The Monte Carlo(MC)method offers significant advantages in handling complex geometries and physical processes in particle transport problems and has become a widely used approach in reactor physics analysis,radiation ...The Monte Carlo(MC)method offers significant advantages in handling complex geometries and physical processes in particle transport problems and has become a widely used approach in reactor physics analysis,radiation shielding design,and medical physics.However,with the rapid advancement of new nuclear energy systems,the Monte Carlo method faces challenges in efficiency,accuracy,and adaptability,limiting its effectiveness in meeting modern design requirements.Overcoming technical obstacles related to high-fidelity coupling,high-resolution computation,and intelligent design is essential for using the Monte Carlo method as a reliable tool in numerical analysis for these new nuclear energy systems.To address these challenges,the Nuclear Energy and Application Laboratory(NEAL)team at the University of South China developed a multifunctional and generalized intelligent code platform called MagicMC,based on the Monte Carlo particle transport method.MagicMC is a developing tool dedicated to nuclear applications,incorporating intelligent methodologies.It consists of two primary components:a basic unit and a functional unit.The basic unit,which functions similarly to a standard Monte Carlo particle transport code,includes seven modules:geometry,source,transport,database,tally,output,and auxiliary.The functional unit builds on the basic unit by adding functional modules to address complex and diverse applications in nuclear analysis.MagicMC introduces a dynamic Monte Carlo particle transport algorithm to address time-space particle transport problems within emerging nuclear energy systems and incorporates a CPU-GPU heterogeneous parallel framework to enable high-efficiency,high-resolution simulations for large-scale computational problems.Anticipating future trends in intelligent design,MagicMC integrates several advanced features,including CAD-based geometry modeling,global variance reduction methods,multi-objective shielding optimization,high-resolution activation analysis,multi-physics coupling,and radiation therapy.In this paper,various numerical benchmarks-spanning reactor transient simulations,material activation analysis,radiation shielding optimization,and medical dosimetry analysis-are presented to validate MagicMC.The numerical results demonstrate MagicMC's efficiency,accuracy,and reliability in these preliminary applications,underscoring its potential to support technological advancements in developing high-fidelity,high-resolution,and high-intelligence MC-based tools for advanced nuclear applications.展开更多
活化-燃耗计算是反应堆分析的重要组成部分,需要耦合临界输运程序和点燃耗程序来迭代求解。LightAB(Light Activation and Burnup)是一个新的轻量化的通用活化-燃耗分析程序,可以用来处理活化-燃耗系统。LightAB采用了基于燃耗计算程序O...活化-燃耗计算是反应堆分析的重要组成部分,需要耦合临界输运程序和点燃耗程序来迭代求解。LightAB(Light Activation and Burnup)是一个新的轻量化的通用活化-燃耗分析程序,可以用来处理活化-燃耗系统。LightAB采用了基于燃耗计算程序ORIGEN-2和ORIGEN-S的燃耗数据库,并使用处理刚性燃耗系统的切比雪夫有理逼近算法(Chebyshev Rational Approximation Method,CRAM)。LightAB支持衰变模式,定通量模式和定功率模式的活化-燃耗计算。为了验证LightAB程序的正确性,计算了237Np衰变问题和锆(Zr)合金的固定通量辐照问题,结果与燃耗程序ORIGEN-2.1一致。实现了LightAB和RMC程序的耦合,并用于计算压水堆(Pressurized Water Reactor,PWR)栅元燃耗基准题、PWR组件燃耗问题、经合组织/核能机构(Organisation for Economic Co-operation and Development/Nuclear Energy Agency,OECD/NEA)快堆燃耗基准问题,计算结果与RMC的燃耗计算模块一致。在超钚同位素的辐照生产与RMC对比模拟计算中,LightAB展现出良好的应用前景。展开更多
Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS m...Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.展开更多
基金supported by the National Natural Science Foundation of China(Nos.12475174 and U2267207)YueLuShan Center Industrial Innovation(No.2024YCII0108)+2 种基金Natural Science Foundation of Hunan Province(No.2022JJ40345)Science and Technology Innovation Project of Hengyang(No.202250045336)the Project of State Key Laboratory of Radiation Medicine and Protection,Soochow University(No.GZK12023031)。
文摘The Monte Carlo(MC)method offers significant advantages in handling complex geometries and physical processes in particle transport problems and has become a widely used approach in reactor physics analysis,radiation shielding design,and medical physics.However,with the rapid advancement of new nuclear energy systems,the Monte Carlo method faces challenges in efficiency,accuracy,and adaptability,limiting its effectiveness in meeting modern design requirements.Overcoming technical obstacles related to high-fidelity coupling,high-resolution computation,and intelligent design is essential for using the Monte Carlo method as a reliable tool in numerical analysis for these new nuclear energy systems.To address these challenges,the Nuclear Energy and Application Laboratory(NEAL)team at the University of South China developed a multifunctional and generalized intelligent code platform called MagicMC,based on the Monte Carlo particle transport method.MagicMC is a developing tool dedicated to nuclear applications,incorporating intelligent methodologies.It consists of two primary components:a basic unit and a functional unit.The basic unit,which functions similarly to a standard Monte Carlo particle transport code,includes seven modules:geometry,source,transport,database,tally,output,and auxiliary.The functional unit builds on the basic unit by adding functional modules to address complex and diverse applications in nuclear analysis.MagicMC introduces a dynamic Monte Carlo particle transport algorithm to address time-space particle transport problems within emerging nuclear energy systems and incorporates a CPU-GPU heterogeneous parallel framework to enable high-efficiency,high-resolution simulations for large-scale computational problems.Anticipating future trends in intelligent design,MagicMC integrates several advanced features,including CAD-based geometry modeling,global variance reduction methods,multi-objective shielding optimization,high-resolution activation analysis,multi-physics coupling,and radiation therapy.In this paper,various numerical benchmarks-spanning reactor transient simulations,material activation analysis,radiation shielding optimization,and medical dosimetry analysis-are presented to validate MagicMC.The numerical results demonstrate MagicMC's efficiency,accuracy,and reliability in these preliminary applications,underscoring its potential to support technological advancements in developing high-fidelity,high-resolution,and high-intelligence MC-based tools for advanced nuclear applications.
文摘活化-燃耗计算是反应堆分析的重要组成部分,需要耦合临界输运程序和点燃耗程序来迭代求解。LightAB(Light Activation and Burnup)是一个新的轻量化的通用活化-燃耗分析程序,可以用来处理活化-燃耗系统。LightAB采用了基于燃耗计算程序ORIGEN-2和ORIGEN-S的燃耗数据库,并使用处理刚性燃耗系统的切比雪夫有理逼近算法(Chebyshev Rational Approximation Method,CRAM)。LightAB支持衰变模式,定通量模式和定功率模式的活化-燃耗计算。为了验证LightAB程序的正确性,计算了237Np衰变问题和锆(Zr)合金的固定通量辐照问题,结果与燃耗程序ORIGEN-2.1一致。实现了LightAB和RMC程序的耦合,并用于计算压水堆(Pressurized Water Reactor,PWR)栅元燃耗基准题、PWR组件燃耗问题、经合组织/核能机构(Organisation for Economic Co-operation and Development/Nuclear Energy Agency,OECD/NEA)快堆燃耗基准问题,计算结果与RMC的燃耗计算模块一致。在超钚同位素的辐照生产与RMC对比模拟计算中,LightAB展现出良好的应用前景。
基金supported by the Platform Development Foundation of the China Institute for Radiation Protection(No.YP21030101)the National Natural Science Foundation of China(General Program)(Nos.12175114,U2167209)+1 种基金the National Key R&D Program of China(No.2021YFF0603600)the Tsinghua University Initiative Scientific Research Program(No.20211080081).
文摘Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.
