The SoTeRiA Research Laboratory proudly congratulates Dr. John Beal on successfully completing his Ph.D. requirements in the Department of Nuclear, Plasma & Radiological Engineering (NPRE) at the Grainger College of Engineering, University of Illinois Urbana-Champaign!
Dr. Beal’s research focused on developing a temporal coupling framework that integrates maintenance human performance, physical degradation, and Digital Twin (DT) models for Probabilistic Risk Assessment (PRA) in nuclear power plants. His work leverages modeling and simulation (M&S) techniques to estimate the reliability of repairable components in data-scarce situations, such as advanced reactor designs or aging nuclear power plants undergoing modernization, including the implementation of DT technology.
As a Graduate Research Assistant in the SoTeRiA Research Laboratory, led by Prof. Zahra Mohaghegh, John Beal received both a Department of Energy (DOE) Nuclear Energy University Program (NEUP) fellowship and a Nuclear Regulatory Commission (NRC) fellowship. He will be joining X-Energy as a Probabilistic Risk Assessment Engineer, under the supervision of Dr. Harry Liao and Mr. Drew Nigh.
The SoTeRiA team celebrates Dr. Beal’s outstanding accomplishments, looks forward to continued collaboration, and wishes him the very best in his career.
Special thanks to John’s Ph.D. advisor, Prof. Zahra Mohaghegh, and to his Ph.D. committee members, Prof. Sriver Ryan, Prof. Sakurahara Tatsuya, Prof. Alam Syed Bahauddin, Prof. Vergari Lorenzo, and Dr. Bui Ha, for their invaluable support, feedback, and guidance throughout his doctoral journey.
Executive Summary:
Temporal Coupling of Maintenance Human Performance, Physical Degradation, and Digital Twin Models for Probabilistic Risk Assessment in Nuclear Power Plants
Probabilistic Risk Assessment (PRA) has played a critical role in improving the safety and performance of existing Nuclear Power Plants (NPPs) and is a key element of the U.S. Nuclear Regulatory Commission’s Risk-Informed Performance-Based (RIPB) regulatory framework. PRA methodologies have evolved over the years, and their continued advancement is increasingly important in light of the Accelerating Deployment of Versatile, Advanced Nuclear for Clean Energy (ADVANCE) Act of 2024. This research leverages modeling and simulation (M&S) to estimate the reliability of repairable components in data-scarce situations, such as new reactor designs or aging plants undergoing operational changes, including the adoption of digital twins (DT). The methodological contributions of this thesis are demonstrated using the Extremely Low Probability of Rupture (xLPR) Probabilistic Fracture Mechanics (PFM) code, applied to reactor coolant system piping susceptible to stress corrosion cracking (SCC). However, the approach is broadly applicable to a wide range of component types and degradation mechanisms. This thesis makes three key scientific contributions:
- A novel method for estimating component reliability by integrating Human Reliability Analysis (HRA)-based maintenance models with physical degradation models. Utilizing dynamic PRA techniques, the method captures the temporal, bi-directional interactions between human actions and component degradation.
- An uncertainty-based validation methodology for coupled maintenance and physical degradation models, designed for contexts lacking empirical maintenance performance data. This approach involves constructing a graphical causal model to represent sources of uncertainty and their interrelationships, followed by quantification using a Bayesian Belief Network (BBN). This supports a comprehensive treatment of epistemic uncertainties, including model-form uncertainty.
- An Integrated PRA (I-PRA) methodological framework tailored for DT-enabled NPPs. This framework simulatesthe dynamic, bidirectional interactions among a high-fidelity physical twin (PT), human interventions (including maintenance decision-making and HRA-based maintenance performance models), and the DT. It establishes a two-way integration between the human-PT-DT system and PRA, allowing for forward-looking plant-level risk estimates based on projected component conditions. These insights inform both near-term maintenance actions and long-term strategic and regulatory decision-making in alignment with RIPB principles.
