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Uesawa, Shinichiro; Yoshida, Hiroyuki
Journal of Nuclear Science and Technology, 61(11), p.1438 - 1452, 2024/11
Times Cited Count：0 Percentile：0.00(Nuclear Science & Technology)We developed a deep learning-based bubble detector with a Shifted window Transformer (Swin Transformer) to detect and segment individual bubbles among overlapping bubbles. To verify the performance of the detector, we calculated its average precision (AP) with different number of training images. The mask AP increased with the increase in the number of training images when there were less than 50 images but remained constant when there were more than 50 images. It was observed that the AP for the Swin Transformer and ResNet were almost the same when there were more than 50 images; however, when few training images were used, the AP of the Swin Transformer were higher than that of the ResNet. Furthermore, with regard to the increase in void fraction, the AP of the Swin Transformer showed a decrease similar to that in the case of the ResNet; however, for few training images, the AP of the Swin Transformer was higher than that of the ResNet in all void fractions. Moreover, we confirmed the detector trained with synthetic bubble images was able to segment overlapping bubbles and deformed bubbles in a bubbly flow experiment. Thus, we verified that the new bubble detector with Swin Transformer provided higher AP than the detector with ResNet for fewer training images.
Tanifuji, Yuta; Hanari, Toshihide; Kawabata, Kuniaki
Proceedings of International Topical Workshop on Fukushima Decommissioning Research 2024 (FDR2024) (Internet), 3 Pages, 2024/10
Seki, Akiyuki; Yoshikawa, Masanori; Nishinomiya, Ryota*; Okita, Shoichiro; Takaya, Shigeru; Yan, X.
Nuclear Technology, 210(6), p.1003 - 1014, 2024/06
Times Cited Count：0 Percentile：0.00(Nuclear Science & Technology)Two types of deep neural network (DNN) systems have been constructed with the intent to assist safety operation of a nuclear power plant. One is a surrogate system (SS) that can estimate physical quantities of a nuclear power plant in a computational time of several orders less than a physical simulation model. The other is an abnormal situation identification system (ASIS) that can estimate the state of the disturbance causing an anomaly from physical quantities of a nuclear power plant. Both systems are trained and tested using data obtained from the analytical code for incore and plant dynamics (ACCORD), which reproduces the steady and dynamic behavior of the actual high Temperature engineering test reactor (HTTR) under various scenarios. The DNN models are built by adjusting, the main hyperparameters. Through these procedures, these systems are shown able to perform with a high degree of accuracy.
Asahi, Yuichi; Maeyama, Shinya*; Fujii, Keisuke*
Keisan Kogaku Koenkai Rombunshu (CD-ROM), 28, 4 Pages, 2023/05
We have developed a deep-learning model to surrogate the effect of small-scale on large-scale fluctuations. We have constructed the sub-grid-scale (SGS) models based on the Mori-Zwanzig projection operatormethod and neural networks. We have performed large eddy simulations (LESs) of the Kuramoto-Sivashinsky turbulence with these SGS models. We have demonstrated that the time averaged energy spectrumof LESs agree with that of the dynamic numerical simulation (DNS).
Dong, F.*; Chen, S.*; Demachi, Kazuyuki*; Yoshikawa, Masanori; Seki, Akiyuki; Takaya, Shigeru
Nuclear Engineering and Design, 404, p.112161_1 - 112161_15, 2023/04
Times Cited Count：24 Percentile：99.21(Nuclear Science & Technology)Asahi, Yuichi; Onodera, Naoyuki; Hasegawa, Yuta; Shimokawabe, Takashi*; Shiba, Hayato*; Idomura, Yasuhiro
Boundary-Layer Meteorology, 186(3), p.659 - 692, 2023/03
Times Cited Count：2 Percentile：25.47(Meteorology & Atmospheric Sciences)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.
Yoshida, Yukihiko
IL Nuovo Cimento, 46(2), p.33_1 - 33_8, 2023/03
Asahi, Yuichi; Hatayama, Sora*; Shimokawabe, Takashi*; Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro
Proceedings of 2021 IEEE International Conference on Cluster Computing (IEEE Cluster 2021) (Internet), p.686 - 691, 2021/10
Times Cited Count：3 Percentile：69.51(Computer Science, Hardware & Architecture)We develop a convolutional neural network model to predict the multi-resolution steady flow. Based on the state-of-the-art image-to-image translation model pix2pixHD, our model can predict the high resolution flow field from the set of patched signed distance functions. By patching the high resolution data, the memory requirements in our model is suppressed compared to pix2pixHD.
Asahi, Yuichi; Hatayama, Sora*; Shimokawabe, Takashi*; Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro
Keisan Kogaku Koenkai Rombunshu (CD-ROM), 26, 4 Pages, 2021/05
We develop a convolutional neural network model to predict the multi-resolution steady flow. Based on the state-of-the-art image-to-image translation model Pix2PixHD, our model can predict the high resolution flow field from the signed distance function. By patching the high resolution data, the memory requirements in our model is suppressed compared to Pix2PixHD.
Tanifuji, Yuta; Kawabata, Kuniaki
Proceedings of International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2020) (Internet), p.246 - 249, 2020/02
Tanifuji, Yuta; Kawabata, Kuniaki; Hanari, Toshihide
Proceedings of 2019 IEEE Region Ten Conference (TENCON 2019) (Internet), p.36 - 40, 2019/10
Tanifuji, Yuta; Kawabata, Kuniaki
Proceedings of International Topical Workshop on Fukushima Decommissioning Research (FDR 2019) (Internet), 4 Pages, 2019/05
Seki, Akiyuki; Yoshikawa, Masanori; Okita, Shoichiro; Takaya, Shigeru; Yan, X.
no journal, ,
Asahi, Yuichi; Maeyama, Shinya*; Bigot, J.*; Garbet, X.*; Grandgirard, V.*; Obrejan, K.*; Padioleau, T.*; Fujii, Keisuke*; Shimokawabe, Takashi*; Watanabe, Tomohiko*; et al.
no journal, ,
We will demonstrate the performance portable implementation of a kinetic plasma code over CPUs, Nvidia and AMD GPUs. We will also discuss the performance portability of the code with C++ parallel algorithm. Deep learning based surrogate models for fluid simulations will also be demonstrated.
Asahi, Yuichi; Maeyama, Shinya*; Bigot, J.*; Garbet, X.*; Grandgirard, V.*; Obrejan, K.*; Padioleau, T.*; Fujii, Keisuke*; Shimokawabe, Takashi*; Watanabe, Tomohiko*; et al.
no journal, ,
We will demonstrate the performance portable implementation of a kinetic plasma code over CPUs, Nvidia and AMD GPUs. We will also discuss the performance portability of the code with C++ parallel algorithm. Deep learning based surrogate models for fluid simulations will also be demonstrated.
Asahi, Yuichi; Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro
no journal, ,
We have developed a convolutional neural network (CNN) model to predict the plume concentrations in the urbanarea under uniform flow condition. By combining the Transformer or Multilayer Perceptron (MLP) layers with CNN model, our model can predict the plume concentrations from the building shapes, release points of plumeand time series data at observation stations.
Murayama, Masahiro*; Harazono, Yuki*; Ishii, Hirotake*; Shimoda, Hiroshi*; Taruta, Yasuyoshi; Koda, Yuya
no journal, ,
no abstracts in English
Asahi, Yuichi; Maeyama, Shinya*; Bigot, J.*; Garbet, X.*; Grandgirard, V.*; Fujii, Keisuke*; Shimokawabe, Takashi*; Watanabe, Tomohiko*; Idomura, Yasuhiro; Onodera, Naoyuki; et al.
no journal, ,
We have established an in-situ data analysis method for large scale fluid simulation data and developed deep learning based surrogate models to predict fluid simulation results. Firstly, we have developed an in-situ data processing approach, which loosely couples the MPI application and python scripts. It has been shown that this approach is simple and efficient which offers the speedup of 2.7 compared to post hoc data processing. Secondly, we have developed a deep learning model for predicting multiresolution steady flow fields. The deep learning model can give reasonably accurate predictions of simulation results with orders of magnitude faster compared to simulations.
Dong, F.*; Chen, S.*; Demachi, Kazuyuki*; Hashidate, Ryuta; Takaya, Shigeru
no journal, ,
Piping and Instrumentation Diagrams contain information about the piping and process equipment together with the instrumentation and control devices, which is essential to the design and management of Nuclear Power Plants. There are abundant complex objects on P&IDs, with imbalanced distribution of these objects and their linked information across different diagrams. Therefore, the content of P&IDs is generally extracted and analyzed manually, which is time consuming and error prone. To efficiently address these issues, we integrate state-of-the-art deep learning-based object detection and Optical Character Recognition models to automatically extract link information from P&IDs. Besides, we propose a novel image pre-processing approach using sliding windows to detect low resolution small objects. The performance of the proposed approach was experimentally evaluated, and the experimental results demonstrate it capable to extract link information from P&IDs of NPPs.
Asahi, Yuichi; Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro
no journal, ,
We have developed a convolutional neural network (CNN) model to predict the plume concentrations in the urban area under uniform flow condition. By combining the Transformer or Multilayer Perceptron (MLP) layers with CNN model, our model can predict the plume concentrations from the building shapes, release points of plume and time series data at observation stations. It is also shown that the exactly same model can be applied to predict the source location, which also gives reasonable prediction accuracy.
Asahi, Yuichi; Hasegawa, Yuta; Onodera, Naoyuki; Shimokawabe, Takashi*; Shiba, Hayato*; Idomura, Yasuhiro
no journal, ,
This paper presents a data assimilation (DA) method using the pseudo ensembles generated by denoising diffusion probabilistic model. Since the model is trained against noisy and sparse observation data, this method can produce reasonable ensembles consistent with observations. This method displays better performance than well-established DA method when the simulation model is imperfect.
