Adaptation of the Polynomial Chaos Method for the Uncertainty Analysis of Critical Experiments
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RETICAL AND EXPERIMENTAL PHYSICS OF NUCLEAR REACTORS
Adaptation of the Polynomial Chaos Method for the Uncertainty Analysis of Critical Experiments A. V. Kuzmina,* and T. N. Korbuta aJoint
Institute for Power and Nuclear Research–Sosny, National Academy of Sciences of Belarus, Minsk, 220109 Belarus *e-mail: [email protected] Received October 18, 2017; revised October 18, 2017; accepted October 30, 2017
Abstract—Improvement of computational methods used for the uncertainty analysis of critical experiments allows the accuracy of the results to be more realistically estimated, which is important for further improvement of computational codes and cross section libraries used to justify nuclear safety of the research on uses of nuclear energy. Standard uncertainty estimation methods (Monte Carlo, sensitivity analysis, etc.) have disadvantages arising from considerably growing needs for computational resources to be used for nonlinear problems with a lot of measured parameters that contribute to the total uncertainty of a critical experiment. This has provoked increasing interest in spectral methods for uncertainty analysis, in particular, the polynomial chaos method. In this work, the necessity of the regression analysis is substantiated for approximations of random variables by the polynomial chaos method to estimate uncertainties and confidence levels of the results of critical experiments. Keywords: uncertainty analysis, polynomial chaos method, critical experiments DOI: 10.1134/S1063778818100095
INTRODUCTION The demand for results of critical experiments is constantly increasing with rising requirements on computational codes and cross section libraries used to justify nuclear safety in activities associated with uses of nuclear energy like, for example, operating nuclear facilities and handling fissionable materials. In this connection, there is a demand for methods to estimate the uncertainty of the results of experiments on criticality of multiplying systems. To make an indepth uncertainty analysis, it is necessary to consider the effect produced on the final result by many more parameters than was recently customary and by the nonlinear character of the corresponding dependences. The increase in the computational capabilities of modern computers and improvement of computational codes make it possible to perform an uncertainty analysis using computer models that allow for the minutest details of geometry and almost exact material compositions of critical assemblies. The thus revealed possibilities of more accurately estimating uncertainties of the results of experiments on criticality of multiplying systems make it necessary to return to the analysis of the accuracy and methods for measurement of experimental parameters to re-estimate inaccuracies of the final results in light of new refined initial data and modern approaches to their interpretation [1, 2].
Simple deterministic methods widely used in the recent past for uncertainty estimation to a first approximation in perturbation theory fail to ensu
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