Uncertainty and sensitivity analysis of a post-experiment calculation in thermal hydraulics

Glaeser, H.; Hofer, Ėduard; Kloos, Martina; Skorek, T.

doi:10.1016/0951-8320(94)90073-6

Cited by 63 publications

(15 citation statements)

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“…One possible optimal solution to the problem of analyzing uncertainties using the RELAP5/MOD3.2 code is to upgrade the code by explicitly including in its computational algorithm the approximate analytical method of quantile estimates, which does not have the drawbacks of the Monte Carlo method or the demonstrated drawbacks of the approaches to analyzing the uncertainties on the basis of the GRS method of [11] and similar methods.…”

Section: Prediction Of the Temperature Of Fuel-element Cladding With mentioning

confidence: 99%

“…The analysis is performed using statistical methods and setting the initial uncertainties as random quantities with a known distribution law (GRS, Germany; IPSN, France; ENUSA, Spain). The authors of [10] implemented the procedure for performing a statistical analysis of the uncertainties in the thermohydraulic calculations on the basis of the GRS method [11], upgraded the RELAP5/MOD3.2 code [12], and analyzed the uncertainties for certain problems. The objective of the calculations was to estimate the confidence interval of the random variable x (with no information on its distribution function), obtained with a prescribed reliability γ and confidence probability β.…”

Section: Prediction Of the Temperature Of Fuel-element Cladding With mentioning

confidence: 99%

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Safety prediction in nuclear power

Rumyantsev

2007

At Energy

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The practical operational safety of nuclear objects is of fundamental importance for assessing the future prospects under discussion and selecting a strategy for the development of nuclear power. It is shown that the methods currently being used for making safety predictions do not contain an analysis of the unavoidable errors and uncertainties of the models used or the initial and boundary conditions under which the physical processes that develop into serious accidents arise and develop. It is proposed that the method of quantile estimates of the uncertainties, which is free of the drawbacks of the Monte Carlo method and which increases the reliability of safety predictions in nuclear power, be used.Considering life expectancy and the threat of serious accidents, the safety of operating nuclear objects is of fundamental importance for making assessments of the prospects and selecting a strategy for the development of nuclear power [1]. The methods which are currently used to make safety predictions are based on stationary and dynamic deterministic models which generalize past experience and, as a rule, do not contain an analysis of the unavoidable errors and uncertainties in the models or the initial and boundary conditions under which the physical processes that develop into serious accidents arise and develop. In contrast to building airplanes or rockets, the development of full-scale prototypes to investigate the safety of nuclear systems experimentally and to eliminate the inaccuracies in the theory and computational models is still considered to be too expensive. For this reason, safety predictions in nuclear power must be based on a generalization of the past limited experience in building and operating its objects. To increase their reliability, safety predictions must include explicitly an analysis of the possible uncertainties of the models used and of the external events which influence the risk and are associated with this field of human activity. However, analysis of the uncertainties only relatively recently became a subject of systematic study in attempts to find validated methods for taking into account explicitly the incompleteness of our knowledge in predictive assessments of the safety of objects in nuclear power.The objective of the present article is to analyze some acceptable methods for predicting the safety of the objects in nuclear power and to demonstrate the need for improving the existing approaches to simulating the physical processes in a manner that explicitly takes account of the incompleteness of our knowledge of the initial and boundary conditions under which processes that turn into serious accidents can arise and develop. The analysis is based on results obtained using the method of quantile estimates of the uncertainties [2][3][4]. This method encompasses the range from almost exact to sparse knowledge with the possibility that the ratio of the width of the 90% confidence interval to the median of the distribution varies over several orders of magnitude. The method operates w...

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Section: Prediction Of the Temperature Of Fuel-element Cladding With mentioning

confidence: 99%

Section: Prediction Of the Temperature Of Fuel-element Cladding With mentioning

confidence: 99%

Safety prediction in nuclear power

Rumyantsev

2007

At Energy

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“…The size of the sampling is determined from the characteristics of the tolerance intervals by applying the Noether-Wilks formula (Wilks, 1962, Galeser et al, 1994. Thus, the number of required calculations does not depend on the number of input parameters neither on any assumption about the probability distribution of results (Crow, 1960).…”

Section: Uncertainty Analysismentioning

confidence: 99%

Uncertainty analysis in environmental radioactivity measurements using the Monte Carlo code MCNP5

Gallardo

Querol

Ortiz

et al. 2015

Radiation Physics and Chemistry

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ElsevierGallardo Bermell, S.; Querol Vives, A.; Ortiz Moragón, J.; Ródenas Diago, J.; Verdú Martín, GJ.; Villanueva López, JF. (2015). Uncertainty analysis in environmental radioactivity measurements using the Monte Carlo code MCNP5. Radiation Physics and Chemistry. 116:214-218. doi:10.1016/j.radphyschem.2015.05.023. Uncertainty analysis in environmental radioactivity measurements using the Monte Carlo code MCNP5 ABSTRACTHigh Purity Germanium (HPGe) detectors are widely used for environmental radioactivity measurements due to their excellent energy resolution. Monte Carlo (MC) codes are a useful tool to complement experimental measurements in calibration procedures at the laboratory. However, the efficiency curve of the detector can vary due to uncertainties associated with measurements. These uncertainties can be classified into some categories: geometrical parameters of the measurement (distance source-detector, volume of the source), properties of the radiation source (radionuclide activity, branching ratio), and detector characteristics (Ge dead layer, active volume, end cap thickness). The Monte Carlo simulation can be also affected by other kind of uncertainties mainly related to cross sections and to the calculation itself. Normally, all these uncertainties are not well known and it is required a deep analysis to determine their effect on the detector efficiency. In this work, the Noether-Wilks formula is used to carry out the uncertainty analysis. A Probability Density Function (PDF) is assigned to each variable involved in the sampling process. The size of the sampling is determined from the characteristics of the tolerance intervals by applying the Noether-Wilks formula. Results of the analysis transform the efficiency curve into a region of possible values into the tolerance intervals. Results show a good agreement between experimental measurements and simulations for two different matrices (water and sand).

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“…Many approaches have been proposed for identifying and quantifying the uncertainties affecting the code outputs and generated by simplifications, approximations, round-off errors, numerical techniques, user errors and variability in the input parameters values (Pourgol-Mohammad 2009) e.g., Code Scaling, Applicability, and Uncertainty (CSAU) (Wulf et al 1990), Automated Statistical Treatment of Uncertainty Method (ASTRUM) and Integrated Methodology for Thermal-Hydraulics Uncertainty Analysis (IMTHUA) (Glaeser et al 1994). All these methods deal with the need of addressing the problem of uncertainty quantification of the Thermal Hydraulics (TH) codes that are used to predict the response of the systems in nominal and accidental conditions.…”

Section: Introductionmentioning

confidence: 99%