Improved adaptive variance reduction algorithm based on RMC code for deep penetration problems

Pan, Qingquan; Rao, Junjie; Huang, Shanfang; Wang, Kan

doi:10.1016/j.anucene.2019.107113

Cited by 16 publications

(1 citation statement)

References 9 publications

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“…However, it still has great challenges in engineering application. For instance, it is difficult to construct the hyperfine model which has complex structure and material composition [8], to improve the low counting efficiency in deep penetration problem [9], and to simplify the timedependent particle transport process [10].…”

Section: Introductionmentioning

confidence: 99%

Development and Application of Particle Transport Monte Carlo Simulation Modeling Toolkit

Shang

Huang

Zhu

et al. 2023

J. Phys.: Conf. Ser.

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In particle transport Monte Carlo simulation, the manual modeling method is not suitable for complex geometric models. A Monte Carlo Simulation Modeling Toolkit (pyMCMT) is developed based on Python language. The toolkit can generate input card in several classical format such as MCNP and GDML format automatically. In this paper, two typical applications are presented based on the pyMCMT toolkit: 1, constructing a multilayer circular geometric model in complex density distribution by pyMCMT toolkit in order to satisfy the requirement of building large-space fine model with the influence of blast wave. A two-dimensional adaptive model is built for non-uniform atmospheric induced by blast wave. The number of the cells in this model is reduced vastly with the same model resolution; 2, producing the optimized weight window parameter using the mesh-based weight window variance reduction combined with density iteration method which is realized to solve the high statistical fluctuation and low counting efficiency problem in deep penetration process. The successful application of pyMCMT shows that pyMCMT is a modularized, parameterized, customizable and easy-to-debug toolkit for particle transport Monte Carlo simulation modeling, which can improve the modeling efficiency tremendously.

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Section: Introductionmentioning

confidence: 99%