- 1Department of Nuclear Engineering and Engineering Physics, University of Wisconsin-Madison, Madison, WI, United States
- 2Idaho National Laboratory (DOE), Idaho Falls, ID, United States
- 3Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Rome, Lazio, Italy
- 4Nuclear Safety Consultancy Netherlands, Amsterdam, Netherlands
- 5Department of Nuclear Engineering, Purdue University, West Lafayette, IN, United States
Editorial on the Research Topic
Rising stars in nuclear energy: 2022
Nuclear provides over 50% of American clean energy (DOE_Office_of_Nuclear_Energy, 2022), as one of the most realistic solutions to the global energy supply risk (Cui et al., 2023). The conventional neutronic, thermal hydraulics, Instrumentation and Control technologies are mature and being used in the Gen IV reactor designs (Locatelli et al., 2013). Meanwhile, advanced materials, machine learning and multi-physical technologies are being developed and push the advances in nuclear energy.
Tens of thousands of the young generation of nuclear scientists and engineers are making this happen. This Research Topic collects several articles, to show some of their contributions. The content covers three aspects: machine learning, advanced numerical calculation, and basic thermal hydraulics.
The machine learning parts collect two articles, the “Deep Learning Health Management Diagnostics applied to NIST Smoke Experiments” by (Hoppman et al.); and “Combinatorial Techniques for Fault Diagnosis in Nuclear Power Plants Based on Bayesian Neural Network and Simplified Bayesian Network-Artificial Neural Network” by (Qi et al.).
The advanced numerical calculation parts collect two articles, the “Influence of structural and operating factors on mixing transfer of rotary energy recovery device through CFD simulation” by (Liu et al.); “Numerical investigation of cooling ability in heat-generating porous debris bed after severe accident in PWR” by (Xu et al.).
The basic thermal hydraulics parts collect two articles, the “Study on the influences of RPV deformation on CHF under IVR conditions” by (Wen et al.), and the “Hierarchical entropy analysis for flow pattern of steam-liquid two phase flow in a rod bundle” by (Zhang et al.).
Due to the limit of time and network, there are various great jobs that cannot be collected into this Research Topic. However, there will be similar Research Topic in the future and all articles are welcome.
Author contributions
JW is the leading and corresponding author, all others are contributors. All authors contributed to the article and approved the submitted version.
Conflict of interest
Author GV was employed by Nuclear Safety Consultancy Netherlands.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Cui, L., Yue, S., Nghiem, X.-H., and Duan, M. (2023). Exploring the risk and economic vulnerability of global energy supply chain interruption in the context of Russo-Ukrainian war. Resour. Policy 81, 103373. doi:10.1016/j.resourpol.2023.103373
Doe_Office_of_Nuclear_Energy, (2022). 5 fast facts about nuclear energy. Washington, United State: Department of Energy. Available: https://www.energy.gov/ne/articles/5-fast-facts-about-nuclear-energy (Accessed April, 2023).
Keywords: risking stars, nuclear energy, machine learning, GEN IV reactors, thermal hydraulics
Citation: Wang J, Bao H, Bersano A, Vayssier G and Revankar ST (2023) Editorial: Rising stars in nuclear energy: 2022. Front. Energy Res. 11:1208426. doi: 10.3389/fenrg.2023.1208426
Received: 19 April 2023; Accepted: 04 May 2023;
Published: 10 May 2023.
Edited and reviewed by:
Uwe Schroder, University of Greifswald, GermanyCopyright © 2023 Wang, Bao, Bersano, Vayssier and Revankar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jun Wang, anV3YW5nOTgyOTRAb3V0bG9vay5jb20=