Estimation of the beam trip frequency of a proton linear accelerator for an accelerator-driven nuclear transmutation system and comparison with the allowable beam trip frequency

Takei, Hayanori

Journal of Nuclear Science and Technology, 45 Pages, 2025/06

https://doi.org/10.1080/00223131.2025.2523023

The Japan Atomic Energy Agency is working on the research and development of an accelerator-driven nuclear transmutation system (ADS) for transmuting minor actinides. This system combines a subcritical nuclear reactor with a high-power superconducting proton linear accelerator (JADS-linac). One of the factors limiting the advancement of the JADS-linac is beam trips, which often induce thermal cycle fatigue, thereby damaging the components in the subcritical core. The average beam current of the JADS-linac is 32 times higher than that of the linear accelerator (linac) of the Japan Proton Accelerator Research Complex (J-PARC). Therefore, according to the development stage, comparing the beam trip frequency of the JADS-linac with the allowable beam trip frequency (ABTF) is necessary. Herein the beam trip frequency of the JADS-linac was estimated through a Monte Carlo program using the reliability functions based on the operational data of the J-PARC linac. The Monte Carlo program afforded the distribution of the beam trip duration, which cannot be obtained using traditional analytical methods. Results show that the frequency of the beam trips with a duration exceeding 5 min must be reduced to 27% of the current J-PARC linac level to be below the ABTF.

Ingestion doses from radionuclides in seafood before and after the Fukushima Daiichi Nuclear Power Plant accident

Mori, Airi; Johansen, M. P.*; McGinnity, P.*; Takahara, Shogo

Communications Earth & Environment (Internet), 6, p.356_1 - 356_11, 2025/05

https://doi.org/10.1038/s43247-025-02338-6

Review of JAEA's Monte Carlo codes for nuclear reactor core analysis

Nagaya, Yasunobu

EPJ Nuclear Sciences & Technologies (Internet), 11, p.1_1 - 1_7, 2025/01

https://doi.org/10.1051/epjn/2024020

Japan Atomic Energy Agency (JAEA) has been developing a general-purpose continuous-energy Monte Carlo code MVP for nuclear reactor core analysis. Recently improvements to MVP have been focused on the development of an advanced neutronics/thermal-hydraulics coupling code. JAEA has also developed a new Monte Carlo solver Solomon for criticality safety analysis. Solomon aims to calculate the criticality of a damaged reactor core including fuel debris. This paper provides an overview of the capabilities and reviews recent applications of MVP and Solomon.

Possible criticality scenario and its mechanism of the Windscale Works criticality accident in 1970 analyzed by computational fluid dynamics and Monte Carlo neutron transport

Fukuda, Kodai

Annals of Nuclear Energy, 208(1), p.110748_1 - 110748_10, 2024/12

https://doi.org/10.1016/j.anucene.2024.110748

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, 61(7), p.935 - 957, 2024/07

https://doi.org/10.1080/00223131.2023.2290067

Neutron importance estimation via new recursive Monte Carlo method for deep penetration neutron transport

Tuya, D.; Nagaya, Yasunobu

Nuclear Science and Engineering, 198(5), p.1021 - 1035, 2024/05

https://doi.org/10.1080/00295639.2023.2233850

In Monte Carlo neutron transport calculations for local response or deep penetration problems, some estimation of an importance function is generally required in order to improve their efficiency. In this work, a new recursive Monte Carlo (RMC) method, which is partly based on the original RMC method, for estimating an importance function for local variance reduction (i.e., source-detector type) problems has been developed. The new RMC method has been applied to two sample problems of varying degrees of neutron penetrations, namely a one-dimensional iron slab problem and a three-dimensional concrete-air problem. The biased Monte Carlo calculations with variance reduction parameters based on the obtained importance functions by the new RMC method have been performed to estimate detector responses in these problems. The obtained results are in agreement with those by the reference unbiased Monte Carlo calculations. Furthermore, the biased calculations offered an increase in efficiency on the order of 1 to 10 in terms of the figure of merit (FOM). The results also indicated that the efficiency increased as the neutron penetration became deeper.

Comparing DGSMC, FIER, and FISPACT simulations to experimental delayed gamma-ray spectra for nuclear safeguards development

Rodriguez, D.; Rossi, F.; Takahashi, Tone

IEEE Transactions on Nuclear Science, 71(3), p.255 - 268, 2024/03

https://doi.org/10.1109/TNS.2024.3353816

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.