一个蒙特卡洛模拟数据集辐射场参数对于一个简单的核设施场景
Yisheng Hao1,2, Zhen Wu1,2,3, Yanheng Pu1,2
1Department of Engineering Physics, Tsinghua University, Beijing, 100084, China.
Scientific data
|September 17, 2024
概括
本研究引入了一种使用神经网络在核设施中重建3D辐射场的新方法. 开发的MCShield程序和AIS方法为提高工人安全提供了可靠的数据集.
科学领域:
- 核工程 核工程是指核工程.
- 计算物理 计算物理
- 辐射检测检测器可以检测到辐射.
背景情况:
- 准确的3D辐射场估计对于核设施的工人安全至关重要.
- 从测量结果中重建辐射场是复杂的,神经网络提供了一个有希望的解决方案.
- 有限的研究和缺乏标准化的数据集阻碍了使用神经网络直接3D辐射场的重建.
研究的目的:
- 开发一种使用神经网络直接3D辐射场重建的方法.
- 创建一个标准化的开源数据集,用于训练和评估神经网络模型.
- 为了验证MCShield程序和AIS减差方法的准确性和有效性.
主要方法:
- 模拟了一个简化的核设施模型.
- 使用蒙特卡洛程序MCShield模拟3D辐射参数.
- 通过基准测试 (WinFrith Iron和NUREG/CR-6115) 和AIS差异减少方法验证了MCShield的准确性.
主要成果:
- 证实了MCShield程序对辐射计算的正确性.
- 证明了AIS差异减少方法的有效性.
- 确定MCShield和AIS方法适用于生成用于辐射场重建的数据集.
结论:
- 可以有效地使用MCShield程序和AIS方法来生成用于3D辐射场重建的数据集.
- 这项工作解决了目前研究和数据集可用性的局限性,用于基于神经网络的辐射场分析.
- 经过验证的方法为改善核设施辐射安全协议铺平了道路.
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