  A nearly autonomous management and control (NAMAC) system is designed to
furnish recommendations to operators for achieving particular goals based on
NAMAC's knowledge base. As a critical component in a NAMAC system, digital
twins (DTs) are used to extract information from the knowledge base to support
decision-making in reactor control and management during all modes of plant
operations. With the advancement of artificial intelligence and data-driven
methods, machine learning algorithms are used to build DTs of various functions
in the NAMAC system. To evaluate the uncertainty of DTs and its impacts on the
reactor digital instrumentation and control systems, uncertainty quantification
(UQ) and software risk analysis is needed. As a comprehensive overview of prior
research and a starting point for new investigations, this study selects and
reviews relevant UQ techniques and software hazard and software risk analysis
methods that may be suitable for DTs in the NAMAC system.
