1,721,041 research outputs found
A New Approach for Polydispersed Turbulent Two-Phase Flows: The Case of Deposition in Pipe-Flows
No full textWeak first- and second-order numerical schemes for stochastic differential equations appearing in Lagrangian two-phase flow modeling
No full textThe FDF or LES/PDF method for turbulent two-phase flows
No full textIn this paper, a new formalism for the filtered density function (FDF) approach is developed for the treatment of turbulent polydispersed two-phase flows in LES simulations. Contrary to the FDF used for turbulent reactive single-phase flows, the present formalislm is based on Lagrangian quantities and, in particular, on the Lagrangian filtered mass density function (LFMDF) as the central concept. This framework allows modeling and simulation of particle flows for LES to be set in a rigorous context and various links with other approaches to be made. In particular, the relation between LES for particle simulations of single-phase flows and Smoothed Particle Hydrodynamics (SPH) is put forward. Then, the discussion and derivation of possible subgrid stochastic models used for Lagrangian models in two-phase flows can set in a clear probabilistic equivalence with the corresponding LFMDF
Compressibility, Laws of Nature, Initial Conditions and Complexity
Full text linkWe critically analyse the point of view for which laws of nature are just a mean to compress data. Discussing some basic notions of dynamical systems and information theory, we show that the idea that the analysis of large amount of data by means of an algorithm of compression is equivalent to the knowledge one can have from scientific laws, is rather naive. In particular we discuss the subtle conceptual topic of the initial conditions of phenomena which are generally incompressible. Starting from this point, we argue that laws of nature represent more than a pure compression of data, and that the availability of large amount of data, in general, is not particularly useful to understand the behaviour of complex phenomena
LES and RANS calculations of particle dispersion behind a wall-mounted cubic obstacle
Full text linkIn the present paper, we evaluate the performances of three stochastic models for particle dispersion in the case of a three-dimensional turbulent flow. We consider the flow in a channel with a cubic wall-mounted obstacle, and perform large-eddy simulations (LESs) including passive particles injected behind the obstacle, for cases of low and strong inertial effects. We also perform Reynolds-averaged simulations of the same case, using standard turbulence models, and employ the two discrete stochastic models for particle dispersion implemented in the open-source code OpenFOAM and the continuous Lagrangian stochastic model proposed by Minier et al. (2004). The Lagrangian model is consistent with a Probability Density Function (PDF) model of the exact particle equations, and is based on the modelling of the fluid velocity seen by particles. This approach allows a consistent formulation which eliminates the spurious drifts flawing discrete models and to have the drag force in a closed form. The LES results are used as reference data both for the fluid RANS simulations and particle simulations with dispersion models. The present test case allows to evaluate the performance of dispersion models in highly non-homogeneous flow, and it used in this context for the first time. The continuous stochastic model generally shows a better agreement with the LES than the discrete stochastic models, in particular in the case of particles with higher inertia
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