Convergence to a model in sparse-Lagrangian FDF simulations
نویسنده
چکیده
This work investigates the problem of distinguishing modelling assumptions and numerical errors in sparseLagrangian FDF (Filtered Density Function) methods. A new interpretation of sparse modelling with Curl’s mixing, which does not require an additional observation scale nor filtering, is given. The diffusion effects induced by mixing, which were previously interpreted as numerical errors, are now treated as modelling instruments. This ability of controlling numerical errors with the purpose of modelling physical quantities is one of the advantages of Lagrangian particle methods in turbulent reacting flows. The development of stochastic methods which use Lagrangian particles has been ongoing for many years, although the exact interpretation of the nature of such particles varies within the literature. Here we briefly discuss these interpretations and introduce the new term – “Pope particles” – to unify terminology used for the particle simulations of turbulent reacting flows.
منابع مشابه
Lagrangian particles with mixing. II. Sparse-Lagrangian methods in application for turbulent reacting flows
Both parts of this work present a more detailed and specific analysis of ideas introduced in the previously published letter Phys. Fluids 19, 031702 2007 . In Paper I Phys. Fluids 19, 065101 2009 , we show that the continuous scalar transport and diffusion can be accurately specified by means of mixing between randomly walking Lagrangian particles with scalar properties. Here, in Paper II, we d...
متن کاملOn simulating scalar transport by mixing between Lagrangian particles
Lagrangian particles with mixing can be used as direct numerical simulations DNS , large eddy simulations LES , or filtered density function FDF methods depending on conditions of the simulations. We estimate major parameters associated with the DNS, LES, and FDF regimes and demonstrate that, under certain conditions specified in the paper, simulations using different mixing models approach the...
متن کاملRobust Sparse Recovery in Impulsive Noise via ℓp-ℓ1 Optimization
This paper addresses the issue of robust sparse recovery in compressive sensing (CS) in the presence of impulsive measurement noise. Recently, robust data-fitting models, such as 1 -norm, Lorentzian-norm, and Huber penalty function, have been employed to replace the popular 2 -norm loss model to gain more robust performance. In this paper, we propose a robust formulation for sparse recovery usi...
متن کاملCoupled Eulerian-Lagrangian (CEL) Modeling of Material Flow in Dissimilar Friction Stir Welding of Aluminum Alloys
In this work, the finite element simulation of dissimilar friction stir welding process is investigated. The welded materials are AA 6061-T6 and AA 7075-T6 aluminum alloys. For this purpose, a 3D coupled thermo-mechanical finite element model is developed according to the Coupled Eulerian-Lagrangian (CEL) method. The CEL method has the advantages of both Lagrangian and Eulerian approaches, whic...
متن کاملA Lagrangian Decomposition Algorithm for Robust Green Transportation Location Problem
In this paper, a green transportation location problem is considered with uncertain demand parameter. Increasing robustness influences the number of trucks for sending goods and products, caused consequently, increase the air pollution. In this paper, two green approaches are introduced which demand is the main uncertain parameter in both. These approaches are addressed to provide a trade-off b...
متن کامل