6 research outputs found
Genuinely nonlinear models for convection-dominated problems
AbstractThis paper introduces a general, nonlinear subgrid-scale (SGS) model, having boundedartificial viscosity, for the numerical simulation of convection-dominated problems. We also present a numerical comparison (error analysis and numerical experiments) between this model and the most common SGS model of Smagorinsky, which uses a p-Laplacian regularization. The numerical experiments for the 2-D convection-dominated convection-diffusion test problem show a clear improvement in solution quality for the new SGS model. This improvement is consistent with the bounded amount of artificial viscosity introduced by the new SGS model in the sharp transition regions
Variable Time Step Method of DAHLQUIST, LINIGER and NEVANLINNA (DLN) for a Corrected Smagorinsky Model
Turbulent flows strain resources, both memory and CPU speed. The DLN method
has greater accuracy and allows larger time steps, requiring less memory and
fewer FLOPS. The DLN method can also be implemented adaptively. The classical
Smagorinsky model, as an effective way to approximate a (resolved) mean
velocity, has recently been corrected to represent a flow of energy from
unresolved fluctuations to the (resolved) mean velocity. In this paper, we
apply a family of second-order, G-stable time-stepping methods proposed by
Dahlquist, Liniger, and Nevanlinna (the DLN method) to one corrected
Smagorinsky model and provide the detailed numerical analysis of the stability
and consistency. We prove that the numerical solutions under any arbitrary time
step sequences are unconditionally stable in the long term and converge at
second order. We also provide error estimate under certain time step condition.
Numerical tests are given to confirm the rate of convergence and also to show
that the adaptive DLN algorithm helps to control numerical dissipation so that
backscatter is visible