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Development and validation of a deep learning‐based model for predicting burnup nuclide density

Development and validation of a deep learning‐based model for predicting burnup nuclide density To address the issue of large inaccuracies in the low‐burnup region of aditonal machine learning algorithms for predicting nuclide density, the DRAGON code is used to produce 9600 samples using the nuclide densities of 235U, 239Pu, 241Pu, 137Cs, and 154Nd as prediction parameters. The mean square error (MSE) was used as the loss function for the deep neural network‐based nuclide density prediction model. The trained model is used to predict the target nuclides in the test set, and the relative error with the multilayer perceptron model are compared. The prediction results demonstrate that the deep neural network‐based prediction model not only overcomes the issue of excessive prediction errors in the low‐burnup region of the traditional machine learning algorithm model, but also has lower prediction errors in the medium‐burnup and high‐burnup regions, demonstrating the feasibility of artificial intelligence in nuclide density prediction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Energy Research Wiley

Development and validation of a deep learning‐based model for predicting burnup nuclide density

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References (22)

Publisher
Wiley
Copyright
© 2022 John Wiley & Sons, Ltd.
ISSN
0363-907X
eISSN
1099-114X
DOI
10.1002/er.8338
Publisher site
See Article on Publisher Site

Abstract

To address the issue of large inaccuracies in the low‐burnup region of aditonal machine learning algorithms for predicting nuclide density, the DRAGON code is used to produce 9600 samples using the nuclide densities of 235U, 239Pu, 241Pu, 137Cs, and 154Nd as prediction parameters. The mean square error (MSE) was used as the loss function for the deep neural network‐based nuclide density prediction model. The trained model is used to predict the target nuclides in the test set, and the relative error with the multilayer perceptron model are compared. The prediction results demonstrate that the deep neural network‐based prediction model not only overcomes the issue of excessive prediction errors in the low‐burnup region of the traditional machine learning algorithm model, but also has lower prediction errors in the medium‐burnup and high‐burnup regions, demonstrating the feasibility of artificial intelligence in nuclide density prediction.

Journal

International Journal of Energy Research – Wiley

Published: Dec 1, 2022

Keywords: burnup prediction; deep learning; deep neural network; DRAGON; nuclide density

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