Publication

Grid Filter Modeling for Large-Eddy Simulation

Michel Deville
2010
Conference Papers

Abstract

An interpretation to the use of deconvolution models when used in implicitly filtered large-eddy simulations as a way to approximate the projective grid filter is given. Consequently, a new category of subgrid models, the grid filter models, is defined. This approach gives a theoretical justification to the use of deconvolution models without explicit filtering of the solution and explains how the use of such models can be effective in this context.

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Michel Deville
2010
Conference Papers

Abstract

Official source

Related concepts (13)

Large eddy simulation

Large eddy simulation (LES) is a mathematical model for turbulence used in computational fluid dynamics. It was initially proposed in 1963 by Joseph Smagorinsky to simulate atmospheric air currents, and first explored by Deardorff (1970). LES is currently applied in a wide variety of engineering applications, including combustion, acoustics, and simulations of the atmospheric boundary layer. The simulation of turbulent flows by numerically solving the Navier–Stokes equations requires resolving a very wide range of time and length scales, all of which affect the flow field.

Particle filter

Particle filters, or sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to find approximate solutions for filtering problems for nonlinear state-space systems, such as signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made and random perturbations are present in the sensors as well as in the dynamical system.

Ensemble Kalman filter

The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component of ensemble forecasting.

Related publications (1)

Grid Filter Modeling for Large-Eddy Simulation

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Advances in Dynamical Low-Rank Approximations for random PDEs and Data Assimilation