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Hybrid windowed networks for on-the-fly doppler broadening in RMC code

Nuclear Science and Techniques 2021年第6期32卷 70-82页

作者： Tian-Yi Huang Ze-Guang Li Kan Wang Xiao-Yu Guo Jin-Gang Liang Institute of Nuclear and New Energy Technology Tsinghua UniversityBeijing 100084China Department of Engineering Physics Tsinghua UniversityBeijing 100084China

on-the-fly doppler broadening of cross sections is important in Monte Carlo simulations,particularly in Monte Carlo neutronics-thermal hydraulics coupling *** such as Target Motion Sampling(TMS)and windowed multipole as well as a method based on regression models have been developed to solve this ***,these methods have limitations such as the need for a cross section in an ACE format at a given temperature or a limited application energy *** this study,a new on-the-fly doppler broadening method based on a Back Propagation(BP)neural network,called hybrid windowed networks(HWN),is proposed to resolve the resonance energy *** the HWN method,the resolved resonance energy range is divided into windows to guarantee an even distribution of resonance *** networks with specially designed structures and training parameters are trained to evaluate the cross section at a base temperature and the broadening *** HWN method is implemented in the Reactor Monte Carlo(RMC)code,and the microscopic cross sections and macroscopic results are *** results show that the HWN method can reduce the memory requirement for cross-sectional data by approximately 65%;moreover,it can generate keff,power distribution,and energy spectrum results with acceptable accuracy and a limited increase in the calculation *** feasibility and effectiveness of the proposed HWN method are thus demonstrated.

关键词： Monte Carlo method Reactor Monte Carlo(RMC) on-the-fly doppler broadening BP network

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