This work aims to develop an uncertainty analysis methodology for the propagation and quantification of the effects of nuclear cross-section uncertainties on important core-wide attributes, such as power distribution and core critical eigenvalue. Given the computationally taxing nature of this endeavor, our goal is to develop a methodology capable of preserving the accuracy of brute force sampling techniques for uncertainty quantification while realizing the efficiency of deterministic techniques. To achieve that, a reduced order modeling (ROM) approach is proposed to deal with the enormous size of the uncertainty space, comprising all the cross-section few-group parameters required in core-wide simulation. The idea is to generate a compressed representation of the uncertainty space, as represented by a covariance matrix, that renders sampling techniques computationally a feasible option for quantifying and prioritizing the various sources of uncertainties. While the proposed developments are general to any reactor physics computational sequence, we customize our approach to the NESTLE [l]-TRITON [2] computational sequence, which will serve as a demonstrative tool for the implementation of our approach. NESTLE is a software used for core wide simulation, which relies on the few-group cross-sections to calculate core wide attributes over multiple cycles of depletion. Its input cross-sections are generated using a matrix of conditions evaluated using a lattice physics code, which in our implementation is done using the TRITON software of the ORNL' SCALE suit. This manuscript presents one of the early steps towards this goal. Specifically, we focus here on the development of the algorithms for determining the reduced dimension of covariance matrix. Numerical experiment using the TRITON software is employed to demonstrate how the reduction is achieved.
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