General purpose software for efficient uncertainty management of large finite element models

Patelli, Edoardo; Panayirci, H. Murat; Broggi, Matteo; Goller, B.; Beaurepaire, Pierre; Pradlwarter, H. J.; Schuëller, Gerhart I.

doi:10.1016/j.finel.2011.11.003

Cited by 61 publications

(29 citation statements)

References 36 publications

(35 reference statements)

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“…In light of this, if the initial network contains non-probabilistic but continuous nodes, the reduced network is not a traditional BN containing only crisp parameters but instead includes nodes whose outcomes are associated with probability bounds. The methodology proposed has been implemented in the general purpose software OpenCossan (Patelli et al 2012Patelli 2016) in an object oriented fashion. This ensures programming flexibility, computational efficiency and allows to avoid code duplication.…”

Section: Proposed Computational Approachmentioning

confidence: 99%

“…The computational framework is organized in classes, i.e. data structures consisting of data fields and methods together with their interactions and interfaces (Patelli et al 2012). Objects (i.e., instances of classes) can be then easily aggregated, forming more complex objects and being processed according to the related methods in order to obtain the output of interest.…”

Section: Proposed Computational Approachmentioning

confidence: 99%

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Robust vulnerability analysis of nuclear facilities subject to external hazards

Tolo

Patelli

Beer

2016

Stoch Environ Res Risk Assess

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Natural hazards have the potential to trigger complex chains of events in technological installations leading to disastrous effects for the surrounding population and environment. The threat of climate change of worsening extreme weather events exacerbates the need for new models and novel methodologies able to capture the complexity of the natural-technological interaction in intuitive frameworks suitable for an interdisciplinary field such as that of risk analysis. This study proposes a novel approach for the quantification of risk exposure of nuclear facilities subject to extreme natural events. A Bayesian Network model, initially developed for the quantification of the risk of exposure from spent nuclear material stored in facilities subject to flooding hazards, is adapted and enhanced to include in the analysis the quantification of the uncertainty affecting the output due to the imprecision of data available and the aleatory nature of the variables involved. The model is applied to the analysis of the nuclear power station of Sizewell B in East Anglia (UK), through the use of a novel computational tool. The network proposed models the direct effect of extreme weather conditions on the facility along several time scenarios considering climate change predictions as well as the indirect effects of external hazards on the internal subsystems and the occurrence of human error. The main novelty of the study consists of the fully computational integration of Bayesian Networks with advanced Structural Reliability Methods, which allows to adequately represent both aleatory and epistemic aspects of the uncertainty affecting the input through the use of probabilistic models, intervals, imprecise random variables as well as probability bounds. The uncertainty affecting the output is quantified in order to attest the significance of the results and provide a complete and effective tool for riskinformed decision making.

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Section: Proposed Computational Approachmentioning

confidence: 99%

Section: Proposed Computational Approachmentioning

confidence: 99%

Robust vulnerability analysis of nuclear facilities subject to external hazards

Tolo

Patelli

Beer

2016

Stoch Environ Res Risk Assess

Self Cite

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“…The computational framework is organized in classes, i.e. data structures consisting of data fields and methods together with their interactions and interfaces [14]. Objects (i.e.…”

Section: Numerical Implementationmentioning

confidence: 99%

A Computational Tool for Bayesian Networks Enhanced With Reliability Methods

Tolo¹,

Patelli²,

Beer³

et al. 2015

Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Abstract. A computational framework for the reduction and computation of Bayesian Networks enhanced with structural reliability methods is presented. During the last decades, the inner flexibility of the Bayesian Network method, its intuitive graphical structure and the strong mathematical background have attracted increasing interest in a large variety of applications involving joint probability of complex events and dependencies. Furthermore, the fast growing availability of computational power on the one side and the implementation of robust inference algorithms on the other, have additionally promoted the success of this method. Inference in Bayesian Networks is limited to only discrete variables (with the only exception of Gaussian distributions) in case of exact algorithms, whereas approximate approach allows to handle continuous distributions but can either result computationally inefficient or have unknown rates of convergence.This work provides a valid alternative to the traditional approach without renouncing to the reliability and robustness of exact inference computation. The methodology adopted is based on the combination of Bayesian Networks with structural reliability methods and allows to integrate random and interval variables within the Bayesian Network framework in the so called Enhanced Bayesian Networks. In the following, the computational algorithms developed are described and a simple structural application is proposed in order to fully show the capability of the tool developed.

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“…When increasing to a target failure probability to the order of 10 -6 -10 -8 the number of simulations would be too numerous even with current HPC capabilities. Thus, 'smart' algorithms such as advanced Monte Carlo simulations would be required to reduce the number of simulations to a level appropriate for HPC [27].…”

mentioning

confidence: 99%

“…By efficient management of the computing tasks within a HPC architecture and streamlining the workflow its aim is to make UQ more approachable and give added value with bespoke toolboxes for stochastic analysis. COSSAN's use has previously been demonstrated successfully with large FE models [27].…”

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confidence: 99%