Title: A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System
Journal: Reliability Engineering & System Safety
Authors: L. Puppo, N. Pedroni, Francesco di Maio, A. Bersano, C. Bertani, Enrico Zio
This is an open-access article that can be downloaded from the following link before October 17: https://authors.elsevier.com/c/1dfEy3OQ~fVt7D
Abstract:
In the safety analyses of passive systems for nuclear energy applications, computationally demanding models can be substituted by fast-running surrogate models coupled with adaptive sampling techniques; for speeding up the exploration of the components and system state-space and the characterization of the conditions leading to failure (i.e., the system Critical failure Regions, CRs). However, in some cases of non-smoothness and multimodality of the state-space, the existing approaches do not suffice. In this paper, we propose a novel methodological framework, based on Finite Mixture Models (FMMs) and Adaptive Kriging (AK-MCS) for CRs characterization in case of non-smoothness and/or multimodality of the output. The framework contains three main steps: 1) dimensionality reduction through FMMs to tackle the output non-smoothness and multimodality, while focusing on its clusters defining the system failure; 2) adaptive training (AK-MCS) of the metamodel on the reduced space to mimic the time-demanding model and, finally, 3) use of the trained metamodel provide the output for new input combinations and retrieve information about the CRs.
The framework is applied to the case study of a generic Passive Safety System (PSS) for Decay Heat Removal (DHR) designed for advanced Nuclear Power Plants (NPPs). The PSS operation is modelled through a time-demanding Thermal-Hydraulic (T-H) model and the pressure selected for characterizing the PSS response to accidental conditions shows a strong non-smooth and multimodal behavior. A comparison with an alternative approach of literature relying on the use of Support Vector Classifier (SVC) to cluster the output domain is presented to support the framework as a valid approach in challenging CRs characterization.
Title: Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks
Journal: Reliability Engineering & System Safety
Authors: Francesco di Maio, O. SCAPINELLO, Enrico Zio, C. CIARAPICA, S. CINCOTTA, A. CRIVELLARI, L. DECARLI, L. LAROSA
This is an open-access article that can be downloaded from the following link before September 22: https://authors.elsevier.com/a/1dWEL3OQ%7EfVt4Z
Abstract:
In this paper, a multistate Bayesian Network (BN) is proposed to model and evaluate the functional performance of safety barriers in Oil and Gas plants. The nodes of the BN represent the safety barriers Health States (HSs) and the corresponding conditional Failure Probability (FP) values are assigned. HSs are assessed on the basis of specific Key Performance Indicators (KPIs) related to the barrier characteristics (i.e., technical, procedural or organizational, continuously monitored or event-based characterized). FP values are estimated from failure datasets (for technical barriers), evaluated by Human Reliability Analysis (HRA) (for operational and organizational barriers) and assigned by expert elicitation (for barriers lacking data or information). For illustration, the multistate BN model is developed for preventive barriers and applied to a case study related to the potential release of flammable material in the slug catcher of a representative O&G Upstream plant which may lead to major accident scenarios (fire, explosion, toxic dispersion). The results from the case study demonstrate that the multistate BN model is able to account for the safety barriers HS and their associated functional performance.
