Abstract
A deep learning based surrogate model is proposed for replacing the conventional diffusion equation solver and predicting the flux and power distribution of the reactor core. Using the training data generated by the conventional diffusion equation solver, a special designed convolutional neural network inspired by the FCN (Fully Convolutional Network) is trained under the deep learning platform TensorFlow. Numerical results show that the deep learning based surrogate model is effective for estimating the flux and power distribution calculated by the diffusion method, which means it can be used for replacing the conventional diffusion equation solver with high efficiency boost.
Highlights
The deep learning technology is widely used in the area of image identification and natural language processing
Numerical results show that the deep learning based surrogate model is effective for estimating the flux and power distribution calculated by the diffusion code and the accuracy superior to the conventional artificial neural network
The more important fact is that the efficiency of deep learning only depends on the scale of core problem and the internal layers of the network and it does not depend on the method used for data generation
Summary
The deep learning technology is widely used in the area of image identification and natural language processing. The deep learning has been investigated in the area of numerical simulation. Several publications proposed studies on solving partial differential equation [1,2] and in the area of mechanics [3] and fluid dynamics [4], the deep learning approach was discussed. The deep learning study on the topic of reactor physics has not been found before 2019. There are many studies on the surrogate model based on artificial neural network used for fuel management in 1990s [6, 7]. The conventional artificial neural network has the following limitations: 1.
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