|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|1740307||1521749||2016||7 صفحه PDF||سفارش دهید||دانلود رایگان|
• A multilayer DT-CNN model is developed for nuclear reactor dynamics simulation.
• Hardware implementation of DT-CNN model on a FPGA architecture is presented.
• FPGA architecture is examined in terms of speed and accuracy using MATLAB and C programs.
• Step and ramp perturbation transients are simulated in 2D cores using DT-CNN model.
• A speedup factor of up to 200 and 30 compare with MATLAB and C solutions is achieved.
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.
Journal: Progress in Nuclear Energy - Volume 89, May 2016, Pages 197–203