2016
DOI: 10.2172/1260312
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RAVEN Theory Manual

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Cited by 34 publications
(34 citation statements)
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“…With the application of RAVEN, an open source software framework for parametric and stochastic analysis (Alfonsi et al 2017), and the use of an HPC cluster [LRZ Linux cluster and Supermuc (Leibniz-Rechenzentrum 2017)], it is possible to calculate a great number of artificial pumping tests in a reasonable time. To use those computational resources efficiently, an adequate temporal and spatial discretization of the individual numerical simulations is necessary without introducing numerical errors or unnecessary inaccuracies for further evaluation.…”
Section: Exploration Of Parameter Spacementioning
confidence: 99%
See 1 more Smart Citation
“…With the application of RAVEN, an open source software framework for parametric and stochastic analysis (Alfonsi et al 2017), and the use of an HPC cluster [LRZ Linux cluster and Supermuc (Leibniz-Rechenzentrum 2017)], it is possible to calculate a great number of artificial pumping tests in a reasonable time. To use those computational resources efficiently, an adequate temporal and spatial discretization of the individual numerical simulations is necessary without introducing numerical errors or unnecessary inaccuracies for further evaluation.…”
Section: Exploration Of Parameter Spacementioning
confidence: 99%
“…For that, the grid sampling method was chosen, which discretizes the parameter ranges in a user-defined number of intervals and combines them into a grid. The model was then simulated at each coordinate and the response captured for further evaluation (Alfonsi et al 2017). The grid was set, limited by the simulation time and the available computational resources and based on the possible parameter ranges listed in Table 1, to 11 values, logarithmically evenly spaced, for fault zone and matrix permeability, six values, linearly evenly spaced, for specific fault zone and matrix storage and seven values for the fault zone thickness (15,20,35,50,100,200 and 300 m).…”
Section: Exploration Of Parameter Spacementioning
confidence: 99%
“…In the previous studies by the SoTeRiA laboratory at UIUC, Fire I-PRA was applied to a critical fireinduced scenario (i.e., a small-break loss-of-coolant accident due to stuck-open pressurizer valves induced by a switchgear room fire) in an NPP [31] that has improved the realism of PRA, leading to a 50% reduction in the total core damage frequency associated with the selected scenario. As compared to the current Fire PRA methodology based on NUREG/CR-6850 [26], Fire I-PRA advances three aspects [31]: Table 1 summarizes four aspects that Fire I-PRA [30] has advanced, as compared to the current Fire PRA methodology based on NUREG/CR-6850 [25] and its subsequent NUREGs and Fire PRA FAQs. As compared to the current Fire PRA methodology, the I-PRA framework creates a "unified" connection between the Plant-Specific PRA Module and the underlying physics and human performance models, where the communications of data and information among multiple levels of causality (i.e., fire growth, detection and suppression, cable damage, component failure, and system failure) are generated in a single computational platform [31].…”
Section: Integrated Pra (I-pra) Methodological Frameworkmentioning
confidence: 99%
“…iii. A computational platform is developed leveraging Risk Analysis Virtual Environment (RAVEN) [30] to operationalize the I-PRA Importance Ranking Methodology and is explained in Section 3.3.…”
Section: Risk Importance Ranking Of Fire Pra Input Datamentioning
confidence: 99%
“…Such distinction is performed employing sampling of the input space. Such capability, available in the RAVEN code, permits to compare a larger sample of data (Alfonsi et al, 2017). In order to assess the accuracy of the physical model under consideration, it is fundamental the inclusion of the experimental data uncertainties.…”
Section: Probabilistic Comparison Metricsmentioning
confidence: 99%