EPJ Web Conf.
Volume 247, 2021PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|Number of page(s)||8|
|Published online||22 February 2021|
K-MEANS CLUSTERING OF NEUTRON SPECTRA FOR CROSS SECTION COLLAPSE
Naval Nuclear Laboratory P.O. Box 1072, Schenectady, NY 12301, USA
Published online: 22 February 2021
The process of generating cross sections for whole-core analysis typically involves collapsing cross sections against an approximate spectrum generated by solving problems with reduced scope (e.g., 2D slices of a fuel assembly). Such spectra vary with the location of a material region and with other state parameters (e.g., burnup, temperature, soluble boron concentration), resulting in a burdensome and potentially time consuming process to store and load spectra. Commonly, this is resolved by manually determinining material regions for which the cross sections can be collapsed with a single weighting flux, requiring a combination of domain knowledge, engineering judgment, and trial and error. Exploring new reactor concepts and solving increasingly complicated problems with deterministic transport methods will therefore benefit greatly from an automated approach to grouping spectra independent of problem geometry or reactor type. This paper leverages a data analytics technique known as k-means clustering to group regions with similar weighting spectra into individual clusters, within each of which an average weighting flux is applied. Despite the clustering algorithm being agnostic to the physics of the problem, the approach results in a nearly 98% decrease in number of spectra regions with minimal impact to the accuracy.
Key words: Multigroup Cross Sections / Cross Section Collapse / Bengal / Jaguar
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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