Development of a surrogate system of a plant dynamics simulation model and an abnormal situation identification system for nuclear power plants using deep neural networks

Seki, Akiyuki; Yoshikawa, Masanori; Nishinomiya, Ryota*; Okita, Shoichiro; Takaya, Shigeru; Yan, X.

Nuclear Technology, 12 Pages, 2024/00

https://doi.org/10.1080/00295450.2023.2273566

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.

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.

AMR-Net: Convolutional neural networks for multi-resolution steady flow prediction

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

https://doi.org/10.1109/Cluster48925.2021.00102

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.

A Learning data collection using a simulator for point cloud based identification system

Tanifuji, Yuta; Kawabata, Kuniaki

Proceedings of International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2020) (Internet), p.246 - 249, 2020/02

A Structure discrimination method by deep learning with point cloud data

Tanifuji, Yuta; Kawabata, Kuniaki

Proceedings of International Topical Workshop on Fukushima Decommissioning Research (FDR 2019) (Internet), 4 Pages, 2019/05

Predicting plume concentrations in the urban area using a deep learning model

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.

Development of AI system to support safety operation of nuclear power plants

Seki, Akiyuki; Yoshikawa, Masanori; Okita, Shoichiro; Takaya, Shigeru; Yan, X.

no journal, , 

Targeting exa-scale systems; Performance portability and scalable data analysis

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.

Generating observation guided ensembles for data assimilation with denosing diffusion probabilistic model

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.