Digital-Twin-Based Improvements to Diagnosis, Prognosis, Strategy Assessment, and Discrepancy Checking in a Nearly Autonomous Management and Control System

TL;DR

This paper enhances a nearly autonomous control system for nuclear reactors by integrating digital twins, machine learning, and discrepancy checking to improve diagnosis, prognosis, and decision-making during complex loss-of-flow scenarios.

Contribution

It introduces a refined NAMAC system utilizing digital twins, machine learning, and discrepancy detection to improve operational recommendations in complex scenarios.

Findings

Enhanced NAMAC performance in loss-of-flow scenarios

Effective digital twin and machine learning integration

Improved discrepancy detection capabilities

Abstract

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.