使用物理和机器学习进行无人机部署的放射性散射装置的风险预测和缓解
Osamong Gideon Akou1, Xuan Wang1, Shuhuan Liu1
1School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, China.
概括
无人机部署的放射性散射装置 (RDD) 构成了安全威胁. 我们的框架集成建模,优化和机器学习来评估RDD风险并告知应急准备.
科学领域:
- 美国国土安全部
- 辐射安全 辐射安全
- 计算建模 计算建模
背景情况:
- 无人机部署放射性散射装置 (RDD) 是一个重大且不断变化的安全挑战.
- 现有的工具需要加强,以便对新型威胁进行有效的后果评估和应对计划.
研究的目的:
- 开发和验证一个综合计算框架,用于评估基于无人机的RDD威胁.
- 确定最佳的对抗场景,并评估潜在的后果,以了解紧急情况的准备.
主要方法:
- 综合框架结合了基于物理的分散建模,受约束优化 (L-BFGS-B) 和机器学习.
- 蒙特卡洛模拟生成了2000个合成场景,包含各种放射性核素,气象和风险区域.
- 使用HotSpot和JRODOS进行后果建模,然后进行保护性行动分析.
主要成果:
- 优化确定了分散效率 (最大化污染>10公里2) 和操作隐形 (最小化能量/检测风险) 之间的权衡.
- 与JRODOS相比,HotSpot预测的总有效剂量 (TED) 和时间整合空气度 (TIAC) 更高.
- 确定I-131和Am-241分别是急性和长期风险的关键放射性核素;加强庇护显著降低了累积剂量.
结论:
- 开发的框架提供了一个强大的工具,用于评估基于无人机的RDD威胁,并为应急响应策略提供信息.
- 机器学习模型提供快速和准确的预测 (R2 = 0.975),距离是主要的预测因素.
- 结果为加强国家安全和公共安全措施对抗辐射威胁提供了可操作的指导.
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