Point-Particle DNS and LES of Particle-Laden Turbulent flow - a state-of-the-art review

Abstract

Particle-laden or droplet-laden turbulent flows occur in many industrial applications and in natural phenomena. Knowledge about the properties of these flows can help to improve the design of unit operations in industry and to predict for instance the occurrence of rain showers. This knowledge can be obtained from experimental research and from numerical simulations. In this paper a review is given of numerical simulation methods for particle-laden flows. There are various simulation methods possible. They range from methods in which all details, including the flow around each particle, are resolved, via point-particle methods, in which for each particle an equation of motion is solved, to Eulerian methods in which equations for particle concentration and velocity are solved. This review puts the emphasis on the intermediate class of methods, the Euler-Lagrange methods in which the continuous phase is described by an Eulerian approach and the dispersed phase in a Lagrangian way with equations of motion for each individual particle.

Large-eddy simulation of turbulent dispersed flows: a review of modelling approaches

“…In fact, several studies have shown that subgrid fluid velocities must be taken into account to provide reliable calculations of particle kinetic properties and/or preferential concentration phenomena [61,83]. The first formulation of particle SGS model accounting explicitly for u s in Eq.…”

“…These models have been widely employed to predict particle deposition and resuspension, especially in the context of Reynoldsaveraged formulations for industrial applications [83,85,102]. Over the last decade, however, growing efforts have been devoted to the extension of stochastic closures to LES with subgrid modelling [60,61,[82][83][84][85]. From an historical perspective, stochastic models were initially developed for free-shear flows in the context of environmental fluid mechanics [98], and closures were typically formulated for the fluid velocity seen by the particles along their trajectory (referred to as velocity seen hereinafter).…”

“…Turbulent dispersed flows are also very rich in physics, as they are characterized by a complex multi-scale interaction between the fluid turbulence and the particle distribution that still poses many challenging issues in terms of modelling, simulation, and experiments. A general overview of these issues is provided by a number of review papers [9,49,61,144], to which the reader is referred for a comprehensive survey of the state of the art in the field.…”

Near-wall dynamics of inertial particles in dilute turbulent channel flows

“…Over the last few decades, the motion of particles in isotropic and wall-bounded turbulence has been simulated to study a range of behaviours and mechanisms such as dispersion, deposition, resuspension, turbulence enhancement/attenuation and interparticle collisions. Typical attempts at performing such computations use tools such as direct numerical simulation (DNS) or large eddy simulation (LES) to predict the turbulence field, however LES is less adequate for dense flows since the two-way coupling influence on turbulence kinetic energy occurs at the unresolved scales, and so further considerations must be taken into account to model this feature (M. Kuerten, 2016). The particulate phase is usually predicted concurrently with the continuous phase using one of a range of techniques such as Eulerian methods (Février et al, 2005), point-particle approaches (Maxey and Riley, 1983) and particleresolved or interface tracking methods (Vreman, 2016).…”

Fractal Reconstruction of Sub-Grid Scales for Large Eddy Simulation

“…Lack of sub-grid scales or sub-grid scale model errors will lead to the progressive divergence of particle trajectories when compared with those obtained in experiment or DNS [2,3]. As a result, particle statistics such as preferential concentration, average settling velocity and relative velocities are either over-or under-estimated [4][5][6][7][8][9][10].…”