https://doi.org/10.1051/epjn/2025019
Regular Article
Fundamental properties and characteristics of flux distribution tallies using proper orthogonal decomposition
1
Nuclear Science and Engineering Center, Japan Atomic Energy Agency 2-4, Shirakata Tokai-mura, Naka-gun Ibaraki 319-1195 Japan
2
Department of Applied Energy, Graduate School of Engineering, Nagoya University Furo-cho, Chikusa-ku Nagoya Aichi 464-8603 Japan
* e-mail: kondo.ryoichi@jaea.go.jp
Received:
3
March
2025
Received in final form:
28
March
2025
Accepted:
17
April
2025
Published online: 4 June 2025
The flux distribution tallies using the proper orthogonal decomposition (POD) called “the POD tallies” have been developed in our previous study. The POD tallies can achieve dimensionality and statistical uncertainty reduction for a finely discretized flux distribution. Some characteristics of the POD tallies, which are left by our previous work, are revealed in the present study. Firstly, the POD tallies with the track length estimator are newly implemented. Since the statistical uncertainty of the POD tallies is reduced compared with the cell tallies, the POD tallies with the track length estimator can obtain the most precise result among the present implantations. Secondly, the basis vectors obtained by the deterministic and the stochastic methods are compared. The statistical uncertainty of the snapshot data invokes the degradation of the extracted basis vectors. This result indicates that the deterministic method might be more efficient for the snapshot calculation. Finally, the impact of the covariances of expansion coefficients on the statistical uncertainty of expanded flux distribution is investigated. The reconstructed statistical uncertainty considering only the variances of the expansion coefficients differs from the reference. This result reveals that the covariances of the expansion coefficients are important to estimate the statistical uncertainty of the local flux in the flux distribution.
© R. Kondo et al., Published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
