Development and Application of Particle Transport Monte Carlo Simulation Modeling Toolkit

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 forma...

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Bibliographic Details
Published in:Journal of physics. Conference series 2023-05, Vol.2508 (1), p.012040
Main Authors: Shang, Peng, Huang, Liu-xing, Zhu, Jin-hui, Niu, Sheng-li, Liu, Li
Format: Article
Language:English
Subjects:
Atmospheric models
Density distribution
Finite element method
Format
Iterative methods
Monte Carlo simulation
Multilayers
Physics
Programming languages
Simulation
Toolkits
Two dimensional models
Weight reduction
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2508/1/012040