摘要
It can be difficult to calculate some under-sampled regions in global Monte Carlo radiation transport calculations. The global variance reduction(GVR) method is a useful solution to the problem of variance reduction everywhere in a phase space. In this research, a GVR procedure was developed and applied to the Chinese Fusion Engineering Testing Reactor(CFETR). A cylindrical CFETR model was utilized for comparing various implementations of the GVR method to find the optimum.It was found that the flux-based GVR method could ensure more reliable statistical results, achieving an efficiency being 7.43 times that of the analog case. A mesh tally of the scalar neutron flux was chosen for the GVR method to simulate global neutron transport in the CFETR model.Particles distributed uniformly in the system were sampled adequately through ten iterations of GVR weight window.All voxels were scored, and the average relative error was 2.4% in the ultimate step of the GVR iteration.
It can be difficult to calculate some under-sam- pled regions in global Monte Carlo radiation transport calculations. The global variance reduction （GVR） method is a useful solution to the problem of variance reduction everywhere in a phase space. In this research, a GVR procedure was developed and applied to the Chinese Fusion Engineering Testing Reactor （CFETR）. A cylin- drical CFETR model was utilized for comparing various implementations of the GVR method to find the optimum. It was found that the flux-based GVR method could ensure more reliable statistical results, achieving an efficiency being 7.43 times that of the analog case. A mesh tally of the scalar neutron flux was chosen for the GVR method to simulate global neutron transport in the CFETR model. Particles distributed uniformly in the system were sampled adequately through ten iterations of GVR weight window. All voxels were scored, and the average relative error was 2.4% in the ultimate step of the GVR iteration.
基金
supported by the National Special Project for Magnetic Confined Nuclear Fusion Energy(Nos.2013GB108004 and2015GB108002)
the Chinese National Natural Science Foundation(No.11175207)
