Digital Twins (DTs) are emerging as powerful tools to enhance monitoring, maintenance, and safety assurance in nuclear power systems. This review synthesizes recent advances in the integration of DTs with artificial intelligence (AI) and machine learning (ML), emphasizing their application to condition monitoring, inservice testing, and inservice inspection. Case studies illustrate how DT frameworks, ranging from anomaly detection and fault classification to virtual sensing, can improve detection of early degradation, quantify severity, and extend observability into regions inaccessible to physical instrumentation. Collectively, these approaches demonstrate the potential of DTs to shift nuclear safety practices from periodic, schedule-based testing and inspection toward predictive and risk-informed strategies. The review also examines regulatory considerations, highlighting the challenges of qualifying AI/ML-enabled DTs.
Safety science has developed extensive taxonomies for categorizing human performance failures but lacks equivalent vocabulary for describing successful work performance, leaving practitioners without adequate language to discuss adaptive practices that enable successful work under varying conditions. This study developed a worker-centered framework for categorizing procedural adaptations through empirical research at a petrochemical facility. The research employed three-phase convergent validation: Phase 1 captured behavioral data through video observation of 1422 procedural steps; Phase 2 documented differences between Work-As-Imagined and Work-As-Done using the Skip-Order-Action Framework with subject matter expert interpretation; Phase 3 evaluated emerging patterns through worker interviews. Analysis revealed that 32.9% of procedural steps showed adaptations, yet all tasks were completed successfully. Three distinct categories emerged from convergent evidence: routine adaptations represent normalized workplace practices; efficiency adaptations optimize workflow while maintaining safety standards; and safety adaptations exceed prescribed requirements through additional verification. The resulting Routine-Efficiency-Safety (RES) framework provides practical vocabulary for Safety-II implementation, enabling organizations to distinguish between different types of procedural adaptations and their functions, moving beyond binary compliance assessments toward learning-focused conversations about successful work practices.
Abstract The U.S. Nuclear Regulatory Commission (NRC) is preparing to review a wide range non-light water reactors (LWRs) including heat pipe cooled microreactors. These microreactors are expected to have low thermal power and large safety margins partly due to the ability of heat pipes to passively reject heat. However, modeling and simulation of heat pipe cooled microreactors are a challenge due to their novelty. The NRC is developing its capability to model and simulate microreactors using the BlueCRAB suite of codes. This paper describes the NRC approach to non-LWRs and the initial efforts to simulate heat pipe cooled microreactors.
Autonomous operations in nuclear power involve complex human factors challenges. This is because the industry has been held to a high standard for safety due to a combination of consequence and public perception of risk and consequence. In industries like manufacturing and transportation, the risks associated with autonomous system failures are typically managed incrementally, allowing for faster adoption and iterative learning based on real-world data. Advanced autonomous concepts in nuclear power often require a redefinition of the operator’s role, which may bring about skill degradation, trust miscalibration, and compromised situation awareness. While these human factors challenges are not new to process control environments with high automation, the industry’s strong regulatory environment, rarity of high-consequence events, and defense-in-depth philosophy mean that addressing these challenges calls for tailored, evidence-based solutions. In this paper, we review characteristics within nuclear power that present human factors challenges that take on added complexity with autonomous operations.
