Incorporate Modeling Uncertainty into the Decision Making of Passive System Reliability Assessment

International audiencePassive safety features will play crucial roles in the development of the future generation nuclear power plant technologies. However because of the insufficient experiences, researches and validations are still necessary with the aim to prove the actual performance and reliability of the passive systems. Uncertain resources, which will influence the reliability and performance of such systems, can be divided into two groups: modeling uncertainties and parametric uncertainties. Up to now, the researchers have as good as established ways to quantify the effects caused by the parameter uncertainties, e.g. the variation of physical parameters (environment temperature, fabrication error, etc.) and have already got a number of achievements. In addition to the parameter uncertainty, the modeling uncertainty, e.g. uncertain physical phenomenon, uncertainties by different modeling techniques, etc. shall also be an important contributor to the passive system performance. How to take into account the effect caused by this kind of factors, there hasn't any mature approaches. In this paper, a survey of researches about the modeling uncertainty from the open literature is presented. A framework to incorporate the modeling uncertainty into the decision making of passive system reliability assessment will be proposed based on the survey and the discussion

Variance Decomposition Sensitivity Analysis of a Passive Residual Heat Removal System Model

International audiencePassive systems are central in the safety design of new generation nuclear power plants (NPPs). In this paper, the variance decomposition method is adopted to identify key parameters which influence the uncertain behavior of the passive Residual Heat Removal system (RHRs) in the High Temperature Gas-Cooled Reactor (HTGR)

A multi-state physics modeling for estimating the size- and location-dependent loss of coolant accident initiating event probability

Multi-State Physics Modeling (MSPM) integrates multi-state modeling to describe a component degradation process by transitions among discrete states (e.g., no damage, micro-crack, flaw, rupture, etc.), with physics modeling by (physic) equations to describe the continuous degradation process within the states. In this work, we propose MSPM to describe the degradation dynamics of a piping system, accounting for the dependence on the size and location of the Loss of Coolant Accident (LOCA) initiating event of the Reactor Coolant System (RCS) of a Pressurized Water Reactor (PWR). Estimated frequencies of LOCA as a function of break size are used in a variety of regulatory applications and for the Probabilistic Risk Assessment (PRA) of Nuclear Power Plants (NPPs). Traditionally, two approaches have been used to assess LOCA frequencies as a function of pipe break size: estimates based on statistical analysis of field data collected from piping systems service experience and Probabilistic Fracture Mechanics (PFM) analysis of specific, postulated, physical damage mechanisms. However, due to the high reliability of NPP piping systems, it is difficult to construct a comprehensive service database based on which perform statistical analysis. On the other hand, it is difficult to utilize PFM models for calculating LOCA frequencies because many of the input variables and model assumptions are over-simplified and may not adequately represent the true plant conditions. We overcome these challenges and propose a size- and location-dependent LOCA initiating event frequencies estimation by resorting to the novel MSPM modeling scheme. Benchmarking is done with respect to the results obtained with the Generic Safety Issue (GSI) 191 framework that makes use of field data for LOCA initiating event probability calculation

Variance Decomposition Sensitivity Analysis of a Passive Residual Heat Removal System Model

AbstractPassive systems are central in the safety design of new generation nuclear power plants (NPPs). In this paper, the variance decomposition method is adopted to identify key parameters which influence the uncertain behavior of the passive Residual Heat Removal system (RHRs) in the High Temperature Gas-Cooled Reactor (HTGR)

Study on Probabilistic Safety Goals for Multimodule High-Temperature Gas-Cooled Reactor Based on Chinese Societal Risks

Nuclear safety goal is the basic standard for limiting the operational risks of nuclear power plants. The statistics of societal risks are the basis for nuclear safety goals. Core damage frequency (CDF) and large early release frequency (LERF) are typical probabilistic safety goals that are used in the regulation of water-cooled reactors currently. In fact, Chinese current probabilistic safety goals refer to the Nuclear Regulatory Commission (NRC) and the International Atomic Energy Agency (IAEA), and they are not based on Chinese societal risks. And the CDF and LERF proposed for water reactor are not suitable for high-temperature gas-cooled reactors (HTGR), because the design of HTGR is very different from that of water reactor. And current nuclear safety goals are established for single reactor rather than unit or site. Therefore, in this paper, the development of the safety goal of NRC was investigated firstly; then, the societal risks in China were investigated in order to establish the correlation between the probabilistic safety goal of multimodule HTGR and Chinese societal risks. In the end, some other matters about multireactor site were discussed in detail

Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective

Fault diagnosis plays an important role in complex and safety-critical systems such as nuclear power plants (NPPs). With the development of artificial intelligence (AI), extensive research has been carried out for fast and efficient fault diagnosis based on intelligent methods. This paper presents a review of various AI-based system-level fault diagnosis methods for NPPs. We first discuss the development history of AI. Based on this exposition, AI-based fault diagnosis techniques are classified into knowledge-driven and data-driven approaches. For knowledge-driven methods, we discuss both the early if–then-based fault diagnosis techniques and the current new theory-based ones. The principles, application, and comparative analysis of the representative methods are systematically described. For data-driven strategies, we discuss single-algorithm-based techniques such as ANN, SVM, PCA, DT, and clustering, as well as hybrid techniques that combine algorithms together. The advantages and disadvantages of both knowledge-driven and data-driven methods are compared, illustrating the tendency to combine the two approaches. Finally, we provide some possible future research directions and suggestions

Multi-Experts Analytic Hierarchy Process for the Sensitivity Analysis of Passive Safety Systems

International audienceInnovative Nuclear Power Plants (NPPs) resort to passive systems to increase their safety and reliability. However, during accidental scenarios, uncertainties affect the actual behavior of passive systems. In this paper, a systematic procedure based on the Analytic Hierarchy Process (AHP) for the identification of the uncertain parameters and the propagation of their associated uncertainties is proposed. An example of application is proposed with respect to the passive Residual Heat Removal system (RHRs) of the High Temperature Reactor-Pebble Modular (HTR-PM)

Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs

Uncertainty analysis of containment dose rate for core damage assessment in nuclear power plants

One of the most widely used methods to estimate core damage during a nuclear power plant accident is containment radiation measurement. The evolution of severe accidents is extremely complex, leading to uncertainty in the containment dose rate (CDR). Therefore, it is difficult to accurately determine core damage. This study proposes to conduct uncertainty analysis of CDR for core damage assessment. First, based on source term estimation, the Monte Carlo (MC) and point-kernel integration methods were used to estimate the probability density function of the CDR under different extents of core damage in accident scenarios with late containment failure. Second, the results were verified by comparing the results of both methods. The point-kernel integration method results were more dispersed than the MC results, and the MC method was used for both quantitative and qualitative analyses. Quantitative analysis indicated a linear relationship, rather than the expected proportional relationship, between the CDR and core damage fraction. The CDR distribution obeyed a logarithmic normal distribution in accidents with a small break in containment, but not in accidents with a large break in containment. A possible application of our analysis is a real-time core damage estimation program based on the CDR. Keywords: Containment Radiation Monitor, Core Damage Assessment, Nuclear Power Plants, Uncertainty Analysi