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|
BASIC STUDY ON A PSEUDO TREND PHENOMENON OF THE FEYNMAN-α ANALYSIS WITH BUNCHING METHOD
1 Atomic Energy Research Institute, Kindai University 3-4-1 Kowakae, Higashi-Osaka city, Osaka, Japan
2 Nagoya University Furo-cho, Chikusa-ku, Nagoya, Japan
3 Graduate School of Science and Engineering, Kindai University 3-4-1 Kowakae, Higashi-Osaka city, Osaka, Japan
Published online: 22 February 2021
In these years, reactor noise analysis methods have been studied to apply for the Debris’ criticality management at the Fukushima Daiichi NPP, Japan. The Feynman-α analysis with bunching method is one of the candidate techniques, however the bunching method itself has never been validated in detail. This synthesis technique is useful to reduce a time required for the experiment, however it is known that a non-physical trend unrelated to the state of a nuclear reactor is generated by the multiple use of time series data, and this phenomenon (we call “pseudo trend phenomenon”) has never been systematically studied in detail. In this study, Poisson events, whose statistical characteristics were clarified, were employed to investigate the pseudo trend phenomenon of the bunching method. The time-sequence count data for various statistical parameters were generated by the Monte Carlo time series simulator. Comparing the two results obtained by applying the conventional bunching method and the moving-bunching method for the same Poisson event time series, and it was found that the same pseudo trend component appears in both results of the bunching method and the moving bunching method. In addition, it was also found that the fluctuation width of the pseudo trend component is smaller than the statistical fluctuation range.
Key words: Feynman-α analysis / bunching method / pseudo trend phenomenon
© 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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