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2 pages, 173 KiB

Open AccessEditorial

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 - 06 Feb 2023

Viewed by 853

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

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16 pages, 4069 KiB

Open AccessArticle

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 - 03 Dec 2021

Cited by 6 | Viewed by 1841

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

Open AccessArticle

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 2255

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

Open AccessArticle

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 1835

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

Open AccessArticle

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 - 05 Sep 2021

Cited by 2 | Viewed by 1662

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

Open AccessArticle

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 - 02 Sep 2021

Cited by 5 | Viewed by 2012

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

Open AccessArticle

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 - 06 Aug 2021

Cited by 14 | Viewed by 3117

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

Open AccessArticle

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 - 01 Jun 2021

Cited by 8 | Viewed by 2725

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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