This paper describes the application of a multilayer discrete-time cellular neural network (DT-CNN1
) and
its hardware implementation on a field programmable gate array (FPGA2
) to model and simulate the
nuclear reactor dynamics equations. A new computing architecture model based on FPGA and its detailed
hardware implementation are proposed for accelerating the solution of nuclear reactor dynamics
equations. The proposed FPGA-based reconfigurable computing platform is implemented on a Xilinx
FPGA device and is utilized to simulate step and ramp perturbation transients in typical 2D nuclear
reactor cores. Properties of the implemented specialized architecture are examined in terms of speed and
accuracy against the numerical solution of the nuclear reactor dynamics equations using MATLAB and C
programs. Steady state as well as transient simulations, show a very good comparison between the two
methods. Also, the validity of the synthesized architecture as a hardware accelerator is demonstrated.