# Development and Assessment of a Nearly Autonomous Management and Control System for Advanced Reactors

arXiv: https://arxiv.org/abs/2008.12888 · HTML: https://mlsanity.com/paper/2008.12888/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models](https://mlsanity.com/paper/2206.14615/) (2022)
- [Advanced Transient Diagnostic with Ensemble Digital Twin Modeling](https://mlsanity.com/paper/2205.11469/) (2022)
- [An Unsupervised Deep Explainable AI Framework for Localization of Concurrent Replay Attacks in Nuclear Reactor Signals](https://mlsanity.com/paper/2508.09162/) (2025)
- [Digital-Twin-Based Improvements to Diagnosis, Prognosis, Strategy Assessment, and Discrepancy Checking in a Nearly Autonomous Management and Control System](https://mlsanity.com/paper/2105.11039/) (2021)
- [A Long-term Dependent and Trustworthy Approach to Reactor Accident Prognosis based on Temporal Fusion Transformer](https://mlsanity.com/paper/2210.17298/) (2022)
- [Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Methods Integrated in Instrumentation & Control Systems](https://mlsanity.com/paper/2308.05120/) (2023)
- [Experimental Assessment of a Multi-Class AI/ML Architecture for Real-Time Characterization of Cyber Events in a Live Research Reactor](https://mlsanity.com/paper/2509.00076/) (2025)
- [Explainable Unsupervised Multi-Anomaly Detection and Temporal Localization in Nuclear Times Series Data with a Dual Attention-Based Autoencoder](https://mlsanity.com/paper/2509.12372/) (2025)
- [Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability](https://mlsanity.com/paper/2604.07292/) (2026)

## Abstract (arXiv)

This paper develops a Nearly Autonomous Management and Control (NAMAC) system for advanced reactors. The development process of NAMAC is characterized by a three layer-layer architecture: knowledge base, the Digital Twin (DT) developmental layer, and the NAMAC operational layer. The DT is described as a knowledge acquisition system from the knowledge base for intended uses in the NAMAC system. A set of DTs with different functions is developed with acceptable performance and assembled according to the NAMAC operational workflow to furnish recommendations to operators. 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 during a single loss of flow accident. When NAMAC is operated in the training domain, it can provide reasonable recommendations that prevent the peak fuel centerline temperature from exceeding a safety criterion.