单次失流事故中近乎自主管理与控制系统的开发与评估

Linyu Lin, Paridhi Athe, P. Rouxelin, Truc-Nam Dinh, J. Lane
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引用次数: 2

摘要

在这项工作中,设计了一个近乎自治的管理和控制(NAMAC)系统,用于诊断反应堆状态,并为操作员提供维护反应堆安全和性能的建议。提出了一个三层分层的工作流程来指导NAMAC系统的设计和开发。该工作流的三层对应于知识库、数字孪生开发层(针对不同的NAMAC功能)和NAMAC操作层。NAMAC中的数字孪生被描述为支持不同自主控制功能的知识获取系统。因此,基于知识库,训练一组数字孪生模型来确定工厂状态,预测物理组件或系统的行为,并对可用的控制选项进行排序。根据NAMAC操作流程组装训练好的数字孪生模型,以支持在事故场景中选择最优控制动作的决策过程。为了展示NAMAC系统的能力,设计了一个案例研究,其中在单次失流事故中,为操作实验增殖反应堆II (EBR-II)的模拟器实施了基线NAMAC。通过对哥特数据生成引擎中的控制参数进行采样,得到用于开发数字孪生模型的训练数据库。经过培训和测试,根据操作流程将数字孪生体组装成NAMAC系统。将该NAMAC系统与哥特式植物模拟器相结合,生成了一个混淆矩阵,以说明所实现的NAMAC系统的准确性和鲁棒性。研究发现,在训练数据库内,NAMAC能够以零混淆率给出合理的推荐。然而,当场景超出训练案例时,混淆率增加,特别是当场景更严重时。因此,增加了一个差异检查器来检测意外的反应堆状态,并提醒操作人员采取安全措施。
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Development and Assessment of a Nearly Autonomous Management and Control System During a Single Loss of Flow Accident
In this work, a Nearly Autonomous Management and Control (NAMAC) system is designed to diagnose the reactor state and provide recommendations to the operator for maintaining the safety and performance of the reactor. A three layer-hierarchical workflow is suggested to guide the design and development of the NAMAC system. The three layers in this workflow corresponds to knowledge base, digital twin developmental layer (for different NAMAC functions), and NAMAC operational layer. Digital twin in NAMAC is described as knowledge acquisition system to support different autonomous control functions. Therefore, based on the knowledge base, a set of digital twin models is trained to determine the plant state, predict behavior of physical components or systems, and rank available control options. The trained digital twin models are assembled according to NAMAC operational workflow to support decision-making process in selecting the optimal control actions during an accident scenario. To demonstrate the capability of the NAMAC system, a case study is designed, where a baseline NAMAC is implemented for operating a simulator of the Experimental Breeder Reactor II (EBR-II) during a single loss of flow accident. Training database for development of digital twin models is obtained by sampling the control parameters in the GOTHIC data generation engine. After the training and testing, the digital twins are assembled into a NAMAC system according to the operational workflow. This NAMAC system is coupled with the GOTHIC plant simulator, and a confusion matrix is generated to illustrate the accuracy and robustness of implemented NAMAC system. It is found that within the training databases, NAMAC can make reasonable recommendations with zero confusion rate. However, when the scenario is beyond the training cases, the confusion rate increases, especially when the scenarios are more severe. Therefore, a discrepancy checker is added to detect unexpected reactor states and alert operators for safety-minded actions.
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