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.
RECET4Rail Kicks off
Polimi is a partner of the project RECET4Rail (Reliable Energy and Cost Efficient Traction system for Railway) which is a collaborative project aiming at improving rail traction sub-systems, under the Shift2Rail Joint Undertaking (JU) Programme funded by the European Union. The project involves 13 partners from 8 EU countries, sharing the common goal to research and innovate for the reliability and efficiency of rail traction systems.
Title: A Feature Selection-Based Approach for the Identification of Critical Components in Complex Technical Infrastructures: Application to the CERN Large Hadron Collider
Journal: Reliability Engineering and System Safety
Authors: Piero Baraldi, Andrea Castellano, Ahmed Shokry, Ugo Gentile, Luigi Serio, Enrico Zio
Free download until July 15th , 2020: https://authors.elsevier.com/c/1b7ig3OQ~fLcRF
Abstract: Complex Technical Infrastructures (CTIs) are large-scale systems made of tens of thousands of interdependent components organized in complex hierarchical architectures. They evolve in time in a way that at one point their functional logic may be more complex than originally designed, and, therefore, traditional reliability/risk importance measures cannot be used for identifying the critical components on which the protection and recovery efforts should be primarily allocated. We propose an approach for identifying the most critical components based on the large amount of operational data collected from the CTI monitoring systems over long time periods and under different operational settings. The underlying idea is to develop binary classifiers to associate different combinations of measured signals to the CTI operating or failed state. The critical CTI components are those whose condition monitoring signals allow optimally classifying the CTI state. To identify the signals and to build the classifier, we consider a feature selection wrapper approach based on the combined use of Support Vector Machine classifiers and the Binary Differential Evolution algorithm for optimization. The approach is successfully applied to a real dataset collected from the CERN (European Centre for Nuclear Research) Large Hadron Collider, a CTI for experiments of physics.
Congratulations to Mingjing Xu! He won the best student poster award at the “European Safety and Reliability Conference (ESREL 2019)”
The work has been done during his PhD in the Laboratory of Signal and Risk Analysis of Politecnico di Milano (LASAR).
Congratulation to Riccardo Borghi! He won the best presentation award at the “Offshore Mediterranean Conference”
The work has been done during his master thesis within a collaboration between the Laboratory of Signal and Risk Analysis of Politecnico di Milano (LASAR) and ENI S.p.A.
Congratulation to Dr. Francesco Cannarile! He has been awarded a PhD title in “mathematical models and methods in engineering from Politecnico di Milano”
The website of the project
funded by INAIL has been launched. Lasar is a member of the project consortium. More information at: https://site.unibo.it/mac4pro/it
CONGRATULATIONS TO FRANCESCO DI MAIO FOR THE PROMOTION TO ASSOCIATE PROFESSOR