This paper describes the outline and development plan for “Advanced Reactor Knowledge- and Artificial Intelligence (AI)-aided design integration approach through the whole plant lifecycle (ARKADIA),” which the Japan Atomic Energy Agency has begun developing to transform advanced nuclear reactor design to meet expectations of a safe, economic, and sustainable carbon-free energy source. ARKADIA will realize AI-aided integrated numerical analysis to offer the best possible solutions for any challenge that could arise in the design and operation of a nuclear plant, including optimization of safety equipment as well as structures, systems, and components. State-of-the-art numerical simulation technologies and a knowledge base that stores data and insights from past nuclear reactor development projects and R&D are integrated with AI. In the first phase of development, ARKADIA-design and ARKADIA-safety will be constructed individually, with the first target of a sodium-cooled reactor. In a subsequent phase, everything will be integrated into a single entity that is technology inclusive and applicable not only to advanced rectors with a variety of concepts, coolants, configurations, and output levels but also to existing light-water reactors.

For a sodium-cooled fast reactor, the capability for stable cooling and avoiding re-criticality on the debris bed is essential for achieving in-vessel retention when severe accidents occur. However, an unexploited uncertainty still existed regarding the compound effect of the heterogeneous configuration and dynamic particle redistribution for the debris bed’s criticality and cooling safety assessment. Therefore, this research aims to develop a numerical tool for investigating the effects of the different transformations of the heterogeneous configurations on the debris bed’s criticality/cooling assessment. Based on the newly proposed methodology in this research, via integrating the Discrete Element Method (DEM) with Computational Fluid Dynamics (CFD) and Monte-Carlo-based Neutronics (MCN), the coupled CFD–DEM–MCN solver was constructed with the originally created interface to integrate two existing codes. The effects of the different bed configurations’ transformations on the bed safety assessments were also quantitively confirmed, indicating that the effect of the particle-centralized fissile material had the dominant negative effect on the safety margin of avoiding re-criticality and particle re-melting accidents and had a more evident impact than the net bed-centralized effect. This coupled solver can serve to further assess the debris bed’s safety via a multi-physics simulation approach, leading to safer SFR design concepts.

In probabilistic risk assessment (PRA), the fracture limit of fuel cladding tubes under loss-of-coolant accident conditions plays a critical role in determining the core damage, highlighting the need for accurate modeling of cladding tube fracture behavior. However, for high-burnup cladding tubes, it is often infeasible to conduct extensive experiments due to limited material availability, high costs, and technical constraints. These limitations make it difficult to acquire sufficient data, leading to substantial epistemic uncertainty in fracture modeling. To enhance the realism of PRA results under such constraints, it is essential to develop methods that can effectively reduce epistemic uncertainty using limited experimental data. In this study, we propose a Bayesian approach for designing experimental conditions based on a widely applicable information criterion (WAIC) in order to effectively reduce the uncertainty in the prediction of fuel cladding tube fracture with limited data. We conduct numerical experiments to evaluate the effectiveness of the proposed method in comparison with conventional approaches based on empirical loss and functional variance. Two cases are considered: one where the true and predictive models share the same mathematical structure (Case 1) and one where they differ (Case 2). In Case 1, the empirical loss-based design performs best when the number of added data points is fewer than approximately 10. In Case 2, the WAIC-based design consistently achieves the lowest Bayes generalization loss, demonstrating superior robustness in situations where the true model is unknown. These results indicate that the proposed method enables more informative experimental designs on average and contributes to the effective reduction in epistemic uncertainty in practical applications.

While traditional risk importance measures in probabilistic risk assessment are effective for ranking safety-significant components, they often overlook critical aspects such as the timing of accident progression and consequences. Dynamic probabilistic risk assessment offers a framework to quantify such risk information, but standardized approaches for estimating risk importance measures remain underdeveloped. This study addresses this gap by: (1) reviewing traditional risk importance measures and their regulatory applications, highlighting their limitations, and introducing newly proposed risk-triplet-based risk importance measures, consisting of timing-based worth, frequency-based worth, and consequence-based worth; (2) conducting a case study of Level 2 dynamic probabilistic risk assessment using the Japan Atomic Energy Agency’s RAPID tool coupled with the severe accident code of MELCOR 2.2 to simulate a station blackout scenario in a boiling water reactor, generating probabilistically sampled sequences with quantified timing, frequency, and consequence of source term release; (3) demonstrating that the new risk importance measures provide differentiated insights into risk significance, enabling multidimensional prioritization of systems and mitigation strategies; for example, the timing-based worth quantifies the delay effect of mitigation systems, and the consequence-based worth evaluates consequence-mitigating potential. This study underscores the potential of dynamic probabilistic risk assessment and risk-triplet-based risk importance measures to support risk-informed and performance-based regulatory decision-making, particularly in contexts where the timing and severity of accident consequences are critical.