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A model accounting for fluidisation by pore gas pressure in dense granular flows is presented. A viscoplastic rheology, based on the Drucker-Prager criterium, is used to describe the granular medium which is a mixture of air and glass beads. The pore gas pressure, which satisfies an advection-diffusion equation, reduces the friction between the particles and thus the value of the apparent viscosity. As a consequence, dense fluidised granular flows can travel longer distances. In laboratory experiments, the run-out distance reached by dense granular columns when collapsing is almost doubled when fluidisation is applied. This fundamental result, in the context of pyroclastic density currents, is reproduced by numerical simulations performed with the fluidised model.

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The accurate prediction of the curvature of fluid-fluid interfaces is crucial for appropriately modeling the surface forces when computing two-phase flows with immiscible fluids. The volume of fluid (VOF) method is often used for these computations to specify the different fluids and the interface by the so-called volume fraction field. In this study, a deep artificial neural network is trained to predict the interface curvature from the volume fraction values. This approach is investigated within an algebraic VOF framework. A rudimentary interface resharpening algorithm is introduced for the input stencils to enhance the accuracy and robustness when the interface can not be captured entirely sharp. The performance of different neural network architectures is evaluated by generic test data and the computation of two oscillating droplet flow configurations.

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Powder-based additive manufacturing technologies, specifically selective laser melting, are challenging to model due to the complex, interrelated physical phenomena that are present on multiple spatial scales, during the process. A key element of such models will be the detailed simulation of flow and heat transfer throughout the melt pool that is formed when the powder particles melt. Due to the high-temperature gradients that are generated inside the melt pool, the Marangoni force plays a key role in governing the flows inside the melt pool and deciding its shape and dimensions. On the other hand, the mass and heat transfer between the melt and the powder also has a significant role in shaping the melt pool at the edges. In this study, we modified an OpenFOAM solver (icoReactingMultiphaseInterFoam) coupled with an in-house developed DEM code known as eXtended Discrete Element Method or XDEM which models the dynamics and thermodynamics of the particles. By adding the Marangoni force to the momentum equation and also defining a laser model as a boundary condition for liquid-gas interface, the solver is capable of modeling the selective laser melting process from the moment of particle melting to the completion of the solidified track. The coupled solver was validated with an ice packed bed melting case and was used to simulate a multi-track selective laser melting process.

Abstract

Discrete versions of Poisson's equation with large contrasts in the coefficients result in very ill-conditioned systems. Thus, its iterative solution represents a major challenge, for instance, in porous media and multiphase flow simulations, where considerable permeability and density ratios are usually found. The existing strategies trying to remedy this are highly dependent on whether the coefficient matrix remains constant at each time iteration or not. In this regard, incompressible multiphase flows with high-density ratios are particularly demanding as their resulting Poisson equation varies along with the density field, making the reconstruction of complex preconditioners impractical. This work presents a strategy for solving such versions of the variable Poisson equation.Roughly, we first make it constant through an adequate approximation. Then, we block-diagonalise it through an inexpensive change of basis that takes advantage of mesh reflection symmetries, which are common in multiphase flows. Finally, we solve the resulting set of fully decoupled subsystems with virtually any solver. The numerical experiments conducted on a multiphase flow simulation prove the benefits of such an approach, resulting in up to 6.6x faster convergences.