Issue |
EPJ Nuclear Sci. Technol.
Volume 11, 2025
Status and advances of Monte Carlo codes for particle transport simulation
|
|
---|---|---|
Article Number | 4 | |
Number of page(s) | 15 | |
DOI | https://doi.org/10.1051/epjn/2024032 | |
Published online | 24 January 2025 |
https://doi.org/10.1051/epjn/2024032
Regular Article
Optimization progress of large-scale radiation shielding Monte Carlo simulation software based on AIS variance reduction technique system: MCShield
1
Department of Engineering Physics, Tsinghua University, Beijing, 100084, C.R. China
2
Key Laboratory of Particle and Radiation Imaging of Ministry of Education, Beijing, 100084, C.R. China
3
Nuctech Company Limited, Beijing, 100084, C.R. China
* e-mail: qiurui@tsinghua.edu.cn
Received:
11
June
2024
Received in final form:
8
November
2024
Accepted:
2
December
2024
Published online: 24 January 2025
The development of novel nuclear facilities has brought safer and more efficient energy options but also poses significant challenges for radiation shielding calculations. MCShield, developed by the Radiation Protection and Environmental Protection Laboratory at Tsinghua University, is a Monte Carlo program designed for coupled neutron/photon/electron transport in radiation shielding calculations. It incorporates a system of variance reduction techniques based on Auto-Importance Sampling (AIS) to address the deep penetration problem commonly encountered in the field of radiation shielding. The accuracy and computational efficiency of MCShield have been validated through benchmark problems and real-world applications. However, the current AIS system faces limitations in complex scenarios, user-friendliness, and reliance on user experience. To address these issues, we optimized the size, shape, and energy parameters for the AIS variance reduction method, expanded the use of regular virtual surfaces, and introduced an irregular AIS virtual surface method. Additionally, we developed an automatic generation method for AIS virtual surfaces and implemented automatic calculation for these surfaces. AIS Energy Bias Method was proposed to improve convergence across different energy intervals. These improvements enhance the applicability and refinement of the AIS virtual surface parameters, significantly boosting the overall performance of MCShield.
© J. Wu, Published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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