Post-processing methods for particle radiation Monte Carlo calculations of large-scale target objects

Hu Youtao; Fan Jieqing; Zhao Qiang; Wang Haoyang; Zhang Fang; Zhang Shuo; Cui Yaping; Hao Jianhong; Dong Zhiwei

doi:10.11884/HPLPB202537.240211

Volume 37 Issue 1

Dec. 2025

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Hu Youtao, Fan Jieqing, Zhao Qiang, et al. Post-processing methods for particle radiation Monte Carlo calculations of large-scale target objects[J]. High Power Laser and Particle Beams, 2025, 37: 016001. doi: 10.11884/HPLPB202537.240211

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Hu Youtao, Fan Jieqing, Zhao Qiang, et al. Post-processing methods for particle radiation Monte Carlo calculations of large-scale target objects[J]. High Power Laser and Particle Beams, 2025, 37: 016001. doi: 10.11884/HPLPB202537.240211

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Post-processing methods for particle radiation Monte Carlo calculations of large-scale target objects

doi: 10.11884/HPLPB202537.240211

1.
School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
2.
Institute of Applied Physics and Computational Mathematics, Beijing 100094, China

Received Date: 2024-06-25
Accepted Date: 2024-11-09
Rev Recd Date: 2024-11-09

Available Online: 2024-11-18

Publish Date: 2025-12-13

Abstract

Abstract

The Monte Carlo (MC) method is one of the most widely applied methods in the simulation study of radiation damage and radiation shielding. When conducting radiation damage studies on large targets such as airports, railways, and ships, the focus is generally on 3D modeling and radiation calculations of these targets; however, the post-calculation data analysis often relies on manual methods, making this aspect of the research technically challenging and inefficient, thus becoming a bottleneck in related research efforts. In this paper, a visualization post-processing method for MC calculations of target particle irradiation is studied, and a post-processing model based on k-dimensional tree (KDtree) + inverse distance weighting (IDW) and genetic algorithm based backpropagation (GABP) neural network is established to realize the visualization analysis of data combined with the model. Compared with traditional data analysis methods, the method proposed in this paper can greatly reduce the difficulty of researchers’ work, improve the speed of data processing, realize the visual display of radiation effects, and enhance the efficiency of post-processing in radiation effects research.
- particle irradiation,
- genetic algorithm,
- BP neural network,
- KDtree,
- inverse distance weighting,
- post-processing

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References(22)

References

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