Monte Carlo analysis and fuzzy sets for uncertainty propagation in SIS performance assessment

Innal, Fares; Dutuit, Yves; Chebila, Mourad

doi:10.1201/b15938-479

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…(5) Generate statistics from the obtained sample for the output result: mean, SD, confidence interval (percentiles), etc. (Chettouh et al, 2014;Innal et al, 2013).…”

Section: Sensitivity Analysis Methodologymentioning

confidence: 99%

Global and local sensitivity analysis of the Emission Dispersion Model input parameters

Chettouh

2021

WJSTSD

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PurposeThe objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO2 generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO2) emitted by an industrial fire in a plant located in an industrial region site in Algeria.Design/methodology/approachThe SA was defined for the following input parameters: wind speed, NO2 emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient.FindingsThe study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO2 initial concentration has the most influence on the predicted NO2 levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO2 concentration estimation.Originality/valueThe study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

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“…(5) Generate statistics from the obtained sample for the output result: mean, SD, confidence interval (percentiles), etc. (Chettouh et al, 2014;Innal et al, 2013).…”

Section: Sensitivity Analysis Methodologymentioning

confidence: 99%

Global and local sensitivity analysis of the Emission Dispersion Model input parameters

Chettouh

2021

WJSTSD

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“…The work proposed in [4] develops a set of generalized and simplified analytical expressions for MooN architectures by considering partial proof tests, while slightly taking the CCF contributions into account. In [11], probabilistic analysis of safety for MooN architectures is proposed when considering different degrees of uncertainty in some safety parameters such as failure rate, CCFs, and diagnostic coverage, by combining Monte Carlo sampling and fuzzy sets. Emphasizing the significance of CCF impact over safety in redundant systems, in [3], authors explore the criticality of beta-factor on safety calculations.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Analysis of Design Parameters in Safety-Critical Computers

Ahangari

Atik

Ozkok

et al. 2020

IEEE Trans. Emerg. Topics Comput.

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Nowadays, safety-critical computers are extensively used in many civil domains like transportation including railways, avionics, and automotive. In evaluating these safety critical systems, previous studies considered different metrics, but some of safety design parameters like failure diagnostic coverage (C) or common cause failure (CCF) ratio have not been seriously taken into account. Moreover, in some cases safety has not been compared with standard safety integrity levels (IEC-61508: SIL1-SIL4) or even have not met them. Most often, it is not very clear that which part of the system is the Achilles heel and how design can be improved to reach standard safety levels. Motivated by such design ambiguities, we aim to study the effect of various design parameters on safety in some prevalent safety configurations, namely, 1oo2 and 2oo3, where 1oo1 is also used as a reference. By employing Markov modeling, we analyzed the sensitivity of safety to important parameters including: failure rate of processing element, failure diagnostic coverage, CCF ratio, test and repair rates. This study aims to provide a deeper understanding on the influence of variation in design parameters over safety. Consequently, to meet appropriate safety integrity level, instead of improving some parts of a system blindly, it will be possible to make an informed decision on more relevant parameters.

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Monte Carlo analysis and fuzzy sets for uncertainty propagation in SIS performance assessment

Cited by 2 publications

References 9 publications

Global and local sensitivity analysis of the Emission Dispersion Model input parameters

Global and local sensitivity analysis of the Emission Dispersion Model input parameters

Analysis of Design Parameters in Safety-Critical Computers

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