Title :
A double time-scale CNN for solving 2-D Navier-Stokes equations
Author :
Kozek, T. ; Roska, T.
Author_Institution :
Comput. & Autom. Inst., Hungarian Acad. of Sci., Budapest, Hungary
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
Keywords :
Navier-Stokes equations; cellular neural nets; finite difference methods; physics computing; 2-D Navier-Stokes equations; CNN templates; boundary conditions; cellular neural network approximation; continuous time model; double time-scale CNN; double time-scale CNN dynamics; finite difference discretization scheme; incompressible fluids; viscous flow; Analog computers; Boundary conditions; Cellular neural networks; Computer networks; Laplace equations; Navier-Stokes equations; Neural networks; Poisson equations; Space stations; Steady-state;
Conference_Titel :
Cellular Neural Networks and their Applications, 1994. CNNA-94., Proceedings of the Third IEEE International Workshop on
Conference_Location :
Rome
Print_ISBN :
0-7803-2070-0
DOI :
10.1109/CNNA.1994.381668
