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Iwamoto, Osamu
EPJ Web of Conferences, 281, p.00009_1 - 00009_4, 2023/03
Kunieda, Satoshi; Endo, Shunsuke; Kimura, Atsushi
EPJ Web of Conferences, 281, p.00017_1 - 00017_6, 2023/03
The AMUR code, which is based on the multi-channel/multi-level R-matrix theory, is under development for the cross-section evaluation with the covariance data in the resolved resonance energy region. Although, the code was initially designed for the analysis of the light-nuclei, the authors extended its capability toward the analysis of heavier nuclei by introducing the Reich-Moore approximation and the free-gas approximation for the Doppler broadening. In this work, we challenge a resonance analysis of neutron cross-section data measured in J-PARC/ANNRI with AMUR, in which the resolution functions and the double-bunching effects were taken into account inside the code. In this presentation, let us show results of resonance analysis on some of the J-PARC/ANNRI measurements together with covariance of the resonance parameters and cross-sections, for the first time. We also plan to discuss differences of correlation matrices among approximations of the R-matrix theory to understand physics underlying on the resonant reaction.
Endo, Shunsuke; Kimura, Atsushi; Nakamura, Shoji; Iwamoto, Osamu; Iwamoto, Nobuyuki; Rovira Leveroni, G.
EPJ Web of Conferences, 281, p.00012_1 - 00012_5, 2023/03
Katabuchi, Tatsuya*; Iwamoto, Osamu; Hori, Junichi*; Kimura, Atsushi; Iwamoto, Nobuyuki; Nakamura, Shoji; Rovira Leveroni, G.; Endo, Shunsuke; Shibahara, Yuji*; Terada, Kazushi*; et al.
EPJ Web of Conferences, 281, p.00014_1 - 00014_4, 2023/03
Yokoyama, Kenji
EPJ Web of Conferences, 281, p.00004_1 - 00004_10, 2023/03
In Japan, development of adjusted nuclear data library for fast rector application based on the cross-section adjustment method has been conducted since the early 1990s. The adjusted library is called the unified cross-section set. The first version was developed in 1991 and is called ADJ91. Recently, the integral experimental data were further expanded to improve the design prediction accuracy of the core loaded with minor actinoids and/or degraded Pu. Using the additional integral experimental data, development of ADJ2017 was started in 2017. In 2022, the latest unified cross-section set AJD2017R was developed based on JENDL-4.0 by using 619 integral experimental data. An overview of the latest version with a review of previous ones will be shown. On the other hand, JENDL-5 was released in 2021. In the development of JENDL-5, some of the integral experimental data used in ADJ2017R were explicitly utilized in the nuclear data evaluation. However, this is not reflected in the covariance data. This situation needs to be considered when developing a unified cross-section set based on JENDL-5. Preliminary adjustment calculation based on JENDL-5 is performed using C/E (calculation/experiment) values simply evaluated by a sensitivity analysis. The preliminary results will be also discussed.
Maruyama, Shuhei; Endo, Tomohiro*; Yamamoto, Akio*
EPJ Web of Conferences, 281, p.00008_1 - 00008_9, 2023/03
The applicability of Akaike's Bayesian Information Criterion (ABIC) to covariance modeling in the cross-section adjustment method was investigated. One of the most important things for a reliable cross-section adjustment method is giving a suitable covariance matrix. However, since we cannot know the true covariance matrix in advance, we usually estimate and assume it. To judge the goodness of the covariance matrix modeling, a metric is desirable. As a candidate for this metric, we focus on ABIC which is one of the information criteria in Bayesian inference, because the cross-section adjustment method is often discussed within the framework of Bayesian inference. In the conventional cross-section adjustment method, incorporation of the analysis model uncertainty in a covariance matrix still requires ad hoc treatment. In JAEA, the integral experimental database for fast reactors has been developed and the adjusted cross-section set ADJ2017 has been created based on this database. Many of the core characteristics in the database have been analyzed by a deterministic method. Therefore, the predicted core characteristics have non-negligible uncertainties with correlations due to some numerical approximations. However, the evaluations of the uncertainties and their correlations are still challenging issues. In addition, there would be unknown uncertainties that experimenters and analysts of reactor physics experiments could not recognize. To judge the goodness of the covariance matrix related to these uncertainties, the applicability of ABIC to the cross-section adjustment method was investigated.
Fukui, Yuhei*; Endo, Tomohiro*; Yamamoto, Akio*; Maruyama, Shuhei
EPJ Web of Conferences, 281, p.00006_1 - 00006_9, 2023/03
We developed a new nuclear data adjustment method for experimental data containing outliers. This method mitigates the effect of outliers by applying M-estimation, a type of robust estimation, to the conventional nuclear data adjustment method using sensitivity coefficients. Based on the M-estimation, we derived a weighted nuclear data adjustment formula and developed a weight calculation method. The weighted nuclear data adjustment formula was derived by weighting the function to take the extremum of the conventional nuclear data adjustment. The weighting of each nuclear characteristic is calculated from the difference between the measured and calculated values of the nuclear characteristic. This weight calculation method can evaluate the validity of each nuclear characteristic by considering correlations between nuclear characteristics using singular value decomposition. The proposed method and the conventional method were compared and verified by twin experiments. In the twin experiments, the nuclear data were adjusted using experimental data that intentionally included outliers. As a result of twin experiments, it was confirmed that the nuclear data were adjusted robustly and appropriately even with the experimental data containing outliers.
Kimura, Atsushi; Endo, Shunsuke; Nakamura, Shoji
no journal, ,
Tokashiki, Mikio*; Ikehara, Tadashi*; Tada, Kenichi; Egawa, Toru*; Yokoyama, Kenji; Iwamoto, Osamu
no journal, ,
The bias factor methods have been widely used for applications to reduce uncertainties of the predicted value in the reactor physics characteristics. In the present study, an attempt has been made to apply the extended bias factor method to qualifying the reliability of measured data by assuming the uncertainty of the numerical model being negligible and the covariance of cross section being reliable. In this presentation, comparison results will be shown between actual measured value and the predicted value of a benchmark case sampled from the benchmark suite. Furthermore, it will be shown that the comparison and their uncertainty enable us to qualify the degree of reliability of the actual measured value.
