Advanced Technologies in Nuclear Engineering
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B4: Nuclear Energy".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 5154
Special Issue Editors
Interests: nuclear energy; nuclear technology; innovative nuclear fuel cycles; neutronics; CFD; advanced nuclear systems; energy scenarios; nuclear hydrogen production; HTR; LFR; GFR; ADS; SMR; nuclear space reactors
Special Issues, Collections and Topics in MDPI journals
Interests: materials processing technologies; metal forming; additive manufacturing; materials technology; data-driven decision methodologies; materials selection in manufacturing; equipment reliability; failure prognosis; nuclear power applications; renewable energy applications; oil & gas applications; aerospace applications;industrial heritage
Special Issues, Collections and Topics in MDPI journals
Interests: neutronics (for fission and fusion); diagnostics and fusion fission hybrid systems; Neutronics and nuclear inventory codes; nuclear measurements; nuclear transmutation and tritium breeding
Special Issue Information
Dear Colleagues,
Advanced Technologies in Nuclear Engineering have revolutionized the field of nuclear power generation and have greatly contributed to the advancements in energy production, safety, and waste management. Through continuous research and development, scientists and engineers have been able to harness the power of nuclear energy in a more efficient, sustainable, and secure manner.
Furthermore, advanced nuclear technologies have contributed to the optimization of nuclear fuel cycles. For example, the development of advanced fuel materials, such as mixed oxide (MOX) and metallic fuels, has improved fuel performance and increased the overall efficiency of nuclear reactors. Additionally, advancements in reprocessing techniques, such as pyroprocessing and advanced solvent extraction methods, have facilitated the recycling of spent nuclear fuel, reducing the volume of high-level waste and maximizing the utilization of valuable resources.
This Special Issue aims to address the role of nuclear energy in a future net-zero electricity market that may feature a high presence of renewables and other variable sources and inexpensive peaking capacity. It will cover nuclear power plant design and operation, as well as related technologies. It will provide a forum to discuss and present recent research results, technologies, and best practices on nuclear power plants and their most relevant equipment and components for both fission and fusion technologies, as well as consider their future developments. Papers can include small modular reactor designs, technologies, and operations. Research results on advanced and innovative nuclear fuel cycles will also be included. This Special Issue will also give particular attention to fusion–fission hybrid reactor technologies because these systems could represent an interesting synthesis between nuclear technologies.
Finally, this Special Issue will also bridge research with educational programs, as well as engineering practices, in all disciplines related to nuclear technology.
Prof. Dr. Guglielmo Lomonaco
Prof. Dr. Álvaro Rodríguez-Prieto
Dr. Fabio Panza
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- advanced nuclear systems (including space nuclear reactors)
- nuclear fuel cycles
- nuclear fusion and fission technologies (including fusion-fission hybrid reactors)
- nuclear power plants (including SMR) design and operation
- nuclear power plants economics
- nuclear power plants integration with the grid
- nuclear reactor engineering
- nuclear reactor physics
- nuclear safety and security
- radiation detection and protection systems
- sustainability of nuclear energy systems
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Development of AI-based image reconstruction model for Partial Defects Verification in Nuclear Fuel Assemblies
Authors: Jae Joon Ahn
Affiliation: Division of Data Science, Yonsei University, Wonju 26493, Republic of Korea
Abstract: The increase in nuclear power plants for carbon neutrality has emphasized the importance of managing high-radiation spent nuclear fuel (SNF). Traditional inspection methods, such as Gamma Emission Tomography (GET), have limitations in detecting partial defects within SNF assemblies. This study aims to enhance defect detection accuracy by optimizing AI-based image classification algorithms. Using emission tomography image data from 3x3 nuclear fuel assemblies, we compared the performance of neural network models (AlexNet, ResNet, SENet) and tree-based ensemble models (XGBoost, Random Forest, LightGBM). Our results show that neural network models, particularly ResNet and SENet, achieve superior classification accuracy with limited training data. SENet, in particular, demonstrates high performance with fewer samples, indicating its effectiveness in defect detection with minimal data. Tree-based models like XGBoost and LightGBM also exhibit high accuracy but are slightly lower than neural networks. In conclusion, AI-based classification systems, especially those utilizing advanced neural networks, can significantly improve the inspection and management of SNF, ensuring safety and compliance in nuclear energy operations. Future work should explore these methodologies on larger configurations beyond the 3x3 assemblies to further validate their effectiveness.