NEW PUBLICATION !
Title: A Bayesian framework of inverse uncertainty quantification with principal component analysis and Kriging for the reliability analysis of passive safety systems
Journal: Nuclear Engineering and Design
Authors: Giovanni Roma, Francesco Di Maio, Andrea Bersano, Nicola Pedroni, Cristina Bertani, Fulvio Mascari, Enrico Zio
Free download until June 8th, 2021: https://authors.elsevier.com/c/1cx276j-yo1jX
Abstract: In this work, we propose an Inverse Uncertainty Quantification (IUQ) approach to assigning Probability Density Functions (PDFs) to uncertain input parameters of Thermal-Hydraulic (T-H) models used to assess the reliability of passive safety systems. The approach uses experimental data within a Bayesian framework. The application to a RELAP5-3D model of the PERSEO (In-Pool Energy Removal System for Emergency Operation) facility located at SIET laboratory (Piacenza, Italy) is demonstrated. Principal Component Analysis (PCA) is applied for output dimensionality reduction and Kriging meta-modeling is used to emulate the reduced set of RELAP5-3D code outputs. This is done to decrease the computational cost of the Markov Chain Monte Carlo (MCMC) posterior sampling of the uncertain input parameters, which requires a large number of model simulations.
Title: Association rules extraction for the identification of functional dependencies in complex technical infrastructures
Journal: Reliability Engineering & System Safety
Authors: Federico Antonello, Piero Baraldi, Ahmed Shokry, Enrico Zio, U. Gentile, L. Serio
Free download: https://authors.elsevier.com/a/1cZf%7E3OQ%7EfTD0i
Abstract: This work proposes a method for identifying functional dependencies among components of complex technical infrastructures using databases of alarm messages. The developed method is based on the representation of the alarm database by a binary matrix, the use of the Apriori algorithm for mining association rules and a new algorithm for identifying groups of functionally dependent components. The effectiveness of the proposed method is shown by means of its application to an artificial case study and a real large-scale database of alarms generated by different supervision systems of the complex technical infrastructure of CERN (European Organization for Nuclear Research).
Title: Optimal sensor positioning on pressurized equipment based on Value of Information
Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Authors: Seyed Mojtaba Hoseyni, Francesco Di Maio, Enrico Zio
Abstract: 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.
Title: Identification of LOFA precursors in ITER superconducting magnet cryogenic cooling circuit
Journal: Reliability Engineering & System Safety
Authors: V. Destino, R. Bonifetto, F. Di Maio, N. Pedroni, R. Zanino, E. Zio
Free download until March 09, 2021: https://authors.elsevier.com/c/1cR1j_Lf6GyXK-
Abstract: In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at cryogenic temperature by a Superconducting Magnet Cryogenic Cooling Circuit (SMCCC). To guarantee cooling, Loss-Of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, an approach to identify LOFA precursors (i.e., those component failures leading to a LOFA) is presented. The approach is based on a Spectral Clustering (SC) method using the Fuzzy C-Means (FCM) algorithm and is applied to the SMCCC of a single module of the ITER Central Solenoid (CS).
Title: A semi-supervised method for the characterization of degradation of nuclear power plants steam generators
Journal: Progress in Nuclear Energy
Authors: Luca Pinciroli, Piero Baraldi, Ahmed Shokry, Enrico Zio, Redouane Seraoui, Carole Mai
Free download until January 23rd , 2021: https://authors.elsevier.com/c/1cBEaXx6ZPO~l
Abstract: The digitalization of nuclear power plants, with the rapid growth of information technology, opens the door to the development of new methods of condition-based maintenance. In this work, a semi-supervised method for characterizing the level of degradation of nuclear power plant components using measurements collected during plant operational transients is proposed. It is based on the fusion of selected features extracted from the monitored signals. Feature selection is formulated as a multi-objective optimization problem. The objectives are the maximization of the feature monotonicity and trendability, and the maximization of a novel measure of correlation between the feature values and the results of non-destructive tests performed to assess the component degradation. The features of the Pareto optimal set are normalized and the component degradation level is defined as the median of the obtained values. The developed method is applied to real data collected from steam generators of pressurized water reactors. It is shown able to identify degradation level with errors comparable to those obtained by ad-hoc non-destructive tests.