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Katano, Ryota; Yamamoto, Akio*; Endo, Tomohiro*
Nuclear Science and Engineering, 196(10), p.1194 - 1208, 2022/10
Times Cited Count：1 Percentile：31.61(Nuclear Science & Technology)In this study, we propose the ROM-Lasso method that enables efficient evaluation of sensitivity coefficients of neutronics parameters to cross-sections. In the proposed method, a vector of sensitivity coefficients is expanded by subspace bases, so-called Active Subspace (AS) based on the idea of Reduced Order Modeling (ROM). Then, the expansion coefficients are evaluated by the Lasso linear regression between cross-sections and neutronics parameters obtained by the random sampling. The proposed method can be applied in the case where the adjoint method is difficult to be applied since the proposed method uses only forward calculations. In addition, AS is an effective subspace that can expand the vector of sensitivity coefficients with the lower number of dimension. Thus, the number of unknows is reduced from the original number of input parameters and the calculation cost is dramatically improved compared to the Lasso regression without AS. In this paper, we conducted ADS burnup calculations as a verification. We have shown how AS bases are obtained and the applicability of the proposed method.
Katano, Ryota; Yamamoto, Akio*; Endo, Tomohiro*
Proceedings of International Conference on Physics of Reactors 2022 (PHYSOR 2022) (Internet), p.2032 - 2041, 2022/05
We have proposed the ROM-Lasso method to perform an efficient evaluation of the sensitivity coefficients of ADS core parameters to cross sections without major modification of the core analysis system. In the ROM-Lasso method, the sensitivity coefficient vector is expanded via the subspace bases so-called Active Subspace (AS), and the effective number of unknowns is reduced. Then, the expansion coefficients are determined via the penalized linear regression with the core parameters obtained by the random sampling, and the sensitivity coefficient vector is estimated. Owing to the AS, the required number of the core calculations is dramatically reduced in the ROM-Lasso method. In this work, we take the sensitivity coefficient evaluation of the coolant void reactivity at the end of the cycle for example and demonstrate how estimation accuracy depends on the number of samples and the AS.
Yokoyama, Kenji; Ishikawa, Makoto*
Annals of Nuclear Energy, 154, p.108100_1 - 108100_11, 2021/05
Times Cited Count：1 Percentile：16.35(Nuclear Science & Technology)In the design of innovative nuclear reactors such as fast reactors, the improvement of the prediction accuracies for neutronics properties is an important task. The nuclear data adjustment is a promising methodology for this issue. The idea of the nuclear data adjustment was first proposed in 1964. Toward its practical application, however, a great deal of study has been conducted over a long time. While it took about 10 years to establish the theoretical formulation, the research and development for its practical application has been conducted for more than half a century. Researches in this field are still active, and the fact suggests that the improvement of the prediction accuracies is indispensable for the development of new types of nuclear reactors. Massimo Salvatores, who passed away in March 2020, was one of the first proposers to develop the nuclear data adjustment technique, as well as one of the great contributors to its practical application. Reviewing his long-time works in this area is almost the same as reviewing the history of the nuclear data adjustment methodology. The authors intend that this review would suggest what should be done in the future toward the next development in this area. The present review consists of two parts: a) the establishment of the nuclear data adjustment methodology and b) the achievements related to practical applications. Furthermore, the former is divided into two aspects: the study on the nuclear data adjustment theory and the numerical solution for sensitivity coefficient that is requisite for the nuclear data adjustment. The latter is separated to three categories: the use of integral experimental data, the uncertainty quantification and design target accuracy evaluation, and the promotion of nuclear data covariance development.
Sugino, Kazuteru; Numata, Kazuyuki*; Ishikawa, Makoto; Takeda, Toshikazu*
Annals of Nuclear Energy, 130, p.118 - 123, 2019/08
Times Cited Count：0 Percentile：0.01(Nuclear Science & Technology)In MA sample irradiation test data calculations, the neutron fluence during irradiation period is generally scaled by using dosimetry data in order to improve calculation accuracy. In such a case, appropriate correction is required to burnup sensitivity coefficients obtained by the conventional generalized perturbation theory because some cancellations occur in the burnup sensitivity coefficients. Therefore, a new formula for the burnup sensitivity coefficient has been derived with the consideration of the neutron fluence scaling effect (NFS). In addition, the cross-section-induced uncertainty is evaluated by using the obtained burnup sensitivity coefficients and the covariance data based on the JENDL-4.0.
Katano, Ryota; Endo, Tomohiro*; Yamamoto, Akio*; Tsujimoto, Kazufumi
Journal of Nuclear Science and Technology, 55(10), p.1099 - 1109, 2018/10
Times Cited Count：3 Percentile：30.05(Nuclear Science & Technology)In this study, we propose the penalized regression "adaptive smooth-lasso" for the estimation of sensitivity coefficients of the neutronics parameters. The proposed method estimates the sensitivity coefficients of the neutronics parameters using the variation of the microscopic cross sections and the neutronics parameter obtained by random sampling. The proposed method utilizes only the forward calculations. Thus, the proposed method can be applied for the complex reactor analysis for which the application of the adjoint method is difficult. In this study, we proposed a penalty term considering the characteristics of the sensitivity coefficients of the neutronics parameter to the microscopic multi-group cross sections. Through verification calculation, we show that the proposed method achieves high accuracy with less computational cost compared to the method based on random sampling proposed in the previous studies.
Katakura, Junichi; Iijima, Shungo*
Journal of Nuclear Science and Technology, 29(1), p.11 - 23, 1992/01
no abstracts in English
; Iijima, Tsutomu
Journal of Nuclear Science and Technology, 15(8), p.553 - 567, 1978/08
Times Cited Count：0no abstracts in English
Iijima, Tsutomu;
Journal of Nuclear Science and Technology, 14(9), p.682 - 684, 1977/09
Times Cited Count：1no abstracts in English
