Nuclear Data Dimension Reduction Article Swipe
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· 2019
· Open Access
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· DOI: https://doi.org/10.2172/1561059
· OA: W2972832373
Our nuclear data consist of 30 energy group cross sections as well as nubar and PFNS. They are sampled from a Gaussian distribution with correlations shown in Figure 1. A few of these samples are shown in Figure 2. The purpose of this section is to describe how we can reduce the dimension of the nuclear data for purposes of uncertainty quantification. Sections 2 and 3 describe why dimension reduction is useful, as well as our approach to dimension reduction. When using these nuclear data as inputs to the PARTISN code, some of the resulting $k_{eff}$ values are unrealistic. For instance, when PARTISN is simulating the Jezebel critical assembly, some of the nuclear data yield $k_{eff}$ far from one. We would like to weight the nuclear data combinations that yield more realistic output higher, while downweighting those that result in unrealistic output. Our approach to this is described in Section 4. Throughout the other sections, we will merely refer to a prior distribution of nuclear data, which is not constrained by the Jezebel experiment, and a posterior distribution of nuclear data, which is conditioned on satisfactory performance for the Jezebel experiment. For instance, the correlation matrix for the reweighted (posterior) nuclear data is shown in Figure 3, along with the difference from the prior correlation matrix. Finally, we will describe dimension reduction results in Section 5.
