Monte Carlo simulation-based sensitivity analysis of the model of a thermal–hydraulic passive system

Zio, Enrico; Pedroni, Nicola

doi:10.1016/j.ress.2011.08.006

Cited by 54 publications

(21 citation statements)

References 83 publications

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“…Notice that the recommendation of using ANN regression models is mainly based on theoretical considerations about the (mathematically) demonstrated capability of ANN regression models of being universal approximants of continuous nonlinear functions [21] and the experience of the authors' in the use of ANN regression models for propagating the uncertainties through mathematical model codes simulating safety systems [56][57][58][59][60]. Since no further comparisons with other types of regression models have been performed by the authors yet, no additional proofs of the superiority of ANNs with respect to other regression models can be provided at present, in general terms.…”

Section: Sensitivity Analysis In a Factor Prioritization Settingmentioning

confidence: 99%

Hybrid Uncertainty and Sensitivity Analysis of the Model of a Twin-Jet Aircraft

Pedroni

Zio

2015

Journal of Aerospace Information Systems

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International audienceThe mathematical models employed in the risk assessment of complex, safety-critical engineering systems cannot capture all the characteristics of the system under analysis, due to: (i) the intrinsically random nature of several of the phenomena occurring during system operation (aleatory uncertainty); (ii) the incomplete knowledge about some of the phenomena (epistemic uncertainty). In this work, we consider the model of a twin-jet aircraft, which includes twenty-one inputs and eight outputs. The inputs are affected by mixed aleatory and epistemic uncertainties represented by probability distributions and intervals, respectively. Within this context, we address the following issues: (A) improvement of the input uncertainty models (i.e., reduction of the corresponding epistemic uncertainties) based on experimental data; (B) sensitivity analysis to rank the importance of the inputs in contributing to output uncertainties; (C) propagation of the input uncertainties to the outputs; (D) extreme case analysis to identify those system configurations that prescribe extreme values of some system performance metrics of interest (e.g., the failure probability). All the tasks are tackled and solved by means of an efficient combination of: (i) Monte Carlo Simulation (MCS) to propagate the aleatory uncertainty described by probability distributions; (ii) Genetic Algorithms (GAs) to solve the numerous optimization problems related to the propagation of epistemic uncertainty by interval analysis, and (iii) fast-running Artificial Neural Network (ANN) regression models to reduce the computational time related to the repeated model evaluations required by uncertainty and sensitivity analyses

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Section: Sensitivity Analysis In a Factor Prioritization Settingmentioning

confidence: 99%

Hybrid Uncertainty and Sensitivity Analysis of the Model of a Twin-Jet Aircraft

Pedroni

Zio

2015

Journal of Aerospace Information Systems

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“…Physical knowledge, expert information and data on the system behavior are used to build the model and estimate its parameters (Aven & Zio, 2011;Aven et al, 2014). The uncertainties in the model and parameters can be propagated by Monte Carlo (MC) simulation (Zio & Pedroni, 2009;Zio & Pedroni, 2012;Zhang et al, 2010;Catelani et al, 2015), Bayesian posterior analysis (Zhang & Mahadevan, 2001) and Fuzzy methodology (Dubais, 2010;Baraldi et al, 2015a;Garg, 2013;Garg, 2014). Most commonly, MC simulation is used, consisting in repeatedly sampling random values of the inputs from probability distributions (Zio, 2013).…”

Section: Introductionmentioning

confidence: 99%

Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment

Wang

Maio

Zio

2017

Reliability Engineering & System Safety

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Multi-State Physics Modeling (MSPM) provides a physics-based semiMarkov modeling framework for a more detailed reliability assessment. In this work, a three-loop Monte Carlo (MC) simulation scheme is proposed to operationalize the MSPM approach, quantifying and controlling the uncertainty affecting the system reliability model. The proposed MC simulation scheme involves three steps: i) the identification of the system components that deserve MSPM, ii) the quantification of the uncertainties in the MSPM component models and their propagation onto the system-level model, and iii) the selection of the most suitable modeling alternative that balances the computational demand for the system model solution and the robustness of the system reliability estimates.A Reactor Protection System (RPS) of a Nuclear Power Plant (NPP) is considered as case study for numerical evaluation.

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“…One of the objectives of reliability sensitivity analysis is to study the influence of probabilistic model parameters onto the reliability of a given structural system [1][2][3][4]. In this context, the system parameters involved in the sensitivity analysis are modeled by a random vector whose joint probability distribution is explicitly known and dependent on a certain number of parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The determination of the variation in the reliability, or equivalently in the failure probability, due to changes in the probabilistic model parameters can provide practical and relevant information. For example, it can identify the most influential system parameters and it can give an important insight on system failure that can be used for general risk-based decision making problems [5,4].…”

Section: Introductionmentioning

confidence: 99%

Reliability sensitivity analysis of stochastic finite element models

Jensen

Mayorga

Papadimitriou

2015

Computer Methods in Applied Mechanics and Engineering

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This contribution presents a scheme for integrating a model reduction technique into a simulation-based method for reliability sensitivity analysis of a class of medium/large nonlinear finite element models under stochastic excitation. The solution of this type of problems requires a large number of finite element model re-analyses to be performed over the space of system parameters. A component mode synthesis technique is implemented to carry out the sensitivity analysis in a reduced space of generalized coordinates. The reliability sensitivity analysis is performed by an approach based on a simple post-processing of an advanced sampling-based reliability analysis. The feasibility and effectiveness of the proposed scheme is demonstrated on a bridge finite element model under stochastic ground excitation.

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Monte Carlo simulation-based sensitivity analysis of the model of a thermal–hydraulic passive system

Abstract: Thermal-Hydraulic (T-H)

Cited by 54 publications

References 83 publications

Hybrid Uncertainty and Sensitivity Analysis of the Model of a Twin-Jet Aircraft

Hybrid Uncertainty and Sensitivity Analysis of the Model of a Twin-Jet Aircraft

Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment

Reliability sensitivity analysis of stochastic finite element models

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