Nuclear data uncertainties lead to substantial uncertainties in the predictions of LWR core calculations. Such problems are typically solved using lattice calculations and nodal codes, such as the WIMS-PANTHER system. It is desirable to derive uncertainties of core simulation output parameters (e.g. enthalpy deposition in an REA) to nuclear data uncertainties for specific nuclide-reaction pairs. This allows the key uncertainty contributions to be identified and ranked and facilitates use of data assimilation techniques. Key challenges in ranking the contributions to uncertainty are (1) the large number of input parameters (2) burnup-dependence of nuclide populations and microscopic cross sections, which challenges the use of adjoint methods (3) propagation of uncertainties through a multi-step calculation sequence. In this paper, an integrated methodology for deriving such uncertainties is presented. This comprises the following steps: (1) sensitivity analysis to identify the key reactions in the problem and exclude insignificant reactions (2) orthogonal decomposition of the nuclear data covariance matrix to calculate and rank eigenvectors (3) direct and consistent perturbation of the microscopic cross sections for the eigenvectors with significant eigenvalues (4) separate lattice (WIMS) and core (PANTHER) calculations with nuclear data perturbed for each eigenvector. This method (which assumes linearity of perturbations) is compared to a direct Sobol sample of the nuclear data and for the cases examined gives consistent results for the importance ranking of reactions, while greatly reducing the number of calculations employed. Hence, it can complement sampling-based approaches in developing an overall understanding of the magnitude, distribution, and contributors to uncertainty in core calculations. Both the linear method and the Sobol sampling method are integrated within the Visual Workshop graphical user environment to automate the process with the WIMS and PANTHER codes.
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