Compression of Cross-Section Data Size for High-Resolution Core Analysis Using Dimensionality Reduction Technique

Abstract

Compression of cross-section data used for high-resolution core analysis is performed using a dimensionality reduction technique based on the singular value decomposition (SVD) and low-rank approximation. The size of cross-section data can be a significant issue in high-resolution core analysis using detailed energy and spatial resolutions. This study addresses this issue focusing on the similarity of multigroup cross sections among different spatial regions. A data compression method using the SVD and low-rank approximation is applied for the multigroup microscopic cross sections of heterogeneous material regions obtained by a lattice physics calculation with burnup and branch calculations. Weighting by nuclide number densities and neutron spectra is considered to improve the efficiency of compression for cross sections. Single-assembly transport calculations with the method of characteristics are carried out using the original cross sections and the reconstructed cross sections after data compression. The accuracy of data compression for cross sections is evaluated by comparing the multiplication factor and multigroup scalar fluxes. The results indicate that the present data compression for microscopic cross sections can reduce approximately 99.7% of the original cross-section data size under the present calculation condition.

Keywords:

• Singular value decomposition
• low-rank approximation
• cross section
• data compression
• core analysis

Acknowledgments

The authors sincerely thank Masato Tabuchi and Kosuke Tsujita for providing multigroup microscopic cross-section data and nuclide number densities from the AEGIS lattice physics code.