Computational fluid dynamics (CFD) is the scientific modeling of the temporal evolution of gas and fluid flows by exploiting the enormous processing power of computer technology. Simulation of fluid flow over complex-shaped objects currently requires several weeks of computing time on high-performance supercomputers. A CNN-UM-based solver of 2D inviscid, adiabatic, and compressible fluids will be presented. The governing partial differential equations (PDEs) are solved by using first- and second-order numerical methods. Unfortunately, the necessity of the coupled multilayered computational structure with nonlinear, space-variant templates does not make it possible to utilize the huge computing power of the analog CNN-UM chips. To improve the performance of our solution, emulated digital CNN-UM implemented on FPGA has been used. Properties of the implemented specialized architecture is examined in terms of area, speed, and accuracy.
ASJC Scopus subject areas
- Signal Processing
- Information Systems
- Hardware and Architecture
- Electrical and Electronic Engineering