Development and validation of a deep learning‐based model for predicting burnup nuclide density-Reference-Cited by-同舟云学术

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

Published:2022-08-02 Issue:15 Volume:46 Page:21257-21265
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Intl J of Energy Research

Author:

Lei Jichong¹²^ORCID,Yang Chao¹²,Ren Changan¹³,Li Wei¹,Liu Chengwei¹²,Sun Aikou¹²,Li Yukun¹²,Chen Zhenping¹²,Yu Tao¹²

Affiliation:

1. School of Nuclear Science and Technology University of South China Hengyang China

2. Research Center for Digital Nuclear Reactor Engineering and Technology of Hunan Province University of South China Hengyang China

3. College of Computer Science & Engineering Hunan Institute of Technology Hengyang China

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.8338

Reference21 articles.

1. Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions

2. Radionuclide neutron source trajectories in the closed nuclear fuel cycle

3. Prediction of burn‐up nucleus density based on machine learning

4. Linear Chain Method for Numerical Modelling of Burnup Systems

5. Trajectory periods folding method for modeling of uranium and thorium fuel transmutations;Oettingen M;Armen J Phys,2019

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