Katrina M. Groth

Coupling nuclear power plants with proton exchange membrane electrolyzers can improve plant utilization and support low-carbon hydrogen production, but it introduces physical and electrical dependencies that may affect nuclear safety and economic performance. This paper develops and applies an integrated probabilistic risk assessment framework to assess the risk introduced by coupling-induced hazards. Monte Carlo simulation is used to estimate the coupling-induced increase in core damage frequency, while economic risk is quantified using the conditional value at risk. The framework is applied to pressurized water reactor and small modular reactor configurations while varying the distances from the hydrogen facility to the hydrogen-side and nuclear-side switchyards. All considered configurations and layouts remain below the acceptability threshold of NRC Regulatory Guide 1.174. Hydrogen releases dominate the risk increase at short separation distances, whereas electrical overcurrent establishes a distance-independent risk floor at larger distances. Economic risk is weakly dependent on separation and is dominated by turbine and PEM-transformer damage. The results suggest that large separation distances may be unnecessary based on coupling-induced risk alone, because additional spacing provides limited benefit once explosion effects are reduced. Accordingly, a nominal 150 m separation distance is proposed, subject to site-specific deterministic requirements, together with increased attention to electrical isolation, breaker reliability, protection diversity, and independence among protection layers. Protecting the turbine and PEM transformer and shortening PEM recovery time offer the greatest economic benefit.

The rapid growth of Artificial Intelligence (AI) is driving an unprecedented increase in electricity demand from Data Centers (DCs). Nuclear Power Plants (NPPs) are increasingly being considered as reliable low-carbon energy sources capable of supplying the required baseload power. However, the coupling introduces new operational interdependencies that may affect the overall plant safety and reliability, which are not captured by traditional Probabilistic Risk Assessment (PRA) frameworks. This work proposes a Bayesian Network (BN)-based framework for the risk assessment of NPPs coupled with DCs. The BN structure explicitly captures the causal relationships from component failures and cyber-attacks to initiating events and their progression to core damage, consistent with standard PRA logic. The BN parameters are estimated through both expert elicitation and data-driven learning from data generated with a system simulator integrated within a Monte Carlo Simulation, while epistemic uncertainty is propagated with probabilistic parameter modeling. The framework enables the estimation of the Core Damage Frequency (CDF) increase due to coupling, identification of the most impactful failures and cyber-attacks, and extraction of the most probable causal paths that lead to core damage. Application to multiple coupling configurations involving Pressurized Water Reactors (PWRs) and Small Modular Reactors (SMRs) demonstrates the capability of the approach to provide both quantitative risk estimates and interpretable insights, supporting risk-informed design and operation of integrated NPP-DC systems.

Human reliability analysis (HRA) characterizes the causes and probabilities of human failure events (HFEs) in complex engineering systems. This work seeks to advance the state of the art in HRA by introducing models with: a causally robust theoretical basis, a user-friendly and data-driven quantification scheme, and applicability to a wide range of scenarios. We present a method for developing HRA Bayesian network (BN) model structures applicable to both control room and ex-control room scenarios, and a further method for model parameterization using literature and data. These structures build upon the Information–Decision–Action in Crew Context (IDAC) framework, which considers information-gathering, decision-making, and action execution to be the three main functions of operators in complex systems. We present the development of three causal models of information, decision, and action HFEs: the Causal Human Reliability Operator-Centered Models (CHROMO). The resulting BNs model the HFEs’ causal pathways and are capable of quantifying the effects of causal factors on failure probabilities. The BN structures, built with comprehensive data, have been validated through expert discussions, an ATHEANA-based case study on the human failure events at Three Mile Island, empirical data from German nuclear power plant operational experience, and control room scenarios in the U.S. HRA Empirical Study. These models are currently being incorporated into the Phoenix HRA method and are suitable for regulatory use.