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Greedy and non-greedy optimization methods have been proposed for maximizing the Value of Information (VoI) for equipment health monitoring by optimal sensors positioning. These methods provide good solutions, but still with limitations and challenges: greedy optimization does not guarantee to find the optimal solution, due to the non-submodularity of the VoI; non-greedy optimization does not suffer from the non-submodularity of the VoI but requires computationally expensive and tedious simulations to find the optimal solution. In this work, the Subset Simulation (SS) method is originally proposed to address these limitations and challenges. A real case study is considered concerning the condition monitoring of a Steam Generator (SG) of a Prototype Fast Breeder Reactor (PFBR). Results show that SS, even if initialized with a small number of Monte Carlo samples, is capable of finding the optimal set of sensors positions in a very short computational time and is insensitive to the non-submodularity of VoI.

International audience; In this work, we apply a simulation-based framework that makes use of the Value of Information (VoI) for identifying the optimal spatial positioning of sensors on pressurized equipment. VoI is a utility-based Figure of Merit (FoM) which quantifies the benefits/losses of acquiring information. Sensors are typically positioned on pressurized equipment in line with specific recommendations based on operational experience, like UNI 11096 in Italy. We show that the recommendations in UNI 11096 are, indeed, justified and that, incidentally, relying on VoI for the optimization of the sensor positioning, one can achieve the same monitoring performance, as measured by VoI, where following UNI 11096, but with a reduced number of sensors. The proposed VoI-based approach can, thus, be used to confirm or revise recommendations coming from operational experience.

This paper provides a vulnerability study for a gas production site. Decision tree analysis is implemented to provide risk-informed decisions on the maintenance strategies to minimize the unavailability and downtime costs. In this context, the decision analysis assesses the impact on the availability of the plant after having decided to buy some spare parts to mitigate the criticality of some equipment. This will help the decision makers to make an optimal decision before spending their budget on buying the spare parts while they can reliably forecast a maximize davailability. The decision analysis also will move from one site approach to the multi-site vulnerability studies. Result concludes a recommendation for the company on deciding the optimum maintenance strategies and provides a list of required spare parts to be bought in advance to reduce the associated risks and costs.

International audience; 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.

In application to thermal–hydraulic calculations by system codes, sensitivity analysis plays an important role for managing the uncertainties of code output and risk analysis. Sensitivity analysis is also used to confirm the results of qualitative Phenomena Identification and Ranking Table (PIRT). Several methodologies have been developed to address uncertainty importance assessment. Generally, uncertainty importance measures, mainly devised for the Probabilistic Risk Assessment (PRA) applications, are not affordable for computationally demanding calculations of the complex thermal–hydraulics (TH) system codes. In other words, for effective quantification of the degree of the contribution of each phenomenon to the total uncertainty of the output, a practical approach is needed by considering high computational burden of TH calculations. This study aims primarily to show the inefficiency of the existing approaches and then introduces a solution to cope with the challenges in this area by modification of variance-based uncertainty importance method. Important parameters are identified by the modified PIRT approach qualitatively then their uncertainty importance is quantified by a local derivative index. The proposed index is attractive from its practicality point of view on TH applications. It is capable of calculating the importance of parameters by a limited number of TH code executions. Application of the proposed methodology is demonstrated on LOFT-LB1 test facility.

The Level-2 probabilistic safety assessment (PSA) of pressurized water reactors studies the possibility of creep rupture for major reactor coolant system components during the course of high pressure severe accident sequences. The present paper covers this technical issue and tries to quantify its associated phenomenological uncertainties for the development of Level-2 PSA. A framework is proposed for the formal quantification of uncertainties in the Level-2 PSA model of a PWR type nuclear power plant using an integrated deterministic and PSA approach. This is demonstrated for estimation of creep rupture failure probability in station blackout severe accident of a 2-loop PWR, which is the representative case for high pressure sequences. MELCOR 1.8.6 code is employed here as the deterministic tool for the assessment of physical phenomena in the course of accident. In addition, a MATLAB code is developed for quantification of the probabilistic part by treating the uncertainties through separation of aleatory and epistemic sources of uncertainty. The probability for steam generator tube creep rupture is estimated at 0.17.

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.

Safety-related structures are designed to provide a safe environment for the occupants and equipment during and after earthquakes. This is due to the fact that any damage imposed to the systems might lead to catastrophic consequences. Seismic probabilistic risk assessment (SPRA) is a systematic approach for the quantification of the seismic risk. One of the crucial steps in this assessment is to determine the seismic capacity of the structures by fragility method. After a review of available methodologies, this article analyzes the seismic fragility for a typical power plant containment considering the effects of soil-structure interaction (SSI). The structure and underneath soil profile are analyzed as a unified model by the subtraction method. Two steps are considered for the assessment of seismic response: In the first step, a fixed-base hypothesis framework is implemented to the computational problem. The second step covers computations taking into account the SSI effects. Using the results of seismic response analysis and safety factor method, seismic fragility of the structure is computed and related fragility curves are developed. Finally, by comparing the fragility curves, the effects of SSI are quantified on the overall seismic risk.