RAVEN Theory Manual

Alfonsi, Andrea; Rabiti, Cristian; Mandelli, Diego; Cogliati, Joshua J.; Wang, Congjian; Maljovec, Dan; Talbot, Paul; Smith, Curtis

doi:10.2172/1260312

Cited by 34 publications

(34 citation statements)

References 28 publications

(43 reference 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%

“…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%

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Hydraulic behavior of fault zones in pump tests of geothermal wells: a parametric analysis using numerical simulations for the Upper Jurassic aquifer of the North Alpine Foreland Basin

et al. 2019

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Fault zones in the Upper Jurassic aquifer of the North Alpine Foreland Basin are generally regions with possibly increased hydraulic properties. They are consequently often part of the geothermal exploration concepts in this area and a primary target for the drilling operation. Data from this aquifer, gathered in pump tests, however, show that only four out of 41 successful wells exhibit hydraulic proof for the presence of such a fault zone in terms of a bi-/linear flow regime. Besides technical effects, also the contrast in hydraulic properties itself, between fault zone and surrounding host rock, can prevent the detection of a fault zone in pump test data. This means a certain threshold has to be surpassed until its effects become clearly visible. A simplified realistic numerical model was constructed and calibrated with pressure data from an exploration site in the south of Munich. This model was then used to observe the presence of linear and bilinear flows in dependence on the Malm aquifers parameter space. Sampling the possible hydraulic property combinations with the help of an HPC (high-performance computing) cluster and automating the detection of the corresponding main flow type allowed to quantify the areas in parameter space where the fault zone-related flow regimes of interest are present. Through the investigation of more than 30,000 combinations between fault zone permeability, matrix permeability, fault zone storage, matrix storage and fault zone thickness, it was found that, in the parameter space of the Malm aquifer, a bilinear flow can be observed for the first time only if the matrix permeability is lower than 2.0 × 10 −13 m 2 , and a linear flow for matrix permeability values below 6.0 × 10 −14 m 2. Additionally, it was shown that fault zones, which have better hydraulic properties than the surrounding matrix, can indeed be hidden in pumping tests due to the parameter setting.

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Section: Exploration Of Parameter Spacementioning

confidence: 99%

Section: Exploration Of Parameter Spacementioning

confidence: 99%

Hydraulic behavior of fault zones in pump tests of geothermal wells: a parametric analysis using numerical simulations for the Upper Jurassic aquifer of the North Alpine Foreland Basin

et al. 2019

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“…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%

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Biersdorf

Bui

Sakurahara

et al. 2020

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Fire is historically and analytically a significant contributor to nuclear power plant risk. The level of fire risk and the methods, tools, and data for modeling this risk are highly debated by experts. One area of debate is the input data used in fire modeling and how to deal with this data's high rate of uncertainty. This report outlines work performed for determining the key parameters causing this uncertainty and how it propagates into nuclear power plant PRA models. This research used the Integrated Probabilistic Risk Assessment (I-PRA) method and paves the way to correctly assess and reduce data uncertainty in fire modeling.

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“…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%

Narcisi

Lorusso

Giannetti

et al. 2019

Annals of Nuclear Energy

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The best estimate plus uncertainty (BEPU) method plays a key role in the development of the innovative Generation IV nuclear reactors, for the improvement of knowledge and the good evaluation of the safety margins for new phenomena. The aim of this paper is to validate an uncertainty quantification (UQ) approach using RAVEN code. RAVEN, developed at the Idaho National Laboratory, is a multipurpose probabilistic and uncertainty quantification framework, capable to communicate with any system code, implemented with an integrated validation methodology involving several different metrics. In this activity, a coupled calculation RELAP5-3D/RAVEN has been performed to assess the validity of the embedded UQ approach. The simulations have concerned two tests conducted on NACIE (NAtural CIrculation Experiment) facility, a nonnuclear loop-type system using Lead-Bismuth Eutectic (LBE) as coolant, realized at the ENEA Brasimone Research Centre (Italy) to support the development of the GEN-IV reactors. The experimental tests are aimed to investigate the phenomena related to the natural and gas enhanced circulation flow regimes of heavy liquid metals (HLM), and to test and validate the main components in a LBE environment. A numerical 1D model of NACIE facility has been realized and the post-test analysis has been carried out using RELAP5-3D © ver. 4.3.4. Based on these results, a statistical analysis has been performed using RAVEN computer code, investigating the capability of three probabilistic comparison metrics fully integrated in the code.

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RAVEN Theory Manual

Cited by 34 publications

References 28 publications

Hydraulic behavior of fault zones in pump tests of geothermal wells: a parametric analysis using numerical simulations for the Upper Jurassic aquifer of the North Alpine Foreland Basin

Hydraulic behavior of fault zones in pump tests of geothermal wells: a parametric analysis using numerical simulations for the Upper Jurassic aquifer of the North Alpine Foreland Basin

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Uncertainty quantification method for RELAP5-3D© using RAVEN and application on NACIE experiments

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