Study on Monte Carlo Particle Transport Method and Application

Deng Li; Li Gang; Zhang Baoyin; Li Rui; Zhang Lingyu; Fu Yuanguang; Liu Peng; Ma Yan; Shi Dunfu; Wang Xin; Qin Guiming

doi:10.13832/j.jnpe.2025.02.0062

Volume 46 Issue 3

Jun. 2025

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Deng Li, Li Gang, Zhang Baoyin, Li Rui, Zhang Lingyu, Fu Yuanguang, Liu Peng, Ma Yan, Shi Dunfu, Wang Xin, Qin Guiming. Study on Monte Carlo Particle Transport Method and Application[J]. Nuclear Power Engineering, 2025, 46(3): 1-17. doi: 10.13832/j.jnpe.2025.02.0062

Citation:

Deng Li, Li Gang, Zhang Baoyin, Li Rui, Zhang Lingyu, Fu Yuanguang, Liu Peng, Ma Yan, Shi Dunfu, Wang Xin, Qin Guiming. Study on Monte Carlo Particle Transport Method and Application[J]. Nuclear Power Engineering, 2025, 46(3): 1-17. doi: 10.13832/j.jnpe.2025.02.0062

Citation:

Deng Li, Li Gang, Zhang Baoyin, Li Rui, Zhang Lingyu, Fu Yuanguang, Liu Peng, Ma Yan, Shi Dunfu, Wang Xin, Qin Guiming. Study on Monte Carlo Particle Transport Method and Application[J]. Nuclear Power Engineering, 2025, 46(3): 1-17. doi: 10.13832/j.jnpe.2025.02.0062

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Study on Monte Carlo Particle Transport Method and Application

doi: 10.13832/j.jnpe.2025.02.0062

Deng Li^1
,,
Li Gang^{1, 2
,
,},
Zhang Baoyin^{1, 2},
Li Rui²,
Zhang Lingyu²,
Fu Yuanguang²,
Liu Peng²,
Ma Yan¹,
Shi Dunfu²,
Wang Xin²,
Qin Guiming¹

1.
Institute of Applied Physics and Computational Mathematics, Beijing, 100094, China
2.
CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088, China

Received Date: 2025-02-21
Rev Recd Date: 2025-03-26

Available Online: 2025-06-09

Publish Date: 2025-06-09

Abstract

Abstract

Monte Carlo (MC) particle transport methodology incorporates stochastic principles derived from probability theory and mathematical statistics to establish computational frameworks. This approach facilitates the numerical resolution of complex particle transport phenomena in nuclear systems. Over the course of seven decades of development, MC particle transport theory and algorithms have reached a high level of technical maturity. This has resulted in the development of several specialized software packages, which are widely applied in fields such as nuclear radiation shielding, reactor core criticality safety analysis, nuclear detection, and radiation medicine. This study commences by establishing the theoretical framework underlying MC particle transport methodologies. Through rigorous mathematical derivation, we present the neutron flux density formulation developed via MC simulations for addressing integral-form neutron transport equations, coupled with analytical frameworks for determining associated response parameters. It also outlines the classification of deterministic approaches for solving transport equations. The study reviews the historical development and computational application of MC particle transport methods, while summarizing significant software developed domestically and internationally. Furthermore, it examines recent advancements in utilizing graphics processing unit (GPU) technology to develop MC particle transport software, highlighting current research directions and progress in this field. This paper provides a comprehensive review of recent advancements in MC particle transport methodologies and associated software, with a specific focus on key features and capabilities of the independently developed J Monte Carlo transport (JMCT) software.
- Monte Carlo (MC) method,
- Particle transport,
- Parallel computing,
- J Monte Carlo transport (JMCT),
- Artificial intelligence

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References(80)

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