Digital-Twin-Based Improvements to Diagnosis, Prognosis, Strategy Assessment, and Discrepancy Checking in a Nearly Autonomous Management and Control System
This work addresses operational challenges in nuclear plant management by improving an existing control system, representing an incremental advancement in domain-specific automation.
The study refined a Nearly Autonomous Management and Control System (NAMAC) to provide control recommendations during complex loss-of-flow scenarios in a nuclear reactor simulator, using digital twins enhanced by machine learning and decision-making schemes, and demonstrated its capability in such scenarios.
The Nearly Autonomous Management and Control System (NAMAC) is a comprehensive control system that assists plant operations by furnishing control recommendations to operators in a broad class of situations. This study refines a NAMAC system for making reasonable recommendations during complex loss-of-flow scenarios with a validated Experimental Breeder Reactor II simulator, digital twins improved by machine-learning algorithms, a multi-attribute decision-making scheme, and a discrepancy checker for identifying unexpected recommendation effects. We assessed the performance of each NAMAC component, while we demonstrated and evaluated the capability of NAMAC in a class of loss-of-flow scenarios.