Solving Navier-Stokes Equation Using FPGA Cellular Neural Network Chip

This paper presents a method of using Cellular Neural Network (CNN) for solving hydraulic Navier-Stokes equations, which are a set of three partial differential equations with 3 functional variables and each function has three variables for time and space. The paper has 5 parts: part 1 is the introduction; Part 2 discusses the Navier-Stokes for flow through narrow slots; Part 3 analyzes and finds templates for designing CNN architecture; Part 4 provides some solutions for optimizing resources and speeding up computing; Part 5 sets up CNN chip for implementing and computing results; the last part is conclusion and developing trends.