Large-scale computer programs simulate severe accident phenomena and often have a moderate-to-large number of models and input variables. Analytical solutions to uncertainty distributions of interested source terms are impractical, and influential inputs on outputs are hard to discover. Runs of such integral codes for complex severe accidents are generally time-consuming and hence computationally expensive. This article presents an integrated approach to uncertainty and sensitivity analyses for nuclear reactor severe accident source terms, with an example which simulates an accident sequence similar to that occurred at Unit 2 of the Fukushima Daiichi Nuclear Power Plant using an integral code, MELCOR. Monte-Carlo-based uncertainty analysis has been elaborated to investigate the released fractions of representative radionuclides, Cs and CsI. In order to estimate the sensitivity of inputs, which have a substantial influence on the core melt progression and the transportation process of radionuclides, a variance decomposition method is applied. Stochastic process, specifically a Dirichlet process, is applied to construct a surrogate model in sensitivity analysis as a substitute of the code. The surrogate model is cross-validated by comparing with corresponding results of MELCOR. The analysis with the simpler model avoids laborious computational cost/load, so that the importance measures for input factors are obtained successfully.

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.

Although dynamic probabilistic risk assessment (PRA) techniques have advanced in their ability to represent the progression of events over time, the formulation of suitable risk importance measures for these methods still poses a substantial challenge. In particular, it is difficult to reflect the full breadth and multidimensional character of the risk information produced by dynamic PRA. In this study, we introduce a set of new importance measures derived from the risk triplet perspective: (i) Timing-Based Worth (TBW), which expresses diversity in scenario occurrence time; (ii) Frequency-Based Worth (FBW), which captures the probability of different scenarios; and (iii) Consequence-Based Worth (CBW), which characterizes scenario consequences. Formal definitions of these three indices are provided, and a conceptual scheme for integrated importance evaluation is proposed to support multidimensional analysis. As an initial demonstration, TBW and FBW are applied to a simplified reliability case using a dynamic PRA framework built on the continuous Markov chain Monte Carlo (CMMC) approach. This application is used to test their interpretability and the internal consistency of the proposed scheme. The findings suggest that TBW and FBW make it possible to conduct more holistic importance evaluations, taking into account resilience effects and temporal diversity in addition to conventional frequency-based perspectives. Such an extension is expected to increase the usefulness of dynamic PRA outputs for risk-informed decision-making.

This paper considers global optimization with a black-box unknown objective function that can be non-convex and non-differentiable. Such a difficult optimization problem arises in many real-world applications, such as parameter tuning in machine learning, engineering design problem, and planning with a complex physics simulator. This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both fast convergence in practice and finite-time error bound in theory. The advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 11 benchmark test functions. Further, we modify the LOGO algorithm to specifically solve a planning problem via policy search with continuous state/action space and long time horizon while maintaining its finite-time error bound. We apply the proposed planning method to accident management of a nuclear power plant. The result of the application study demonstrates the practical utility of our method.