Addressing Human Factors Challenges for Control Room Operators in Small Modular Reactors
Addressing Human Factors Challenges for Control Room Operators in Small Modular Reactors
批准号:
580479-2022
负责人:
Samuel, SibySS
金额:
$8.74万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31
中文摘要
小型模块化反应堆(SMR)是核工业中的一项新兴技术,可能有助于满足世界未来的能源需求。人为因素(HF)设计问题将因技术创新和操作而出现。操作员在从中央主控室(MCR)或远程监控单元同时监控多个反应堆单元时,可能会遇到更多的认知需求。自动化程序可能会减少人为错误,但在操作员需要建立适合工厂条件的心理模型的情况下,它们也可能降低操作员的意识。多机组运行的主要人为因素问题和研究关注是:在机组之间切换任务以及从被动监控到主动控制操作员的性能;当操作员更容易厌烦和出错、由于多个机组同时发生故障而导致超负荷时,工作量和对长时间监控的警觉性;MCR操作,特别是决策辅助技术,对操作员的性能的影响。在这方面,拟议的项目旨在提供一个自适应和辅助的AR接口,当核电厂操作员认识到高水平的工作量或低水平的情况意识(SA)时,该接口将进行实时干预,从而帮助操作员减少决策中的潜在操作员错误。预期结果将为改善政府对SMR的监管提供尖端专业知识和科学数据。
英文摘要
Small modular reactors (SMRs) are an emerging technology in the nuclear industry that may help meet the world's future energy needs. Human Factors (HF) design issues will arise due to technological innovations and operations. Operators may experience increased cognitive demands while monitoring several reactor units simultaneously from a central main control room (MCR) or remotely monitoring units. Automated procedures may reduce human error, but they may also lower operator awareness in cases where operators need to establish a suitable mental model of plant conditions. The primary human factors issues and research concerns for multi-unit operation are the impacts of switching tasks between units and from passive monitoring to the active control of operators' performance; workload, and vigilance dichotomy of prolonged monitoring when operators are more prone to boredom and errors, to overload due to concurrent failures on numerous units; MCR operations, particularly decision-aiding technology, on operators' performance. In this context, the proposed project aims to provide an adaptive and assistive AR interface that will intervene in real-time when high levels of workload or low levels of situation awareness (SA) are recognized in nuclear power plant operators, thereby assisting operators in limiting potential operator errors in decision-making. The expected outcomes will provide cutting-edge expertise and scientific data to improve government regulatory supervision of SMRs.
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