  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.
