Development and Assessment of a Nearly Autonomous Management and Control System for Advanced Reactors
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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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Aug 2020.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 0; 2025: 3; 2026: 1.
9 papers.
- 2022Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Modelscited by 9
- 2022Advanced Transient Diagnostic with Ensemble Digital Twin Modelingcited by 2
- 2025An Unsupervised Deep Explainable AI Framework for Localization of Concurrent Replay Attacks in Nuclear Reactor Signalscited by 2
- 2021Digital-Twin-Based Improvements to Diagnosis, Prognosis, Strategy Assessment, and Discrepancy Checking in a Nearly Autonomous Management and Control Systemcited by 1
- 2022A Long-term Dependent and Trustworthy Approach to Reactor Accident Prognosis based on Temporal Fusion Transformercited by 1
- 2023Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Methods Integrated in Instrumentation & Control Systemscited by 0
- 2025Experimental Assessment of a Multi-Class AI/ML Architecture for Real-Time Characterization of Cyber Events in a Live Research Reactorcited by 0
- 2025Explainable Unsupervised Multi-Anomaly Detection and Temporal Localization in Nuclear Times Series Data with a Dual Attention-Based Autoencodercited by 0
- 2026Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observabilitycited by 0
- 2026Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability8 Apr 2026
- 2025Explainable Unsupervised Multi-Anomaly Detection and Temporal Localization in Nuclear Times Series Data with a Dual Attention-Based Autoencoder15 Sep 2025
- 2025Experimental Assessment of a Multi-Class AI/ML Architecture for Real-Time Characterization of Cyber Events in a Live Research Reactor26 Aug 2025
- 2025An Unsupervised Deep Explainable AI Framework for Localization of Concurrent Replay Attacks in Nuclear Reactor Signals5 Aug 2025
- 2023Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Methods Integrated in Instrumentation & Control Systems8 Aug 2023
- 2022A Long-term Dependent and Trustworthy Approach to Reactor Accident Prognosis based on Temporal Fusion Transformer28 Oct 2022
- 2022Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models27 Jun 2022
- 2022Advanced Transient Diagnostic with Ensemble Digital Twin Modeling23 May 2022
- 2021Digital-Twin-Based Improvements to Diagnosis, Prognosis, Strategy Assessment, and Discrepancy Checking in a Nearly Autonomous Management and Control System23 May 2021