Distinguishing ion dynamics from muon diffusion in muon spin relaxation

Ito, Takashi; Kadono, Ryosuke*

Journal of the Physical Society of Japan, 93(4), p.044602_1 - 044602_7, 2024/04

https://doi.org/10.7566/JPSJ.93.044602

Estimation of continuous distribution of iterated fission probability using an artificial neural network with Monte Carlo-based training data

Tuya, D.; Nagaya, Yasunobu

Journal of Nuclear Engineering (Internet), 4(4), p.691 - 710, 2023/11

https://doi.org/10.3390/jne4040043

The Monte Carlo method is used to accurately estimate various quantities such as k-eigenvalue and integral neutron flux. However, when a distribution of a quantity is desired, the Monte Carlo method does not typically provide continuous distribution. Recently, the functional expansion tally and kernel density estimation methods have been developed to provide continuous distribution. In this paper, we propose a method to estimate a continuous distribution of a quantity using artificial neural network (ANN) model with Monte Carlo-based training data. As a proof of concept, a continuous distribution of iterated fission probability (IFP) is estimated by ANN models in two systems. The IFP distributions by the ANN models were compared with the Monte Carlo-based data and the adjoint angular neutron fluxes by the PARTISN code. The comparisons showed varying degrees of agreement or discrepancy; however, it was observed that the ANN models learned the general trend of the IFP distributions.

Application of quasi-Monte Carlo and importance sampling to Monte Carlo-based fault tree quantification for seismic probabilistic risk assessment of nuclear power plants

Kubo, Kotaro; Tanaka, Yoichi; Hakuta, Yuto*; Arake, Daisuke*; Uchiyama, Tomoaki*; Muramatsu, Ken

Mechanical Engineering Journal (Internet), 10(4), p.23-00051_1 - 23-00051_17, 2023/08

https://doi.org/10.1299/mej.23-00051

The significance of probabilistic risk assessments (PRAs) of nuclear power plants against external events was re-recognized after the Fukushima Daiichi Nuclear Power Plant accident. Regarding the seismic PRA, handling correlated failures of systems, components, and structures (SSCs) is very important because this type of failure negatively affects the redundancy of accident mitigation systems. The Japan Atomic Energy Research Institute initially developed a fault tree quantification methodology named the direct quantification of fault tree using Monte Carlo simulation (DQFM) to handle SSCs' correlated failures in detail and realistically. This methodology allows quantifying the top event occurrence probability by considering correlated uncertainties related to seismic responses and capacities with Monte Carlo sampling. The usefulness of DQFM has already been demonstrated. However, improving its computational efficiency would allow risk analysts to perform several analyses. Therefore, we applied quasi-Monte Carlo and importance sampling to the DQFM calculation of simplified seismic PRA and examined their effects. Specifically, the conditional core damage probability of a hypothetical pressurized water reactor was analyzed with some assumptions. Applying the quasi-Monte Carlo sampling accelerates the convergence of results at intermediate and high ground motion levels by an order of magnitude over Monte Carlo sampling. The application of importance sampling allows us to obtain a statistically significant result at a low ground motion level, which cannot be obtained through Monte Carlo and quasi-Monte Carlo sampling. These results indicate that these applications provide a notable acceleration of computation and raise the potential for the practical use of DQFM in risk-informed decision-making.

Cavitation damage prediction in mercury target for pulsed spallation neutron sources by Monte Carlo simulation

Wakui, Takashi; Takagishi, Yoichi*; Futakawa, Masatoshi; Tanabe, Makoto*

Jikken Rikigaku, 23(2), p.168 - 174, 2023/06

Cavitation damage on the inner surface of the mercury target for the spallation neutron source occurs by proton bombarding in mercury. The prediction method of the cavitation damage using Monte Carlo simulations was suggested taking variability of the bubble core position and impact pressure distribution into account. The impact pressure distribution was estimated using the inverse analysis with Bayesian optimization was conducted with comparison between cavitation damage distribution obtained from experiment and the cumulative plastic strain distribution obtained from simulation. The average value and spread of maximum impact pressure estimated assuming the Gaussian distribution were 3.1 GPa and 1.2 m, respectively. Simulation results reproduced experimental results and it can be said that this evaluation method is useful.

JAEA/ISCN delayed gamma-ray spectroscopy inverse Monte Carlo development status

Rodriguez, D.; Rossi, F.

Proceedings of INMM & ESARDA Joint Annual Meeting 2023 (Internet), 9 Pages, 2023/05

The Development of Petri Net-based continuous Markov Chain Monte Carlo methodology applying to dynamic probability risk assessment for multi-state resilience systems with repairable multi-component interdependency under longtermly thereat

Li, C.-Y.; Watanabe, Akira*; Uchibori, Akihiro; Okano, Yasushi

Journal of Nuclear Science and Technology, 23 Pages, 2023/00

https://doi.org/10.1080/00223131.2023.2290067

Evaluation of power distribution calculation of the very high temperature reactor critical assembly (VHTRC) with Monte Carlo MVP3 code

Simanullang, I. L.*; Nakagawa, Naoki*; Ho, H. Q.; Nagasumi, Satoru; Ishitsuka, Etsuo; Iigaki, Kazuhiko; Fujimoto, Nozomu*

Annals of Nuclear Energy, 177, p.109314_1 - 109314_8, 2022/11

https://doi.org/10.1016/j.anucene.2022.109314

Uncertainty analysis of dynamic PRA using nested Monte Carlo simulations and multi-fidelity models

Zheng, X.; Tamaki, Hitoshi; Takahara, Shogo; Sugiyama, Tomoyuki; Maruyama, Yu

Proceedings of Probabilistic Safety Assessment and Management (PSAM16) (Internet), 10 Pages, 2022/09

https://www.iapsam.org/PSAM16/

A Scoping study on the use of direct quantification of fault tree using Monte Carlo simulation in seismic probabilistic risk assessments

Kubo, Kotaro; Fujiwara, Keita*; Tanaka, Yoichi; Hakuta, Yuto*; Arake, Daisuke*; Uchiyama, Tomoaki*; Muramatsu, Ken*

Proceedings of 29th International Conference on Nuclear Engineering (ICONE 29) (Internet), 8 Pages, 2022/08

https://doi.org/10.1115/ICONE29-88773

After the Fukushima Daiichi Nuclear Power Plant accident, the importance of conducting probabilistic risk assessments (PRAs) of external events, especially seismic activities and tsunamis, was recognized. The Japan Atomic Energy Agency has been developing a computational methodology for seismic PRA, called the direct quantification of fault tree using Monte Carlo simulation (DQFM). When appropriate correlation matrices are available for seismic responses and capacities of components, the DQFM makes it possible to consider the effect of correlated failures of components connected through AND and/or OR gates in fault trees, which is practically difficult when methods using analytical solutions or multidimensional numerical integrations are used to obtain minimal cut set probabilities. The usefulness of DQFM has already been demonstrated. Nevertheless, a reduction of the computational time of DQFM would allow the large number of analyses required in PRAs conducted by regulators and/or operators. We; therefore, performed scoping calculations using three different approaches, namely quasi-Monte Carlo sampling, importance sampling, and parallel computing, to improve calculation efficiency. Quasi-Monte Carlo sampling, importance sampling, and parallel computing were applied when calculating the conditional core damage probability of a simplified PRA model of a pressurized water reactor, using the DQFM method. The results indicated that the quasi-Monte Carlo sampling works well at assumed medium and high ground motion levels, importance sampling is suitable for assumed low ground motion level, and that parallel computing enables practical uncertainty and importance analysis. The combined implementation of these improvements in a PRA code is expected to provide a significant acceleration of computation and offers the prospect of practical use of DQFM in risk-informed decision-making.

Adjoint-weighted correlated sampling for -eigenvalue perturbation in Monte Carlo calculation

Tuya, D.; Nagaya, Yasunobu

Annals of Nuclear Energy, 169, p.108919_1 - 108919_9, 2022/05

https://doi.org/10.1016/j.anucene.2021.108919

Estimating an effect of a perturbation in a fissile system on its -eigenvalue requires special technique called perturbation theory when the considered perturbation is small. In this study, we develop an adjoint-weighted correlated sampling (AWCS) method based on the exact perturbation theory without any approximation by combining the correlated sampling (CS) method with iterated-fission probability (IFP) based adjoint-weighting method. With the advantages of the CS method being good at providing very small uncertainty for small perturbations and the IFP-based adjoint-weighting method being suitable for continuous-energy Monte Carlo calculation, the developed AWCS method based on the exact perturbation theory offers a new rigorous approach for perturbation calculations. The obtained results by the developed AWCS method for verification problems involving Godiva and simplified STACY density perturbations showed good agreement with the reference calculations.