通用核事故剂量计

IF 3.4
Paige K. Witter*, Brian Champine, Paul E. Maggi and Mark A. Mitchell, 
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引用次数: 0

摘要

劳伦斯利弗莫尔国家实验室(LLNL)通用核事故剂量测定(UNAD)项目是一项为期四年的倡议,旨在推进核事故剂量测定方法。本文概述了研究、主要发现和整个项目的进展。主要目标包括了解核事故剂量学的历史背景,巩固NNSA/DOE综合体内现有的剂量学技术,促进主题专家之间的合作,并探索潜在实施的新技术。技术重点是研究新的和新颖的技术、仪器方法和分析方法,为潜在核事故剂量计(NAD)提出建议,以便在美国能源部的综合设施中普遍部署。成立了一个多实验室和多国用户小组,定期举行会议以促进知识交流。联合国开发计划署小组参加了两次国际核事故剂量测定相互比较活动和一次特征描述活动,在这些活动中部署了现有的LLNL - NAD和一个原型丙氨酸电子顺磁剂量计NAD。根据演习、实验室研究和与其他实验室合作的结果,正在对NAD原型进行持续改进。目前正在实施一种机器学习算法,以优化当前LLNL NAD的几何形状和转换因子,最终设计将在下一次练习中进行测试。讨论了项目的主要经验教训和未来发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Universal Nuclear Accident Dosimeter

Universal Nuclear Accident Dosimeter

The Lawrence Livermore National Laboratory (LLNL) Universal Nuclear Accident Dosimetry (UNAD) project is a four-year initiative aimed at advancing nuclear accident dosimetry methods. This article presents an overview of the research, key findings, and the progress made throughout the project. The primary goals included a background into the history of nuclear accident dosimetry, consolidating current dosimetry techniques within the NNSA/DOE complex, fostering collaboration among subject matter experts, and exploring novel technologies for potential implementation. The technical focus centered on investigating new and novel technologies, instrumentation methods, and analysis methods to develop recommendations for a potential nuclear accident dosimeter (NAD) to be universally deployed through the DOE complex. A multilaboratory and multinational Usergroup was established, conducting periodic meetings to facilitate knowledge exchange. The UNAD team has participated in two international nuclear accident dosimetry intercomparison exercises and one characterization exercise, where the existing LLNL NAD and a prototype alanine electron paramagnetic dosimeter NAD were deployed. Ongoing improvements are being made to the prototype NAD based on results from the exercises, laboratory studies, and collaboration with other laboratories. A machine learning algorithm to optimize the geometry and conversion factors of the current LLNL NAD is being implemented, and the resulting design will be tested in the next exercise. Key lessons learned and future directions for the project are discussed.

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