Supersonic flow simulation on IBM cell processor based emulated digital Cellular Neural Networks

In the area of mechanical, aerospace, chemical and civil engineering the solution of partial differential equations (PDEs) has been one of the most important problems of mathematics for a long time. In this field, one of the most exciting areas is the simulation of fluid flow, which involves for example problems of air, sea and land vehicle motion. In engineering applications the temporal evolution of non-ideal, compressible fluids is quite often modeled by the system of Navier-Stokes equations. They are a coupled set of nonlinear hyperbolic partial differential equations and form a relatively simple, yet efficient model of compressible fluid dynamics. Unfortunately the necessity of the coupled multi-layered computational structure with nonlinear, space-variant templates does not make it possible to utilize the huge computing power of the analog cellular neural network universal machine (CNN-UM) chips. To improve the performance of our solution emulated digital CNN-UM implemented on IBM cell broadband engine has been used. The goal is to perform the operations with the highest possible parallelism.