A double time-scale CNN for solving 2-D Navier-Stokes equations

Abstract

A practical cellular neural network (CNN) approximation to the Navier Stokes equation describing viscous flow of incompressible fluids is presented. The implementation of the CNN templates based on a finite difference discretization scheme, including the double time-scale CNN dynamics and the treatment of various types of boundary conditions are explained. The operation of the continuous time model is demonstrated through several examples.<>