Invariant methods for an ensemble-based sensitivity analysis of a passive containment cooling system of an AP1000 nuclear power plant

open4noSensitivity Analysis (SA) is performed to gain fundamental insights on a system behavior that is usually reproduced by a model and to identify the most relevant input variables whose variations affect the system model functional response. For the reliability analysis of passive safety systems of Nuclear Power Plants (NPPs), models are Best Estimate (BE) Thermal Hydraulic (TH) codes, that predict the system functional response in normal and accidental conditions and, in this paper, an ensemble of three alternative invariant SA methods is innovatively set up for a SA on the TH code input variables. The ensemble aggregates the input variables raking orders provided by Pearson correlation ratio, Delta method and Beta method. The capability of the ensemble is shown on a BE-TH code of the Passive Containment Cooling System (PCCS) of an Advanced Pressurized water reactor AP1000, during a Loss Of Coolant Accident (LOCA), whose output probability density function (pdf) is approximated by a Finite Mixture Model (FMM), on the basis of a limited number of simulations.Di Maio, Francesco; Nicola, Giancarlo; Borgonovo, Emanuele; Zio, EnricoDI MAIO, Francesco; Nicola, Giancarlo; Borgonovo, Emanuele; Zio, Enric

A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis

The challenge of Risk-Informed Safety Margin Characterization (RISMC) is to develop a methodology for estimating system safety margins in the presence of stochastic and epistemic uncertainties affecting the system dynamic behavior. This is useful to support decision-making for licensing purposes. In the present work, safety margin uncertainties are handled by Order Statistics (OS) (with both Bracketing and Coverage approaches) to jointly estimate percentiles of the distributions of the safety parameter and of the time required for it to reach these percentiles values during its dynamic evolution. The novelty of the proposed approach consists in the integration of dynamic aspects (i.e., timing of events) into the definition of a dynamic safety margin for a probabilistic Quantification of Margin and Uncertainties (QMU). The system here considered for demonstration purposes is the Lead-Bismuth Eutectic- eXperimental Accelerator Driven System (LBE-XADS)

How to effectively compute the reliability of a thermal-hydraulic nuclear passive system

International audienceThe computation of the reliability of a thermal-hydraulic (T-H) passive system of a nuclear power plant can be obtained by (i) Monte Carlo (MC) sampling the uncertainties of the system model and parameters, (ii) computing, for each sample, the system response by a mechanistic T-H code and (iii) comparing the system response with pre-established safety thresholds, which define the success or failure of the safety function. The computational effort involved can be prohibitive because of the large number of (typically long) T-H code simulations that must be performed (one for each sample) for the statistical estimation of the probability of success or failure. The objective of this work is to provide operative guidelines to effectively handle the computation of the reliability of a nuclear passive system. Two directions of computation efficiency are considered: from one side, efficient Monte Carlo Simulation (MCS) techniques are indicated as a means to performing robust estimations with a limited number of samples: in particular, the Subset Simulation (SS) and Line Sampling (LS) methods are identified as most valuable; from the other side, fast-running, surrogate regression models (also called response surfaces or meta-models) are indicated as a valid replacement of the long-running T-H model codes: in particular, the use of bootstrapped Artificial Neural Networks (ANNs) is shown to have interesting potentials, including for uncertainty propagation.The recommendations drawn are supported by the results obtained in an illustrative application of literature

Safety margin sensitivity analysis for model selection in nuclear power plant probabilistic safety assessment

The safety assessment of Nuclear Power Plants makes use of Thermal-Hydraulic codes for the quantification of the safety margins with respect to upper/lower safety thresholds, when postulated accidental scenarios occur. To explicitly treat uncertainties in the safety margins estimates within the Risk-Informed Safety Margin Characterization (RISMC) framework, we resort to the concept of Dynamic Probabilistic Safety Margin (DPSM). We propose to add to the framework a sensitivity analysis that calculates how much the Thermal-Hydraulic (TH) code inputs affect the DPSM, in support to the selection of the most proper probabilistic safety assessment method to be used for the problem at hand, between static or dynamic methods (e.g., Event Trees (ETs) or Dynamic ETs (DETs), respectively). Two case studies are considered: firstly a Station Black Out followed by a Seal Loss Of Coolant Accident (LOCA) for a 3-loops Pressurized Water Reactor (PWR), whose dynamics is simulated by a MAAP5 model and, secondly, the accidental scenarios that can occur in a U-Tube Steam Generator, whose dynamics is simulated by a SIMULINK model. The results show that the sensitivity analysis performed on the DPSM points out that an ET-based analysis is sufficient in one case, whereas a DET-based analysis is needed for the other case

Ensembles of climate change models for risk assessment of nuclear power plants

Climate change affects technical Systems, Structures and Infrastructures (SSIs), changing the environmental context for which SSI were originally designed. In order to prevent any risk growth beyond acceptable levels, the climate change effects must be accounted for into risk assessment models. Climate models can provide future climate data, such as air temperature and pressure. However, the reliability of climate models is a major concern due to the uncertainty in the temperature and pressure future projections. In this work, we consider five climate change models (individually unable to accurately provide historical recorded temperatures and, thus, also future projections), and ensemble their projections for integration in a probabilistic safety assessment, conditional on climate projections. As case study, we consider the Passive Containment Cooling System (PCCS) of two AP1000 Nuclear Power Plants (NPPs). Results provided by the different ensembles are compared. Finally, a risk-based classification approach is performed to identify critical future temperatures, which may lead to PCCS risks beyond acceptable levels

Guideline for Selection of Systems, Structures and Components to be considered in Ageing PSA

The guideline intends to provide a practical approach and to recommend the methods to be used in selection/prioritization of components, systems and structures (SSC) sensitive to ageing and important from risk point of view in operating nuclear power plants. The approach intends to ensure that the selection process will be carried out and documented in a uniform and consistent manner. The methods suitable for selection are briefly presented, and their advantages and disadvantages are specified. A list of generic ageing mechanisms, the factors favorable for their occurrence and some sensitive materials are provided in appendices. In the appendices are presented also the specific approaches and criteria used for SSC prioritization and selection in case studies performed in the frame of Ageing PSA task 3 activities. The guideline was developed in the frame of EC JRC Ageing PSA Network (APSA) activities.JRC.DDG.F.5-Safety of present nuclear reactor