源词是基于机器学习的复杂核事故中短寿命核素的逆转,使用场外马剂量速率
Yongsheng Ling1, Chengfeng Liu2, Qing Shan2
1Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106 Nanjing, China; Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, 215021 Suzhou, China; Institute of Environmental Engineering, ETH Zürich, Zürich CH-8093, Switzerland.
Journal of hazardous materials
|January 1, 2024
概括
机器学习准确地估计了核事故期间短暂的放射性核素释放率. 长期短期记忆网络实现了高精度,有助于应急响应和保护措施的决策.
科学领域:
- 核工程 核工程是指核工程.
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 核事故释放出危险的短寿命放射性核素,需要准确的源代信息来应对紧急情况.
- 估计放射性核素释放时间是具有挑战性的,因为复杂的事故条件和不可靠的监测数据.
研究的目的:
- 在复杂的核事故场景中估计关键的短寿命放射性核素 (Kr-88,Sr-91,Te-132,I-131) 的释放率.
- 评估机器学习方法在源代码反转方面的有效性.
主要方法:
- 利用机器学习方法,特别是长期短期记忆 (LSTM) 网络,估计放射性核素释放率.
- 分析了两个复杂的核事故场景,以评估模型的性能.
主要成果:
- 在估计释放率方面,LSTM网络表现出强的表现,Te-132.的平均绝对百分比误差低至7.16%.
- 玛剂量率被确定为影响估计准确性的关键参数.
结论:
- 机器学习,特别是LSTM网络,为核紧急情况中准确的源期估计提供了一个有前途的工具.
- 从这种方法中获得的准确的源期数据可以显著改善核事故期间保护措施的决策.
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