GPU Based General-Purpose Parallel Computing to Solve Nuclear Reactor In-Core Fuel Management Design and Operation Problem

摘要

In-core fuel management study is a crucial activity in nuclear power plant design and operation. Its common problem is to find an optimum arrangement of fuel assemblies inside the reactor core. Main objective for this activity is to reduce the cost of generating electricity, which can be done by altering several physical properties of the nuclear reactor without violating any of the constraints imposed by operational and safety considerations. This research try to address the problem of nuclear fuel arrangement problem, which is, leads to the multi-objective optimization problem. However, the calculation of the reactor core physical properties itself is a heavy computation, which became obstacle in solving the optimization problem by using genetic algorithm optimization. This research tends to address that problem by using the emerging General Purpose Computation on Graphics Processing Units (GPGPU) techniques implemented by C language for 'CUDA (Compute Unified Device Architecture) parallel programming. By using this parallel programming technique, we develop parallelized nuclear reactor fitness calculation, which is involving numerical finite difference computation. This paper describes current prototype of the parallel algorithm code we have developed on CUDA, that performs one hundreds finite difference calculation for nuclear reactor fitness' evaluation in parallel by using. GPU G9 Hardware Series developed by NVIDIA.