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Special Issue of ESREL2020 PSAM15

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (15 June 2021) | Viewed by 18872

Special Issue Editors


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Guest Editor
Energy Department, Politecnico di Milano, 20156 Milano, Italy
Interests: development of methods and techniques for system health monitoring, fault diagnostics, prognostics and maintenance; methodologies for rationally handling the uncertainty and ambiguity in the information

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Guest Editor
1. Department of Electrical and Computer Engineering, University of Windsor, Canada
2. School of Computer Science, University of Windsor, Windsor, ON, Canada
Interests: machine learning; big data analytics; cybernetics; diagnostics; and prognostics of safety-critical and cyber-physical systems

Special Issue Information

This Special Issue includes a selection of papers originated from contributions to the ESREL2020 PSAM15 Conference, which combines the 30th European Safety and Reliability Conference (ESREL 2020) and the 15th Probabilistic Safety Assessment and Management Conference (PSAM 15), which was held in Venice, Italy, during the dates November 1–5, 2020.

This Conference takes place only every eight years (Crete 1996, Berlin 2004, Helsinki 2012, and Venice 2020) and brings together the top experts of the world in the science and practice of reliability, safety and security. It is a unique opportunity to advance knowledge in all fields of reliability, safety and security, by sharing achievements and challenges. It provides a forum to discuss scientific methodologies and technical solutions for the reliable design and operation of components and systems, and for the prevention and management of risk in complex systems and critical infrastructures.

The program of the Conference has consisted of 728 abstracts and papers selected through a peer-review process conducted by more than 130 Track Directors, who have organized the work of more than 800 reviewers.

This Special Issue includes selected papers related to the following conference thematic areas: electric vehicles, electric power industry, energy industry, nuclear industry, oil and gas industry, renewable energy industry.

Prof. Dr. Piero Baraldi
Dr. Roozbeh Razavi-Far
Prof. Dr. Enrico Zio
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • reliability
  • risk
  • safety
  • security
  • maintenance
  • prognostics and health management
  • electric vehicles
  • electric power industry
  • energy industry
  • nuclear industry
  • oil and gas industry
  • renewable energy industry

Published Papers (8 papers)

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Editorial

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2 pages, 173 KiB  
Editorial
Guest Editorial: Special Issue of ESREL2020 PSAM15
by Piero Baraldi, Roozbeh Razavi-Far and Enrico Zio
Energies 2023, 16(4), 1610; https://doi.org/10.3390/en16041610 - 6 Feb 2023
Viewed by 950
Abstract
This Special Issue includes seven extended works that have been selected from papers presented at the ESREL 2020 PSAM 15 Conference, the 30th European Safety and Reliability Conference (ESREL 2020) and the 15th Probabilistic Safety Assessment and Management Conference (PSAM 15), jointly held [...] Read more.
This Special Issue includes seven extended works that have been selected from papers presented at the ESREL 2020 PSAM 15 Conference, the 30th European Safety and Reliability Conference (ESREL 2020) and the 15th Probabilistic Safety Assessment and Management Conference (PSAM 15), jointly held virtually on 1–5 November 2020 to discuss the second wave of the pandemic breakout [...] Full article
(This article belongs to the Special Issue Special Issue of ESREL2020 PSAM15)

Research

Jump to: Editorial

16 pages, 4069 KiB  
Article
Quantification of Uncertainty in CFD Simulation of Accidental Gas Release for O & G Quantitative Risk Assessment
by Fabrizio Pappalardo, Alberto Moscatello, Gianmario Ledda, Anna Chiara Uggenti, Raffaella Gerboni, Andrea Carpignano, Francesco Di Maio, Riccardo Mereu and Enrico Zio
Energies 2021, 14(23), 8117; https://doi.org/10.3390/en14238117 - 3 Dec 2021
Cited by 6 | Viewed by 1957
Abstract
Quantitative Risk Assessment (QRA) of Oil & Gas installations implies modeling accidents’ evolution. Computational Fluid Dynamics (CFD) is one way to do this, and off-the-shelf tools are available, such as FLACS developed by Gexcon US and KFX developed by DNV-GL. A recent model [...] Read more.
Quantitative Risk Assessment (QRA) of Oil & Gas installations implies modeling accidents’ evolution. Computational Fluid Dynamics (CFD) is one way to do this, and off-the-shelf tools are available, such as FLACS developed by Gexcon US and KFX developed by DNV-GL. A recent model based on ANSYS Fluent, named SBAM (Source Box Accident Model) was proposed by the SEADOG lab at Politecnico di Torino. In this work, we address one major concern related to the use of CFD tools for accident simulation, which is the relevant computational demand that limits the number of simulations that can be performed. This brings with it the challenge of quantifying the uncertainty of the results obtained, which requires performing a large number of simulations. Here we propose a procedure for the Uncertainty Quantification (UQ) of FLACX, KFX and SBAM, and show its performance considering an accidental high-pressure methane release scenario in a realistic offshore Oil & Gas (O & G) platform deck. The novelty of the work is that the UQ of the CFD models, which is performed relying on well-consolidated approaches such as the Grid Convergence Index (GCI) method and a generalization of Richardson’s extrapolation, is originally propagated to a set of risk measures that can be used to support the decision-making process to prevent/mitigate accidental scenarios. Full article
(This article belongs to the Special Issue Special Issue of ESREL2020 PSAM15)
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17 pages, 2933 KiB  
Article
Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews
by Luca Pinciroli, Piero Baraldi, Guido Ballabio, Michele Compare and Enrico Zio
Energies 2021, 14(20), 6743; https://doi.org/10.3390/en14206743 - 16 Oct 2021
Cited by 17 | Viewed by 2397
Abstract
The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) [...] Read more.
The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) by combining the (uncertain) information provided by PHM with the other factors influencing O&M activities, including the limited availability of maintenance crews, the variability of energy demand and corresponding production requests, and the long-time horizons of energy systems operation. In this work, we consider the operation and maintenance optimization of wind turbines in wind farms woth multiple crews. A new formulation of the problem as a sequential decision problem over a long-time horizon is proposed and solved by deep reinforcement learning based on proximal policy optimization. The proposed method is applied to a wind farm of 50 turbines, considering the availability of multiple maintenance crews. The optimal O&M policy found outperforms other state-of-the-art strategies, regardless of the number of available maintenance crews. Full article
(This article belongs to the Special Issue Special Issue of ESREL2020 PSAM15)
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19 pages, 4781 KiB  
Article
Identification of Critical Components in the Complex Technical Infrastructure of the Large Hadron Collider Using Relief Feature Ranking and Support Vector Machines
by Ahmed Shokry, Piero Baraldi, Andrea Castellano, Luigi Serio and Enrico Zio
Energies 2021, 14(18), 6000; https://doi.org/10.3390/en14186000 - 21 Sep 2021
Cited by 2 | Viewed by 1931
Abstract
This work proposes a data-driven methodology for identifying critical components in Complex Technical Infrastructures (CTIs), for which the functional logic and/or the system structure functions are not known due the CTI’s complexity and evolving nature. The methodology uses large amounts of CTI monitoring [...] Read more.
This work proposes a data-driven methodology for identifying critical components in Complex Technical Infrastructures (CTIs), for which the functional logic and/or the system structure functions are not known due the CTI’s complexity and evolving nature. The methodology uses large amounts of CTI monitoring data acquired over long periods of time and under different operating conditions. The critical components are identified as those for which the condition monitoring signals permit the optimal classification of the CTI functioning or failed state. The methodology includes two stages: in the first stage, a feature selection filter method based on the Relief technique is used to rank the monitoring signals according to their importance with respect to the CTI functioning or failed state; the second stage identifies the subset of signals among those highlighted by the Relief technique that are most informative with respect to the CTI state. This identification is performed on the basis of evaluating the performance of a Cost-Sensitive Support Vector Machine (CS-SVM) classifier trained with several subsets of the candidate signals. The capabilities of the methodology proposed are assessed through its application to different benchmarks of highly imbalanced datasets, showing performances that are competitive to those obtained by other methods presented in the literature. The methodology is finally applied to the monitoring signals of the Large Hadron Collider (LHC) of the European Organization for Nuclear Research (CERN), a CTI for experiments of physics; the criticality of the identified components has been confirmed by CERN experts. Full article
(This article belongs to the Special Issue Special Issue of ESREL2020 PSAM15)
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37 pages, 10105 KiB  
Article
Metamodeling and On-Line Clustering for Loss-of-Flow Accident Precursors Identification in a Superconducting Magnet Cryogenic Cooling Circuit
by Vincenzo Destino, Nicola Pedroni, Roberto Bonifetto, Francesco Di Maio, Laura Savoldi and Enrico Zio
Energies 2021, 14(17), 5552; https://doi.org/10.3390/en14175552 - 5 Sep 2021
Cited by 2 | Viewed by 1738
Abstract
In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at the cryogenic temperature of 4.5 K by one or more Superconducting Magnet Cryogenic Cooling Circuits (SMCCC). To guarantee cooling, Loss-of-Flow Accidents (LOFAs) in the [...] Read more.
In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at the cryogenic temperature of 4.5 K by one or more Superconducting Magnet Cryogenic Cooling Circuits (SMCCC). To guarantee cooling, Loss-of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, we develop a three-step methodology for the prompt detection of LOFA precursors (i.e., those combinations of component failures causing a LOFA). First, we randomly generate accident scenarios by Monte Carlo sampling of the failures of typical SMCCC components and simulate the corresponding transient system response by a deterministic thermal-hydraulic code. In this phase, we also employ quick-running Proper Orthogonal Decomposition (POD)-based Kriging metamodels, adaptively trained to reproduce the output of the long-running code, to decrease the computational time. Second, we group the generated scenarios by a Spectral Clustering (SC) employing the Fuzzy C-Means (FCM), in order to identify the main patterns of system evolution towards abnormal states (e.g., a LOFA). Third, we develop an On-line Supervised Spectral Clustering (OSSC) technique to associate time-varying parameters measured during plant functioning to one of the prototypical groups obtained, which may highlight the related LOFA precursors (in terms of SMCCC components failures). We apply the proposed technique to the simplified model of a cryogenic cooling circuit of a single module of the ITER Central Solenoid Magnet (CSM). The framework developed promptly detects 95% of LOFA events and around 80% of the related precursors. Full article
(This article belongs to the Special Issue Special Issue of ESREL2020 PSAM15)
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18 pages, 889 KiB  
Article
An Expert-Driven Probabilistic Assessment of the Safety and Security of Offshore Wind Farms
by Oscar Hernán Ramírez-Agudelo, Corinna Köpke, Yann Guillouet, Jan Schäfer-Frey, Evelin Engler, Jennifer Mielniczek and Frank Sill Torres
Energies 2021, 14(17), 5465; https://doi.org/10.3390/en14175465 - 2 Sep 2021
Cited by 5 | Viewed by 2159
Abstract
Offshore wind farms (OWFs) are important infrastructure which provide an alternative and clean means of energy production worldwide. The offshore wind industry has been continuously growing. Over the years, however, it has become evident that OWFs are facing a variety of safety and [...] Read more.
Offshore wind farms (OWFs) are important infrastructure which provide an alternative and clean means of energy production worldwide. The offshore wind industry has been continuously growing. Over the years, however, it has become evident that OWFs are facing a variety of safety and security challenges. If not addressed, these issues may hinder their progress. Based on these safety and security goals and on a Bayesian network model, this work presents a methodological approach for structuring and organizing expert knowledge and turning it into a probabilistic model to assess the safety and security of OWFs. This graphical probabilistic model allowed us to create a high-level representation of the safety and security state of a generic OWF. By studying the interrelations between the different functions of the model, and by proposing different scenarios, we determined the impacts that a failing function may have on other functions in this complex system. Finally, this model helped us define the performance requirements of such infrastructure, which should be beneficial for optimizing operation and maintenance. Full article
(This article belongs to the Special Issue Special Issue of ESREL2020 PSAM15)
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15 pages, 628 KiB  
Article
Liquid Hydrogen Spills on Water—Risk and Consequences of Rapid Phase Transition
by Lars H. Odsæter, Hans L. Skarsvåg, Eskil Aursand, Federico Ustolin, Gunhild A. Reigstad and Nicola Paltrinieri
Energies 2021, 14(16), 4789; https://doi.org/10.3390/en14164789 - 6 Aug 2021
Cited by 14 | Viewed by 3314
Abstract
Liquid hydrogen (LH2) spills share many of the characteristics of liquefied natural gas (LNG) spills. LNG spills on water sometimes result in localized vapor explosions known as rapid phase transitions (RPTs), and are a concern in the LNG industry. LH2 [...] Read more.
Liquid hydrogen (LH2) spills share many of the characteristics of liquefied natural gas (LNG) spills. LNG spills on water sometimes result in localized vapor explosions known as rapid phase transitions (RPTs), and are a concern in the LNG industry. LH2 RPT is not well understood, and its relevance to hydrogen safety is to be determined. Based on established theory from LNG research, we present a theoretical assessment of an accidental spill of a cryogen on water, including models for pool spreading, RPT triggering, and consequence quantification. The triggering model is built upon film-boiling theory, and predicts that the mechanism for RPT is a collapse of the gas film separating the two liquids (cryogen and water). The consequence model is based on thermodynamical analysis of the physical processes following a film-boiling collapse, and is able to predict peak pressure and energy yield. The models are applied both to LNG and LH2, and the results reveal that (i) an LNG pool will be larger than an LH2 pool given similar sized constant rate spills, (ii) triggering of an LH2 RPT event as a consequence of a spill on water is very unlikely or even impossible, and (iii) the consequences of a hypothetical LH2 RPT are small compared to LNG RPT. Hence, we conclude that LH2 RPT seems to be an issue of only minor concern. Full article
(This article belongs to the Special Issue Special Issue of ESREL2020 PSAM15)
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18 pages, 2349 KiB  
Article
Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components
by Zhaojun Hao, Francesco Di Maio and Enrico Zio
Energies 2021, 14(11), 3241; https://doi.org/10.3390/en14113241 - 1 Jun 2021
Cited by 8 | Viewed by 2854
Abstract
Cyber-Physical Energy Systems (CPESs) are energy systems which rely on cyber components for energy production, transmission and distribution control, and other functions. With the penetration of Renewable Energy Sources (RESs), CPESs are required to provide flexible operation (e.g., load-following, frequency regulation) to respond [...] Read more.
Cyber-Physical Energy Systems (CPESs) are energy systems which rely on cyber components for energy production, transmission and distribution control, and other functions. With the penetration of Renewable Energy Sources (RESs), CPESs are required to provide flexible operation (e.g., load-following, frequency regulation) to respond to any sudden imbalance of the power grid, due to the variability in power generation by RESs. This raises concerns on the reliability of CPESs traditionally used as base-load facilities, such as Nuclear Power Plants (NPPs), which were not designed for flexible operation, and more so, since traditionally only hardware components aging and stochastic failures have been considered for the reliability assessment, whereas the contribution of the degradation and aging of the cyber components of CPSs has been neglected. In this paper, we propose a multi-state model that integrates the hardware components stochastic failures with the aging of cyber components, and quantify the unreliability of CPES in load-following operations under normal/emergency conditions. To show the application of the reliability assessment model, we consider the case of the Control Rod System (CRS) of a NPP typically used for a base-load energy supply. Full article
(This article belongs to the Special Issue Special Issue of ESREL2020 PSAM15)
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