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Journal Articles

LASSO reconstruction scheme to predict radioactive source distributions inside reactor building rooms; Practical applications

Machida, Masahiko; Shi, W.*; Yamada, Susumu; Miyamura, Hiroko; Yoshida, Toru*; Hasegawa, Yukihiro*; Okamoto, Koji; Aoki, Yuto; Ito, Rintaro; Yamaguchi, Takashi; et al.

Proceedings of Waste Management Symposia 2023 (WM2023) (Internet), 11 Pages, 2023/02

Journal Articles

Sensitivity coefficient evaluation of an accelerator-driven system using ROM-Lasso method

Katano, Ryota; Yamamoto, Akio*; Endo, Tomohiro*

Nuclear Science and Engineering, 196(10), p.1194 - 1208, 2022/10

 Times Cited Count:0 Percentile:33.72(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.

Journal Articles

Proposal and application of ROM-Lasso method for sensitivity coefficient evaluation

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.

Journal Articles

Estimation of sensitivity coefficient based on lasso-type penalized linear regression

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:30.45(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.

Oral presentation

Investigation on distribution of radioactive substances in Fukushima, 4; Study on the ecological half-lives of ambient dose rates using car-borne survey data with fused LASSO

Liu, X.; Machida, Masahiko; Saito, Kimiaki; Tanimura, Naoki*

no journal, , 

Ecological half-life has been widely used to describe the long-term decrease of ambient dose rates in addition to radioactive decay. In the present work, we introduce a new, robust, and efficient numerical method to extract space dependent ecological half-lives from car-borne survey data. Numerical results indicate complexed ecological half-life profiles, which are originated from their spatial patterns.

Oral presentation

Estimation of radiation source distribution using LASSO

Yamada, Susumu; Machida, Masahiko

no journal, , 

no abstracts in English

Oral presentation

Anthropogenic impacts on temporal decrease in air dose rate in urban areas, 3; Correlation between population dynamics and ecological half-life decay estimated by using LASSO

Machida, Masahiko; Uno, Shumpei*; Tanimura, Naoki*; Saito, Kimiaki; Yoshimura, Kazuya

no journal, , 

Air dose rates in urban areas decrease faster in residential areas than in evacuation zones. This suggests that human activities facilitate the reduction. However, since human activities include various factors such as decontamination and road wear caused by traffic, it is difficult to evaluate the effect of human activities by conventional analysis using monitoring data, except for the effect of decontamination. Then, this series of studies aim to evaluate the effects of human activities on the reduction. Among the series presentations, this one focuses on correlations between population dynamics characterized by mobile phone GPS information and ecological half life decays of air dose rates measured by KURAMA over a wide area with long-term monitoring using LASSO scheme.

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