This page has only limited features, please log in for full access.
Passive systems are fundamental for the safe development of Nuclear Power Plant (NPP) technology. The accurate assessment of their reliability is crucial for their use in the nuclear industry. In this paper, we present a review of the approaches and procedures for the reliability assessment of passive systems. We complete the work by discussing the pending open issues, in particular with respect to the need of novel sensitivity analysis methods, the role of empirical modelling and the integration of passive safety systems assessment in the (static/dynamic) Probabilistic Safety Assessment (PSA) framework.
Francesco Di Maio; Nicola Pedroni; Barnabás Tóth; Luciano Burgazzi; Enrico Zio. Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues. Energies 2021, 14, 4688 .
AMA StyleFrancesco Di Maio, Nicola Pedroni, Barnabás Tóth, Luciano Burgazzi, Enrico Zio. Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues. Energies. 2021; 14 (15):4688.
Chicago/Turabian StyleFrancesco Di Maio; Nicola Pedroni; Barnabás Tóth; Luciano Burgazzi; Enrico Zio. 2021. "Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues." Energies 14, no. 15: 4688.
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.
F. Dimaio; O. Scapinello; E. Zio; C. Ciarapica; S. Cincotta; A. Crivellari; L. Decarli; L. Larosa. Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks. Reliability Engineering & System Safety 2021, 216, 107943 .
AMA StyleF. Dimaio, O. Scapinello, E. Zio, C. Ciarapica, S. Cincotta, A. Crivellari, L. Decarli, L. Larosa. Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks. Reliability Engineering & System Safety. 2021; 216 ():107943.
Chicago/Turabian StyleF. Dimaio; O. Scapinello; E. Zio; C. Ciarapica; S. Cincotta; A. Crivellari; L. Decarli; L. Larosa. 2021. "Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks." Reliability Engineering & System Safety 216, no. : 107943.
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.
Zhaojun Hao; Francesco Di Maio; Enrico Zio. Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components. Energies 2021, 14, 3241 .
AMA StyleZhaojun Hao, Francesco Di Maio, Enrico Zio. Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components. Energies. 2021; 14 (11):3241.
Chicago/Turabian StyleZhaojun Hao; Francesco Di Maio; Enrico Zio. 2021. "Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components." Energies 14, no. 11: 3241.
The ultimate barrier to prevent contamination of the environment due to a release of radioactivity from a Nuclear Power Plant (NPP) is the reinforced concrete (RC) Reactor Building (RB) which encloses the nuclear reactor. The integrity of this barrier is the main focus of Probabilistic Risk Assessment (PRA)-Level 2, in which accident scenarios that might affect this barrier are modeled in terms of their consequences and their probabilities of occurrence. Traditionally, aging and degradation of the RB are not explicitly considered in the modeling. In this paper, a time-dependent reliability approach is adopted to explicitly model aging and degradation, and the effects on the RB resistance to the accidental stresses and eventually its failure probability. A Finite Element Model (FEM) of the RC is developed and coupled with a degradation model. By this, risk measures, like the Large Early Release Frequency (LERF) and its increase in time due to aging (ΔLERF), are actualized on the basis of the condition monitoring data related to the reactor building and the time-dependent risk of failure is quantified. A case study of an internal overpressure due to a hydrogen explosion is considered to exemplify the methodology.
Di Maio Francesco; Fumagalli Matteo; Guerini Carlo; Perotti Federico; Zio Enrico. Time-dependent reliability analysis of the reactor building of a nuclear power plant for accounting of its aging and degradation. Reliability Engineering & System Safety 2020, 205, 107173 .
AMA StyleDi Maio Francesco, Fumagalli Matteo, Guerini Carlo, Perotti Federico, Zio Enrico. Time-dependent reliability analysis of the reactor building of a nuclear power plant for accounting of its aging and degradation. Reliability Engineering & System Safety. 2020; 205 ():107173.
Chicago/Turabian StyleDi Maio Francesco; Fumagalli Matteo; Guerini Carlo; Perotti Federico; Zio Enrico. 2020. "Time-dependent reliability analysis of the reactor building of a nuclear power plant for accounting of its aging and degradation." Reliability Engineering & System Safety 205, no. : 107173.
In this work, we consider diagnostics of cyber attacks in Cyber-Physical Systems (CPSs), based on data analytics. For the first time to authors knowledge, the performance of such diagnosis is quantified considering the possible failure of the human operator cognitive process in interpreting and understanding the diagnosis support tool outcomes. A Non-Parametric CUmulative SUM (NP-CUSUM) approach is used for data-driven diagnostic, and the cognitive process of the human operator who interprets its outputs is modeled by a Bayesian Belief Network (BBN). The overall framework is applied on the digital controller of the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).
Wei Wang; Francesco Di Maio; Enrico Zio. Considering the human operator cognitive process for the interpretation of diagnostic outcomes related to component failures and cyber security attacks. Reliability Engineering & System Safety 2020, 202, 107007 .
AMA StyleWei Wang, Francesco Di Maio, Enrico Zio. Considering the human operator cognitive process for the interpretation of diagnostic outcomes related to component failures and cyber security attacks. Reliability Engineering & System Safety. 2020; 202 ():107007.
Chicago/Turabian StyleWei Wang; Francesco Di Maio; Enrico Zio. 2020. "Considering the human operator cognitive process for the interpretation of diagnostic outcomes related to component failures and cyber security attacks." Reliability Engineering & System Safety 202, no. : 107007.
Variance decomposition is an effective sensitivity analysis method to screen the key parameters influencing passive safety system operation based on the uncertainties of thermal-hydraulic (T-H) model inputs. However, such method needs a large number of samples gained from T-H model running with the inputs sampled randomly from their probabilistic distributions, and the T-H model always takes quite long time to run once, then it will be a heavy calculation burden to do the analysis. In this paper, we propose a method to improve the analyzing efficient: based on the system T-H characteristics, the system behavior in a short time after an accident happening can represent the system T-H performance and be used to do the sensitivity analysis. Passive Containment Cooling System (PCCS) in AP1000 is used as a case study in our analysis, by which the heat produced in the containment can be transferred to the atmosphere through natural circulations. After steam line break (SLB) accident, the peak value of pressure in the containment appears within 1000s, we do the sensitivity analysis to screen key parameters in two ways: firstly, we screen the key inputs with variance decomposition method directly, 100 samples are gained from T-H model simulating 1000s, the inputs are sampled based on their probabilistic distributions, and the results show that air pressure is the most important parameter and the others don't have enough differentiation degrees. Then we get more 100 samples under the condition that air pressure is supposed as 0.1 MPa, here air temperature and steam mass flow are important ones besides air pressure. In another way, we analyze the correlation between pressure in the containment in a short time after SLB and the peak value according to the system T-H behavior, and get 600 samples from T-H model simulating 50s, the results are in accordance with that from T-H model simulating 1000s, air pressure, air temperature and steam mass flow are important parameters, and it just needs 1.55 h to calculate the important factor for one input.
Yu Yu; Shengfei Wang; Zhangpeng Guo; Xuefeng Lyu; Fenglei Niu; Francesco Di Maio; Enrico Zio. An efficient method of key parameter screening for PCCS under SLB accident in AP1000. Progress in Nuclear Energy 2020, 122, 103283 .
AMA StyleYu Yu, Shengfei Wang, Zhangpeng Guo, Xuefeng Lyu, Fenglei Niu, Francesco Di Maio, Enrico Zio. An efficient method of key parameter screening for PCCS under SLB accident in AP1000. Progress in Nuclear Energy. 2020; 122 ():103283.
Chicago/Turabian StyleYu Yu; Shengfei Wang; Zhangpeng Guo; Xuefeng Lyu; Fenglei Niu; Francesco Di Maio; Enrico Zio. 2020. "An efficient method of key parameter screening for PCCS under SLB accident in AP1000." Progress in Nuclear Energy 122, no. : 103283.
Safety by passive systems is a key design feature for new generation Nuclear Power Plants (NPPs). The Passive Containment Cooling System (PCCS) of the AP1000 NPP is a typical passive safety system, by which the heat produced in the containment is transferred to the environment through natural circulation and atmosphere is used as ultimate heat sink. Then, the climate conditions at the plant location influence the system performance. Monte Carlo (MC) simulation of random scenarios of embedding Thermal-Hydraulic (T-H) response of the passive system is commonly used for passive safety systems reliability assessment. However, T-H codes are usually computationally burdensome, in this paper, an effective way to overcome this issue and estimate passive safety system reliability is proposed and applied to the PCCS of the AP1000 NPP, showing that the T-H model runs can be reduced for efficient reliability assessment.
Yu Yu; Francesco Di Maio; Enrico Zio; Shengfei Wang; Zhangpeng Guo; Xuefeng Lyu; Zulong Hao; Fenglei Niu. An efficient method for passive safety systems reliability assessment. Annals of Nuclear Energy 2020, 141, 107347 .
AMA StyleYu Yu, Francesco Di Maio, Enrico Zio, Shengfei Wang, Zhangpeng Guo, Xuefeng Lyu, Zulong Hao, Fenglei Niu. An efficient method for passive safety systems reliability assessment. Annals of Nuclear Energy. 2020; 141 ():107347.
Chicago/Turabian StyleYu Yu; Francesco Di Maio; Enrico Zio; Shengfei Wang; Zhangpeng Guo; Xuefeng Lyu; Zulong Hao; Fenglei Niu. 2020. "An efficient method for passive safety systems reliability assessment." Annals of Nuclear Energy 141, no. : 107347.
Seyed Mojtaba Hoseyni; Francesco Di Maio; Enrico Zio. Condition-based probabilistic safety assessment for maintenance decision making regarding a nuclear power plant steam generator undergoing multiple degradation mechanisms. Reliability Engineering & System Safety 2019, 191, 1 .
AMA StyleSeyed Mojtaba Hoseyni, Francesco Di Maio, Enrico Zio. Condition-based probabilistic safety assessment for maintenance decision making regarding a nuclear power plant steam generator undergoing multiple degradation mechanisms. Reliability Engineering & System Safety. 2019; 191 ():1.
Chicago/Turabian StyleSeyed Mojtaba Hoseyni; Francesco Di Maio; Enrico Zio. 2019. "Condition-based probabilistic safety assessment for maintenance decision making regarding a nuclear power plant steam generator undergoing multiple degradation mechanisms." Reliability Engineering & System Safety 191, no. : 1.
The problem of sensor positioning for condition monitoring of Systems, Structures and Components (SSCs) has been recently proposed to be addressed by Value of Information (VoI) optimization. VoI is a metric that quantifies the benefit of taking a measurement prior to adopting it. This metric lacks the characteristics of sub-modularity, i.e. the benefit of adding a measurement to a small set of measurement is higher than adding it to a bigger set. This causes the VoI optimization to not guarantee optimal results when the problem is solved by greedy optimization algorithms. In this work, the sub-modularity issue is considered with reference to the thickness gauge sensor positioning on a Steam Generator (SG) of Nuclear Power Plant (NPP), and ways forward to overcome the sub-modularity issue are suggested.
Seyed Mojtaba Hoseyni; Francesco Di Maio; Enrico Zio. VoI-Based Optimal Sensors Positioning and the Sub-Modularity Issue. 2019 4th International Conference on System Reliability and Safety (ICSRS) 2019, 148 -152.
AMA StyleSeyed Mojtaba Hoseyni, Francesco Di Maio, Enrico Zio. VoI-Based Optimal Sensors Positioning and the Sub-Modularity Issue. 2019 4th International Conference on System Reliability and Safety (ICSRS). 2019; ():148-152.
Chicago/Turabian StyleSeyed Mojtaba Hoseyni; Francesco Di Maio; Enrico Zio. 2019. "VoI-Based Optimal Sensors Positioning and the Sub-Modularity Issue." 2019 4th International Conference on System Reliability and Safety (ICSRS) , no. : 148-152.
Cyber-physical systems (CPSs) must consider both components failures and cyber threats. In this paper, we propose the goal tree success tree master logic diagram (GTST-MLD) to analyze these within the same framework. The benchmark with a conventional attack tree (AT)-bow tie (BT) method of literature shows that GTST-MLD can overcome the limits of conventional AT-BT and requires less information to quantify the risk of a generic CPS, properly managing the scarcity of information on security threats. The method is applied to a CPS comprising of a chemical reactor and its control system that is exposed to cyber-attacks to the SCADA system.
Francesco Di Maio; Roberto Mascherona; Enrico Zio. Risk Analysis of Cyber-Physical Systems by GTST-MLD. IEEE Systems Journal 2019, 14, 1333 -1340.
AMA StyleFrancesco Di Maio, Roberto Mascherona, Enrico Zio. Risk Analysis of Cyber-Physical Systems by GTST-MLD. IEEE Systems Journal. 2019; 14 (1):1333-1340.
Chicago/Turabian StyleFrancesco Di Maio; Roberto Mascherona; Enrico Zio. 2019. "Risk Analysis of Cyber-Physical Systems by GTST-MLD." IEEE Systems Journal 14, no. 1: 1333-1340.
Defenders have to enforce defense strategies by taking decisions on allocation of resources to protect the integrity and survivability of cyber-physical systems (CPSs) from intentional and malicious cyber attacks. In this work, we propose an adversarial risk analysis approach to provide a novel one-sided prescriptive support strategy for the defender to optimize the defensive resource allocation, based on a subjective expected utility model, in which the decisions of the adversaries are uncertain. This increases confidence in cyber security through robustness of CPS protection actions against uncertain malicious threats compared with prescriptions provided by a classical defend-attack game-theoretical approach. We present the approach and the results of its application to a nuclear CPS, specifically the digital instrumentation and control system of the advanced lead-cooled fast reactor European demonstrator.
Wei Wang; Francesco Di Maio; Enrico Zio. Adversarial Risk Analysis to Allocate Optimal Defense Resources for Protecting Cyber–Physical Systems from Cyber Attacks. Risk Analysis 2019, 39, 2766 -2785.
AMA StyleWei Wang, Francesco Di Maio, Enrico Zio. Adversarial Risk Analysis to Allocate Optimal Defense Resources for Protecting Cyber–Physical Systems from Cyber Attacks. Risk Analysis. 2019; 39 (12):2766-2785.
Chicago/Turabian StyleWei Wang; Francesco Di Maio; Enrico Zio. 2019. "Adversarial Risk Analysis to Allocate Optimal Defense Resources for Protecting Cyber–Physical Systems from Cyber Attacks." Risk Analysis 39, no. 12: 2766-2785.
Both stochastic failures and cyber attacks can compromise the correct functionality of Cyber-Physical Systems (CPSs). Cyber attacks manifest themselves in the physical system and, can be misclassified as component failures, leading to wrong control actions and maintenance strategies. In this chapter, we illustrate the use of a nonparametric cumulative sum (NP-CUSUM) approach for online diagnostics of cyber attacks to CPSs. This allows for (i) promptly recognizing cyber attacks by distinguishing them from component failures, and (ii) guiding decisions for the CPSs recovery from anomalous conditions. We apply the approach to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED) and its digital Instrumentation and Control (I&C) system. For this, an object-oriented model previously developed is embedded within a Monte Carlo (MC) engine that allows injecting into the I&C system both components (stochastic) failures (such as sensor bias, drift, wider noise and freezing) and cyber attacks (such as Denial of Service (DoS) attacks mimicking component failures).
Wei Wang; Francesco Di Maio; Enrico Zio. A Non-parametric Cumulative Sum Approach for Online Diagnostics of Cyber Attacks to Nuclear Power Plants. Physics of Automatic Target Recognition 2019, 195 -228.
AMA StyleWei Wang, Francesco Di Maio, Enrico Zio. A Non-parametric Cumulative Sum Approach for Online Diagnostics of Cyber Attacks to Nuclear Power Plants. Physics of Automatic Target Recognition. 2019; ():195-228.
Chicago/Turabian StyleWei Wang; Francesco Di Maio; Enrico Zio. 2019. "A Non-parametric Cumulative Sum Approach for Online Diagnostics of Cyber Attacks to Nuclear Power Plants." Physics of Automatic Target Recognition , no. : 195-228.
Francesco Di Maio; Roberto Mascherona; Wei Wang; Enrico Zio. Simulation-Based Goal Tree Success Tree for the Risk Analysis of Cyber-Physical Systems. Proceedings of the 29th European Safety and Reliability Conference (ESREL) 2019, 1 .
AMA StyleFrancesco Di Maio, Roberto Mascherona, Wei Wang, Enrico Zio. Simulation-Based Goal Tree Success Tree for the Risk Analysis of Cyber-Physical Systems. Proceedings of the 29th European Safety and Reliability Conference (ESREL). 2019; ():1.
Chicago/Turabian StyleFrancesco Di Maio; Roberto Mascherona; Wei Wang; Enrico Zio. 2019. "Simulation-Based Goal Tree Success Tree for the Risk Analysis of Cyber-Physical Systems." Proceedings of the 29th European Safety and Reliability Conference (ESREL) , no. : 1.
Early detection of the failure of a nuclear system is an important topic in nuclear energy. This paper proposes three machine learning methodologies to detect the failure modes (FM) of the Lead-Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS) nuclear system after the first 10%, 50% and 90% time periods of the 3000 seconds mission time of the LBEXADS. The first methodology detects the FM of the LBE-XADS after the first 10% time period and consists of two Gaussian mixture-based (GM-based) classifiers. The second methodology detects the FM of the LBE-XADS after the first 50% time period and consists of a GM-based classifier and a neural network MLP1. The third methodology detects the failure mode of the LBE-XADS after the first 90% time period and consists of a GM-based classifier and a neural network MLP2. The three proposed methodologies outperformed the fuzzy similarity approach of the previous work.
David Tian; Jiamei Deng; Enrico Zio; Francesco Maio; Fucheng Liao. Failure Modes Detection of Nuclear Systems Using Machine Learning. 2018 5th International Conference on Dependable Systems and Their Applications (DSA) 2018, 35 -43.
AMA StyleDavid Tian, Jiamei Deng, Enrico Zio, Francesco Maio, Fucheng Liao. Failure Modes Detection of Nuclear Systems Using Machine Learning. 2018 5th International Conference on Dependable Systems and Their Applications (DSA). 2018; ():35-43.
Chicago/Turabian StyleDavid Tian; Jiamei Deng; Enrico Zio; Francesco Maio; Fucheng Liao. 2018. "Failure Modes Detection of Nuclear Systems Using Machine Learning." 2018 5th International Conference on Dependable Systems and Their Applications (DSA) , no. : 35-43.
This work presents a method to improve the diagnostic performance of empirical classification system (ECS), which is used to estimate the degradation state of components based on measured signals. The ECS is embedded in a homogenous continuous-time, finite-state semi-Markov model (HCTFSSMM), which adjusts diagnoses based on the past history of components. The combination gives rise to a homogeneous continuous-time finite-state hidden semi-Markov model (HCTFSHSMM). In an application involving the degradation of bearings in automotive machines, the proposed method is shown to be superior in classification performance compared to the single-stage ECS.
Francesco Cannarile; Michele Compare; Piero Baraldi; Francesco Di Maio; Enrico Zio. Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components. Machines 2018, 6, 34 .
AMA StyleFrancesco Cannarile, Michele Compare, Piero Baraldi, Francesco Di Maio, Enrico Zio. Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components. Machines. 2018; 6 (3):34.
Chicago/Turabian StyleFrancesco Cannarile; Michele Compare; Piero Baraldi; Francesco Di Maio; Enrico Zio. 2018. "Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components." Machines 6, no. 3: 34.
When a fleet of similar Systems, Structures and Components (SSCs) is available, the use of all the available information collected on the different SSCs is expected to be beneficial for the diagnosis purpose. Although different SSCs experience different behaviours in different environmental and operational conditions, they maybe informative for the other (even if different) SSCs. In the present work, the objective is to build a fault diagnostic tool aimed at capitalizing the available data (vibration, environmental and operational conditions) and knowledge of a heterogeneous fleet of P Nuclear Power Plants (NPPs) turbines. To this aim, a framework for incrementally learning different clusterings independently obtained for the individual turbines is here proposed. The basic idea is to reconciliate the most similar clusters across the different plants. The data of shut-down transients acquired from the past operation of the P NPPs turbines are summarized into a final, reconciliated consensus clustering of the turbines behaviors under different environmental and operational conditions. Eventually, one can distinguish, among the groups, those of anomalous behavior and relate them to specific root causes. The proposed framework is applied on the shut-down transients of two different NPPs. Three alternative approaches for learning data are applied to the case study and their results are compared to those obtained by the proposed framework: results show that the proposed approach is superior to the other approaches with respect to the goodness of the final consensus clustering, computational demand, data requirements, and fault diagnosis effectiveness.
Sameer Al-Dahidi; Francesco Di Maio; Piero Baraldi; Enrico Zio; Redouane Seraoui. A framework for reconciliating data clusters from a fleet of nuclear power plants turbines for fault diagnosis. Applied Soft Computing 2018, 69, 213 -231.
AMA StyleSameer Al-Dahidi, Francesco Di Maio, Piero Baraldi, Enrico Zio, Redouane Seraoui. A framework for reconciliating data clusters from a fleet of nuclear power plants turbines for fault diagnosis. Applied Soft Computing. 2018; 69 ():213-231.
Chicago/Turabian StyleSameer Al-Dahidi; Francesco Di Maio; Piero Baraldi; Enrico Zio; Redouane Seraoui. 2018. "A framework for reconciliating data clusters from a fleet of nuclear power plants turbines for fault diagnosis." Applied Soft Computing 69, no. : 213-231.
In recent years, there has been a growing interest in nuclear fusion as energy source due to its several principle advantages over fission, which include reduced radioactivity in operation and in waste, large fuel supplies, and increased safety. The most promising configuration of a nuclear fusion system is currently the tokamak, the largest of which, called the largest of which (ITER), is under construction in Cadarache, France. The safety of nuclear fusion systems has to be proved and verified by a systematic analysis of the system behavior under normal transient and accidental conditions. One challenge to the analysis is that the operation of tokamaks presents complex dynamic features as it is based on the transformer principle: in particular, they employ superconducting magnets, a subset of which operates with variable current to generate one of the components of the magnetic field needed to confine the plasma in the chamber where nuclear fusion reactions occur. In the present paper, we apply techniques of Integrated Deterministic and Probabilistic Safety Assessment (IDPSA), which combine phenomenological models of system dynamics with stochastic process models, taking for the first time as reference system the cooling circuit of a superconducting magnet for fusion applications, subject to a Loss-Of-Flow-Accident (LOFA).
R. Bellaera; R. Bonifetto; N. Pedroni; L. Savoldi; R. Zanino; Francesco Di Maio; E. Zio. Integrated deterministic and probabilistic safety assessment of the cooling circuit of a superconducting magnet for nuclear fusion applications. Safety and Reliability – Safe Societies in a Changing World 2018, 2161 -2168.
AMA StyleR. Bellaera, R. Bonifetto, N. Pedroni, L. Savoldi, R. Zanino, Francesco Di Maio, E. Zio. Integrated deterministic and probabilistic safety assessment of the cooling circuit of a superconducting magnet for nuclear fusion applications. Safety and Reliability – Safe Societies in a Changing World. 2018; ():2161-2168.
Chicago/Turabian StyleR. Bellaera; R. Bonifetto; N. Pedroni; L. Savoldi; R. Zanino; Francesco Di Maio; E. Zio. 2018. "Integrated deterministic and probabilistic safety assessment of the cooling circuit of a superconducting magnet for nuclear fusion applications." Safety and Reliability – Safe Societies in a Changing World , no. : 2161-2168.
Wei Wang; Francesco Di Maio; Enrico Zio. Hybrid fuzzy-PID control of a nuclear Cyber-Physical System working under varying environmental conditions. Nuclear Engineering and Design 2018, 331, 54 -67.
AMA StyleWei Wang, Francesco Di Maio, Enrico Zio. Hybrid fuzzy-PID control of a nuclear Cyber-Physical System working under varying environmental conditions. Nuclear Engineering and Design. 2018; 331 ():54-67.
Chicago/Turabian StyleWei Wang; Francesco Di Maio; Enrico Zio. 2018. "Hybrid fuzzy-PID control of a nuclear Cyber-Physical System working under varying environmental conditions." Nuclear Engineering and Design 331, no. : 54-67.
Optimal sizing of peak loads has proven to be an important factor affecting the overall energy consumption of heating ventilation and air-conditioning (HVAC) systems. Uncertainty quantification of peak loads enables optimal configuration of the system by opting for a suitable size factor. However, the representation of uncertainty in HVAC sizing has been limited to probabilistic analysis and scenario-based cases, which may limit and bias the results. This study provides a framework for uncertainty representation in building energy modeling, due to both random factors and imprecise knowledge. The framework is shown by a numerical case study of sizing cooling loads, in which uncertain climatic data are represented by probability distributions and human-driven activities are described by possibility distributions. Cooling loads obtained from the hybrid probabilistic–possibilistic propagation of uncertainty are compared to those obtained by pure probabilistic and pure possibilistic approaches. Results indicate that a pure possibilistic representation may not provide detailed information on the peak cooling loads, whereas a pure probabilistic approach may underestimate the effect of uncertain human behavior. The proposed hybrid representation and propagation of uncertainty in this paper can overcome these issues by proper handling of both random and limited data.
Fazel Khayatian; Maryam MeshkinKiya; Piero Baraldi; Francesco Di Maio; Enrico Zio. Hybrid Probabilistic–Possibilistic Treatment of Uncertainty in Building Energy Models: A Case Study of Sizing Peak Cooling Loads. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg 2018, 4, 041008 .
AMA StyleFazel Khayatian, Maryam MeshkinKiya, Piero Baraldi, Francesco Di Maio, Enrico Zio. Hybrid Probabilistic–Possibilistic Treatment of Uncertainty in Building Energy Models: A Case Study of Sizing Peak Cooling Loads. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg. 2018; 4 (4):041008.
Chicago/Turabian StyleFazel Khayatian; Maryam MeshkinKiya; Piero Baraldi; Francesco Di Maio; Enrico Zio. 2018. "Hybrid Probabilistic–Possibilistic Treatment of Uncertainty in Building Energy Models: A Case Study of Sizing Peak Cooling Loads." ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg 4, no. 4: 041008.
Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) aims at analyzing, mainly by simulation, the evolution of accident scenarios in complex dynamic systems, accounting for the mutual interactions between failure and recovery of system components, the evolving physical processes, the control and operator actions, the software and the firmware. The analysis can be seen as composed of three successive steps, namely scenario generation, modeling and post-processing. Generation involves simulating an as complete and realistic as possible set of accident scenarios; modeling involves representing in a structured way the simulated scenarios; post-processing involves extracting from the simulated scenarios information relevant for the safety assessment of the system, in particular, untangling the safe scenarios from the prime implicants (PIs) (minimum combinations of failure events that are capable of leading the system into a fault state) and the near misses (NMs) (combinations of failure events that lead the system to a quasi-fault state, a condition close to accident). The three steps of the process of IDPSA are exemplified by the analysis of accident scenarios for a U-tube steam generator (UTSG) of a nuclear power plant (NPP). With reference to this case study, efficient methods for scenarios generation (specifically, Monte Carlo (MC) sampling and multi valued logic (MVL)), modeling (specifically, dynamic event tree (DET) and repairable DET (RDET)) and post-processing (specifically, evolutionary algorithms (EA) and clustering methods) are overviewed.
Francesco Di Maio; Enrico Zio. Dynamic Accident Scenario Generation, Modeling and Post-Processing for the Integrated Deterministic and Probabilistic Safety Analysis of Nuclear Power Plants. Design-Basis Accident Analysis Methods for Light-Water Nuclear Power Plants 2018, 477 -504.
AMA StyleFrancesco Di Maio, Enrico Zio. Dynamic Accident Scenario Generation, Modeling and Post-Processing for the Integrated Deterministic and Probabilistic Safety Analysis of Nuclear Power Plants. Design-Basis Accident Analysis Methods for Light-Water Nuclear Power Plants. 2018; ():477-504.
Chicago/Turabian StyleFrancesco Di Maio; Enrico Zio. 2018. "Dynamic Accident Scenario Generation, Modeling and Post-Processing for the Integrated Deterministic and Probabilistic Safety Analysis of Nuclear Power Plants." Design-Basis Accident Analysis Methods for Light-Water Nuclear Power Plants , no. : 477-504.