sinusoidal body-force imposes a mean flow with large scale shear.

Different configurations will be studied, by varying volume fraction, large scale Weber number, and viscosity ratio. The flow will be characterized through global variables (e.g. kinetic energy balance) as well as spectral and physical scale-by-scale analysis, while simultaneously studying alterations of droplet size distributions induced by anisotropies.

The study will be performed by means of direct numerical simulations, using the Volume of Fluid method to account for the presence of the dispersed phase.The many configurations explored in this project will cover a vast region of the phase-space, previously unexplored and mostly unknown for anisotropic multiphase turbulent flows. Furthermore, these configurations will provide a significant understanding of the role of each property in the modulation of turbulence.

The resulting knowledge will be crucial in designing reduced-order models and for predicting the formation of droplets, ultimately contributing to improving the understanding of multiphase turbulence.