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Ugolini; Yoshikawa, Shinji; Ozawa, Kenji
PNC TN9410 95-210, 11 Pages, 1995/09
The proper control of the outlet steam temperature of the evaporator is of major importance for improving the overall performance of the balance of plant of a nuclear power reactor. This report presents a predictive and an anticipatory control algorithms based on the artificial neural network (ANN) technique. The two control algorithms are embedded on a model reference adaptive control system based on the ANN technique, defined as MRAC
. It has already been illustrated that nonlinear dynamical systems such as the evaporator of a nuclear power plant can be controlled by an MRAC system. However, little attention has been devoted on exploiting the forecasting potential of the ANN technique for enhancing the accuracy and improving the efficacy of the control action of the MRAC system. The improved MRAC system has been tested to simulate the behavior of a fast breeder reactor (FBR) evaporator and to control its outlet steam temperature. The simulation results indicate that the performance of the MRAC system substantially improves when the predictive and the anticipatory control algorithms are activated.
Hatayama, Sora*; Shimokawabe, Takashi*; Onodera, Naoyuki
no journal, ,
Computational fluid dynamics (CFD) is widely used as a fluid analysis technique. However, these have a problem that the calculation cost is very expensive and the execution time for reaching a steady-state is long. To solve this problem, we use convolutional neural networks (CNN), which is one of the deep learning methods, to predict CFD results. In this research, we provide the method and implementation of steady flow prediction using CNN with boundary exchange to predict the CFD results in a large area.
Hatayama, Sora*; Shimokawabe, Takashi*; Onodera, Naoyuki
no journal, ,
We propose a prediction method for large-scale simulation results by dividing the input geometry into multiple parts and applying a single small neural network to each part in parallel. The constructed model predicts a two-dimensional velocity field using a signed distance function as input. In addition, we divide a large area into multiple regions and the prediction is iteratively performed for each region until convergence. Finally, we confirmed that the velocity fields of multiple regions are reproduced by using a boundary exchange method.
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.
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.
