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.

CityTransformer; A Transformer-based model for contaminant dispersion prediction in a realistic urban area

Asahi, Yuichi; Onodera, Naoyuki; Hasegawa, Yuta; Shimokawabe, Takashi*; Shiba, Hayato*; Idomura, Yasuhiro

Boundary-Layer Meteorology, 186(3), p.659 - 692, 2023/03

https://doi.org/10.1007/s10546-022-00777-8

We develop a Transformer-based deep learning model to predict the plume concentrations in the urban area under uniform flow conditions. Our model has two distinct input layers: Transformer layers for sequential data and convolutional layers in convolutional neural networks (CNNs) for image-like data. Our model can predict the plume concentration from realistically available data such as the time series monitoring data at a few observation stations and the building shapes and the source location. It is shown that the model can give reasonably accurate prediction with orders of magnitude faster than CFD simulations. It is also shown that the exactly same model can be applied to predict the source location, which also gives reasonable prediction accuracy.

Evaluations with autoencoder whether the image used for image recognition is appropriate

Nomura, Masahiro; Okita, Hidefumi; Shimada, Taihei; Tamura, Fumihiko; Yamamoto, Masanobu; Furusawa, Masashi*; Sugiyama, Yasuyuki*; Hasegawa, Katsushi*; Hara, Keigo*; Omori, Chihiro*; et al.

Proceedings of 18th Annual Meeting of Particle Accelerator Society of Japan (Internet), p.80 - 82, 2021/10

no abstracts in English

Annual report of Nuclear Human Resource Development Center (April 1, 2019 - March 31, 2020)

Nuclear Human Resource Development Center

JAEA-Review 2021-010, 70 Pages, 2021/09

JAEA-Review-2021-010.pdf:3.53MB

This annual report summarizes the activities of Nuclear Human Resource Development Center (NuHRDeC) of Japan Atomic Energy Agency (JAEA) in the fiscal year (FY) 2019.

Outline of Regional Workshops held in 2006 - 2017 by the International Atomic Energy Agency in the proposal of Nuclear Emergency Preparedness Group of the Asian Nuclear Safety Network

Okuno, Hiroshi; Yamamoto, Kazuya

JAEA-Review 2020-066, 32 Pages, 2021/02

JAEA-Review-2020-066.pdf:3.01MB

The International Atomic Energy Agency (abbreviated as IAEA) has been implementing the Asian Nuclear Safety Network (abbreviated as ANSN) activities since 2002. As part of this effort, Topical Group on Emergency Preparedness and Response (abbreviated as EPRTG) for nuclear or radiation disasters was established in 2006 under the umbrella of the ANSN. Based on the EPRTG proposal, the IAEA conducted 23 Asian regional workshops in the 12 years from 2006 to 2017. Typical topical fields of the regional workshops were nuclear emergency drills, emergency medical care, long-term response after nuclear/radiological emergency, international cooperation, national nuclear disaster prevention system. The Japan Atomic Energy Agency has produced coordinators for EPRTG since its establishment and has led its activities since then. This report summarizes the Asian regional workshops conducted by the IAEA based on the recommendations of the EPRTG.

Optimizing long-term monitoring of radiation air-dose rates after the Fukushima Daiichi Nuclear Power Plant

Sun, D.*; Wainwright-Murakami, Haruko*; Oroza, C. A.*; Seki, Akiyuki; Mikami, Satoshi; Takemiya, Hiroshi; Saito, Kimiaki

Journal of Environmental Radioactivity, 220-221, p.106281_1 - 106281_8, 2020/09

https://doi.org/10.1016/j.jenvrad.2020.106281

We have developed a methodology for optimizing the monitoring locations of radiation air dose-rate monitoring. For the method, we use a Gaussian mixture model to identify the representative locations among multiple environmental variables, such as elevation and land-cover types. Next, we use a Gaussian process model to capture and estimate the heterogeneity of air-dose rates across the domain. Our results have shown that this approach allows us to select monitoring locations in a systematic manner such that the heterogeneity of air dose rates is captured by the minimal number of monitoring locations.

Annual report of Nuclear Human Resource Development Center (April 1, 2017 - March 31, 2018)

Nuclear Human Resource Development Center

JAEA-Review 2019-009, 65 Pages, 2019/09

JAEA-Review-2019-009.pdf:5.56MB

This annual report summarizes the activities of Nuclear Human Resource Development Center (NuHRDeC) of Japan Atomic Energy Agency (JAEA) in the fiscal year (FY) 2017.

Determination of reactivity and neutron flux using modified neural network for HTGR

Subekti, M.*; Kudo, Kazuhiko*; Nabeshima, Kunihiko; Takamatsu, Kuniyoshi

Atom Indonesia, 43(2), p.93 - 102, 2017/08

https://doi.org/10.17146/aij.2017.683

Reactor kinetics based on point kinetic model have been generally applied as the standard method for neutronics codes. As the central control rod (C-CR) withdrawal test has demonstrated in a prismatic core of HTTR, the transient calculation of kinetic parameter, such as reactivity and neutron fluxes, requires a new method to shorten calculation-process time. Development of neural network method was applied to point kinetic model as the necessity of real-time calculation that could work in parallel with the digital reactivity meter. The combination of TDNN and Jordan RNN, such as TD-Jordan RNN, was the result of the modeling approach. The application of TD-Jordan RNN with adequate learning, tested offline, determined results accurately even when signal inputs were noisy. Furthermore, the preprocessing for neural network input utilized noise reduction as one of the equations to transform two of twelve time-delayed inputs into power corrected inputs.

Socio-economic effects of the material science in JAERI

Yanagisawa, Kazuaki; Takahashi, Shoji*

Scientometrics, 78(3), p.505 - 524, 2008/10

https://doi.org/10.1007/s11192-008-1636-4

A socio-economic evaluation of Material Science (MS) of JAERI was made. The goal was to reveal the emphasized basic research fields (EBRF) of MS and to observe its socio-economic networking. High ranked keywords for the former and the number of co-authored papers for the latter were used along with many MS related papers. The obtained results are: (1) The EBRF of MS of JAERI were typically represented by the keywords of ion irradiation, actinides, etc., i.e., those having a strong relation to the nuclear field. Regarding actinides, the socio-economic networking between JAERI and PS occurred at the growth rate of 3-4% per 25 years, but 8% during the past 5 years. This implies that the research cooperation between the two was markedly enhanced. (2) The EBRF of MS between JAERI and 5 selected research bodies (SRB) represented by Tokyo University was directly compared and revealed that only 7 keywords as typically represented by neutron and accelerators. After overlapping, JAERI and SRB seem to be raising the national standard level.

Development of a quake-proof information inference system by using data mining technology

Shu, Y.; Nakajima, Norihiro

Proceedings of 11th International Conference on Human-Computer Interaction (HCI International 2005) (CD-ROM), 9 Pages, 2005/07

To understand the behavior of NPP (nuclear power plant) under different operating environment, JAERI is carrying out full-scaled plant simulation. As one part of full scaled plant simulation, our ongoing work is to develop an information inference system to manage and interpret NPP quake-proof data. In this paper, we proposed a hybrid data mining approach, which integrates human cognitive model in a data mining loop. Rule-based mining control agent emulated human analysts directly interacts with the data miner, analyzing and verifying the output of data miner and controlling data mining process. In additional, artificial neural network method, which is adopted as a core component of the proposed hybrid data mining method, is evolved by adding the retraining facility and explaining function for handling complicated nuclear power plant quake-proof data. To demonstrate how the method can be used as a powerful tool for extracting information relevant to plant safety and reliability, plant quake-proof testing data have been applied to the inference system.