Optimal Prediction of Burgers's Equation

We examine an application of the optimal prediction framework to the truncated Fourier–Galerkin approximation of Burgers’s equation. Under particular conditions on the density of the modes and the length of the memory kernel, optimal prediction introduces an additional term to the Fourier–Galerkin approximation which represents the influence of an arbitrary number of small wavelength unresolved modes on the long wavelength resolved modes. The modified system, called the t‐model by previous authors, takes the form of a time‐dependent cubic term added to the original quadratic system. Numerical experiments show that this additional term restores qualitative features of the solution in the case where the number of modes is insufficient to resolve the resulting shocks (i.e., zero or very small viscosity) and for which the original Fourier–Galerkin approximation is very poor. In particular, numerical examples are shown in which the kinetic energy decays in the same manner as in the exact solution, i.e., as $t^...

[1]  A. Pouquet,et al.  Hyperviscosity for compressible flows using spectral methods , 1988 .

[2]  Alexandre J Chorin,et al.  Viscosity-dependent inertial spectra of the Burgers and Korteweg-deVries-Burgers equations. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[3]  B. Alder,et al.  Velocity Autocorrelations for Hard Spheres , 1967 .

[4]  C. Meneveau,et al.  Scale-Invariance and Turbulence Models for Large-Eddy Simulation , 2000 .

[5]  P. Sagaut Large Eddy Simulation for Incompressible Flows , 2001 .

[6]  S. Motamen,et al.  Nonlinear diffusion equation , 2002 .

[7]  M. Lesieur,et al.  New Trends in Large-Eddy Simulations of Turbulence , 1996 .

[8]  George W. Platzman,et al.  A table of solutions of the one-dimensional Burgers equation , 1972 .

[9]  A J Majda,et al.  Remarkable statistical behavior for truncated Burgers-Hopf dynamics. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[10]  J. Barber,et al.  Application of Optimal Prediction to Molecular Dynamics , 2004 .

[11]  A. Chorin,et al.  Stochastic Tools in Mathematics and Science , 2005 .

[12]  Alexandre J. Chorin,et al.  Optimal prediction with memory , 2002 .

[13]  Alexandre J. Chorin,et al.  Problem reduction, renormalization, and memory , 2005 .

[14]  Panagiotis Stinis Stochastic Optimal Prediction for the Kuramoto-Sivashinsky Equation , 2004, Multiscale Model. Simul..

[15]  E. Tadmor,et al.  Convergence of spectral methods for nonlinear conservation laws. Final report , 1989 .

[16]  Dror Givon,et al.  Existence proof for orthogonal dynamics and the Mori-Zwanzig formalism , 2005 .

[17]  B. Alder,et al.  Decay of the Velocity Autocorrelation Function , 1970 .

[18]  Weizhang Huang,et al.  Moving Mesh Methods Based on Moving Mesh Partial Differential Equations , 1994 .

[19]  P. Lax Hyperbolic Systems of Conservation Laws and the Mathematical Theory of Shock Waves , 1987 .