人工神经网络用于优化马辐射屏蔽
Mahdieh Mokhtari Dorostkar1, Fatemeh Sadat Rasouli2
1Department of Physics, Urmia University, Urmia, Iran.
Journal of biomedical physics & engineering
|December 11, 2025
概括
人工神经网络 (ANN) 可以准确预测最佳的马辐射屏蔽. 与传统的蒙特卡洛模拟相比,这种机器学习方法大大减少了所需的时间和计算资源.
科学领域:
- 核工程 核工程是指核工程.
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 玛辐射屏蔽对于医疗,工业和研究应用至关重要.
- 传统的蒙特卡洛模拟用于盾牌优化是有效的,但计算密集且耗时.
研究的目的:
- 研究人工神经网络 (ANN) 在识别优化马辐射屏蔽的有效性.
- 与传统模拟相比,开发一种更快,更有效的盾牌设计方法.
主要方法:
- 使用MCNPX蒙特卡洛代码进行初始模拟,使用拟议的屏蔽材料.
- 从模拟中生成一个大数据集来训练ANN模型.
- 根据蒙特卡洛模拟结果验证了ANN的预测,并使用水幻影进行了剂量计算.
主要成果:
- 在ANN和蒙特卡洛模拟之间偏差不到1%的预测准确度.
- 证明了ANN能够准确预测未知配置的屏蔽性能的能力.
- 使用水幻影进行评估剂量降低,以进一步优化屏蔽材料的选择.
结论:
- ANN为辐射屏蔽研究提供了强大而高效的工具.
- 开发的ANN模型准确地预测了屏蔽材料的最佳重量分数组合.
- 这种方法加速了有效的马辐射屏蔽的设计过程.
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