The spatial distribution of neutron flux within the core of a nuclear reactor plays a crucial role in ensuring nuclear safety. It can be determined by solving the neutron diffusion equation, where computational resource consumption is gradually receiving more attention. This paper employs a nonlinear iterative method (NIM) as the iterative strategy and uses a novel boundary-type algorithm called the half-boundary method (HBM) to calculate the coupling correction factors. By establishing an equation similar to coarse mesh finite differences (CMFD), the number of unknowns and memory requirements are significantly reduced, effectively improving computational efficiency. Compared to traditional methods such as the nodal method (NM) or finite difference method (FDM), the coupling factors calculated using the half-boundary method can meet higher accuracy requirements. The method is verified using several problems, including the single, multigroup eigenvalue problems with different boundaries. All these problems have proved good results.

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A Boundary-Type Algorithm Based on Nonlinear Iteration for Neutron Diffusion Equation

  • Yan Ting Cheng,
  • Mei Huang,
  • Xiao Ping Ouyang

摘要

The spatial distribution of neutron flux within the core of a nuclear reactor plays a crucial role in ensuring nuclear safety. It can be determined by solving the neutron diffusion equation, where computational resource consumption is gradually receiving more attention. This paper employs a nonlinear iterative method (NIM) as the iterative strategy and uses a novel boundary-type algorithm called the half-boundary method (HBM) to calculate the coupling correction factors. By establishing an equation similar to coarse mesh finite differences (CMFD), the number of unknowns and memory requirements are significantly reduced, effectively improving computational efficiency. Compared to traditional methods such as the nodal method (NM) or finite difference method (FDM), the coupling factors calculated using the half-boundary method can meet higher accuracy requirements. The method is verified using several problems, including the single, multigroup eigenvalue problems with different boundaries. All these problems have proved good results.