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Enrico Zio - One of the best experts on this subject based on the ideXlab platform.

  • joint optimization of safety barriers for enhancing business continuity of nuclear power plants against Steam Generator Tube ruptures accidents
    Reliability Engineering & System Safety, 2020
    Co-Authors: Jinduo Xing, Zhiguo Zeng, Enrico Zio
    Abstract:

    Abstract In nuclear power plants (NPPs), different types of safety barriers are designed to ensure the safe and continuous operation of the NPP against disruptive events. These safety barriers, although designed to operate in different phases of the accidents evolution, are often optimized separately, without considering their collective effects on preventing disruptions and quickly recovering from the disruptions. This paper develops a joint optimization model for synthetically optimizing safety barriers of different natures, including prevention, mitigation, emergency and recovery barriers to enhance the business continuity of the NPP, considering the threat of Steam Generator Tube rupture (SGTR) accidents. The joint optimization is guided by a business continuity metric called expected business continuity value (EBCV). A physics-of-failure model is developed to describe the crack growth process of the Steam Generator Tube and to model the effect of the prevention barriers, i.e., periodical inspection of the crack length. An event tree model is developed to describe the evolution of the SGTR-initiated accident and to model the effect of the mitigation and emergency barriers. Recovery measures are also considered via a widely-used logarithmic function model. A mixed-integer genetic algorithm (MIGA) is used to obtain optimal solutions of the joint optimization model. The results show that the developed joint optimization model can achieve better performance in terms of business continuity, compared to the conventional methods that optimize the safety barriers separately.

  • Condition-based probabilistic safety assessment for maintenance decision making regarding a nuclear power plant Steam Generator undergoing multiple degradation mechanisms
    Reliability Engineering and System Safety, 2019
    Co-Authors: Seyed Mohsen Hoseyni, Francesco Di Maio, Enrico Zio
    Abstract:

    Condition-Based Probabilistic Safety Assessment (CB-PSA) makes use of inspections and monitoring information on Systems, Structures, and Components (SSCs) to update risk quantities. In this paper, we show the benefits of exploiting the condition-based estimates for taking maintenance decisions on a SSC undergoing multiple degradation mechanisms. To develop the method, we make reference to a spontaneous Steam Generator Tube Rupture (SGTR) Accident Scenario in a Nuclear Power Plant (NPP). The SG is susceptible to multiple degradation mechanisms, i.e., Stress Corrosion Cracking (SCC) and pitting. Tube plugging and Water Lancing and Chemical Cleaning (WL-CC) can be performed, before leading to a SGTR accident. Decisions must be taken on the maintenance strategy to perform at each inspection cycle. Results of a case study regarding SGTR show that the decisions based on the risk estimates provided by a CB-PSA approach allow controlling the SGTR risk at minimum maintenance cost.

  • identification and classification of dynamic event tree scenarios via possibilistic clustering application to a Steam Generator Tube rupture event
    Accident Analysis & Prevention, 2009
    Co-Authors: D Mercurio, Enrico Zio, Luca Podofillini, Vinh N Dang
    Abstract:

    This paper illustrates a method to identify and classify scenarios generated in a dynamic event tree (DET) analysis. Identification and classification are carried out by means of an evolutionary possibilistic fuzzy C-means clustering algorithm which takes into account not only the final system states but also the timing of the events and the process evolution. An application is considered with regards to the scenarios generated following a Steam Generator Tube rupture in a nuclear power plant. The scenarios are generated by the accident dynamic simulator (ADS), coupled to a RELAP code that simulates the thermo-hydraulic behavior of the plant and to an operators' crew model, which simulates their cognitive and procedures-guided responses. A set of 60 scenarios has been generated by the ADS DET tool. The classification approach has grouped the 60 scenarios into 4 classes of dominant scenarios, one of which was not anticipated a priori but was "discovered" by the classifier. The proposed approach may be considered as a first effort towards the application of identification and classification approaches to scenarios post-processing for real-scale dynamic safety assessments.

Zio Enrico - One of the best experts on this subject based on the ideXlab platform.

Seyed Mohsen Hoseyni - One of the best experts on this subject based on the ideXlab platform.

  • Condition-based probabilistic safety assessment for maintenance decision making regarding a nuclear power plant Steam Generator undergoing multiple degradation mechanisms
    Reliability Engineering and System Safety, 2019
    Co-Authors: Seyed Mohsen Hoseyni, Francesco Di Maio, Enrico Zio
    Abstract:

    Condition-Based Probabilistic Safety Assessment (CB-PSA) makes use of inspections and monitoring information on Systems, Structures, and Components (SSCs) to update risk quantities. In this paper, we show the benefits of exploiting the condition-based estimates for taking maintenance decisions on a SSC undergoing multiple degradation mechanisms. To develop the method, we make reference to a spontaneous Steam Generator Tube Rupture (SGTR) Accident Scenario in a Nuclear Power Plant (NPP). The SG is susceptible to multiple degradation mechanisms, i.e., Stress Corrosion Cracking (SCC) and pitting. Tube plugging and Water Lancing and Chemical Cleaning (WL-CC) can be performed, before leading to a SGTR accident. Decisions must be taken on the maintenance strategy to perform at each inspection cycle. Results of a case study regarding SGTR show that the decisions based on the risk estimates provided by a CB-PSA approach allow controlling the SGTR risk at minimum maintenance cost.

  • Success criteria analysis in support of probabilistic risk assessment for nuclear power plants: application on SGTR accident
    Nuclear Science and Techniques, 2017
    Co-Authors: Seyed Mohsen Hoseyni, Kaveh Karimi, Meisam Mohammadnia
    Abstract:

    Success criteria analysis (SCA) bridges the gap between deterministic and probabilistic approaches for risk assessment of complex systems. To develop a risk model, SCA evaluates systems behaviour in response to postulated accidents using deterministic approach to provide required information for the probabilistic model. A systematic framework is proposed in this article for extracting the front line systems success criteria. In this regard, available approaches are critically reviewed and technical challenges are discussed. Application of the proposed methodology is demonstrated on a typical Westinghouse-type nuclear power plant. Steam Generator Tube rupture is selected as the postulated accident. The methodology is comprehensive and general; therefore, it can be implemented on the other types of plants and complex systems.

Jibo Tan - One of the best experts on this subject based on the ideXlab platform.

Enhou Han - One of the best experts on this subject based on the ideXlab platform.