A prediction model for thermal conductivity of metallic nuclear fuel based on multiple machine learning models-Reference-Cited by-同舟云学术

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A prediction model for thermal conductivity of metallic nuclear fuel based on multiple machine learning models

Published:2023-09 Issue: Volume:583 Page:154553
ISSN:0022-3115
Container-title:Journal of Nuclear Materials
language:en
Short-container-title:Journal of Nuclear Materials

Author:

Lu Yong,Huang Xiaoyi,Ren Zhiyuan,Sun Dan,Guo Yihui,Liu Xingjun,Wang Cuiping

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering,General Materials Science,Nuclear and High Energy Physics

Reference54 articles.

1. Metallic fast reactor fuels;Hofman;Prog. Nucl. Energy,1997

2. Metallic fuels for advanced reactors;Carmack;J. Nucl. Mater.,2009

3. An Innovative High Thermal Conductivity Fuel Design;Tulenko,2007

4. Fuel Thermo-physical Characterization Project. Fiscal Year 2014 Final Report;Burkes,2015

5. Thermal properties of U–Mo alloys irradiated to moderate burnup and power;Burkes;J. Nucl. Mater.,2015

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of thermodynamic stability of actinide compounds by machine learning model;Ceramics International;2024-01

2. A multiclass classification model for predicting the thermal conductivity of uranium compounds;Journal of Nuclear Science and Technology;2023-10-20

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