小型空间反应堆屏蔽结构设计与优化OA北大核心
Design and optimization of shielding for small space reactors
本研究针对空间核动力系统的辐射屏蔽问题,开展了屏蔽结构的设计及优化方法研究.研究采用蒙特卡洛方法进行屏蔽计算,通过固定源和函数展开以及权重窗口法加快了蒙特卡洛计算速度.结合改进的神经网络与多目标优化算法,提升了屏蔽计算速度和预测精度.使用NSGA2 DE算法进行多目标优化分析,通过分析不同条件下的辐射剂量,得到了屏蔽体在空间堆中合适的位置高度和层数,最终通过决策系统选择了3组不同的屏蔽设计方案,在满足辐射防护要求的同时,实现了屏蔽质量和厚度的最优化.研究结果表明,上述4种材料在空间堆屏蔽应用中的有效性,合理选择和优化屏蔽结构可以在保证辐射防护效果的同时,显著降低屏蔽体的质量和体积,满足空间应用的特殊需求.
This study addresses the radiation shielding challenges in space nuclear power systems and pro-poses a series of optimized design strategies.By analyzing the basic principles of radiation interaction with mat-ter,the shielding requirements for neutrons and gamma rays are clarified,and suitable shielding materials such as lithium hydride,boron carbide,tungsten,and stainless steel are selected to maximize shielding effec-tiveness.Monte Carlo methods were employed for shielding calculations,with computational acceleration achieved via fixed-source modeling,function expansion,and weight window techniques.By integrating im-proved neural networks and multi-objective optimization algorithms,the speed and accuracy of shielding cal-culations are significantly improved.The NSGA2 DE algorithm was used for multi-objective optimization analysis,which helps determine the optimal positioning,height,and layer count of the shielding within the space reactor.Three different shielding design schemes were selected through a decision-making system,opti-mizing shielding mass and thickness while meeting radiation protection requirements.The results demonstrate the effectiveness of these four materials in space reactor shielding applications,showing that reasonable selec-tion and optimization of shielding structures can significantly reduce the mass and volume while maintaining radiation protection performance,meeting the stringent demands of space-based implementations.
李耀晨;张克凡;陈红丽
中国科学技术大学核科学技术学院,合肥 230027中国科学技术大学核科学技术学院,合肥 230027中国科学技术大学核科学技术学院,合肥 230027
核科学
空间核动力系统辐射屏蔽蒙特卡洛方法多目标优化NSGA2 DE算法
Space nuclear power systemsRadiation shieldingMonte Carlo methodMulti-objective optimi-zationNSGA2 DE algorithm
《四川大学学报(自然科学版)》 2025 (3)
651-660,10
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