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.

中文翻译：

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使用降维技术压缩用于高分辨率岩心分析的横截面数据大小

摘要 用于高分辨率岩心分析的横截面数据的压缩是使用基于奇异值分解 (SVD) 和低秩近似的降维技术进行的。在使用详细能量和空间分辨率的高分辨率岩心分析中，横截面数据的大小可能是一个重要问题。本研究着重于不同空间区域之间多组横截面的相似性来解决这个问题。将使用 SVD 和低秩近似的数据压缩方法应用于通过具有燃耗和分支计算的晶格物理计算获得的异质材料区域的多组微观横截面。核素数密度和中子谱的加权被认为可以提高横截面的压缩效率。使用原始横截面和数据压缩后的重建横截面进行特征方法的单组件输运计算。通过比较倍增因子和多组标量通量来评估横截面数据压缩的准确性。结果表明，在目前的计算条件下，目前的微观截面数据压缩可以减少大约99.7%的原始截面数据大小。