Advanced Examinations, Methods, and Tools for the Performance Analysis of Nuclear Fuel Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B4: Nuclear Energy".
Deadline for manuscript submissions: 14 August 2024 | Viewed by 1014
Special Issue Editors
Interests: nuclear fuel; nuclear energy; nuclear engineering; post-irradiation examination; nuclear fuel performance
Interests: nuclear fuel; cladding and structure materials; waste form; TEM; fuel performance; microstructure characterization
Special Issue Information
Dear Colleagues,
Nuclear energy is the backbone of low-carbon electricity generation. Nuclear fuel provides a source of energy via fission reactions, which split uranium or plutonium fissile atoms to produce energy. The sequent energy transfer from nuclear energy to thermal energy creates thermal–mechanical effects, as well as radiation damages to nuclear fuel systems (here intended as nuclear fuel and cladding materials). A fundamental understanding and quantification of the abovementioned effects is at the heart of fuel performance analysis, particularly with regard to studying new nuclear fuel systems and qualifying them, or expanding the operating conditions of commercially employed products. Synergistically and complementary efforts from advanced examinations, methods and tools can fundamentally change our approach to understanding the underlying mechanisms of nuclear fuel performance and accelerating the discovery of new viable nuclear fuel systems. Other emerging techniques, such as domain knowledge-informed and scientific-data-driven artificial intelligence and machine learning, are becoming also essential.
This Special Issue welcomes contributions that attend to topics including, but not limited to, the following:
- Innovative approaches to experimental examinations applied to nuclear fuel system performance;
- Multiscale experimental examinations applied to nuclear fuel systems (irradiated and as fabricated);
- Multiscale modelling and advanced methods;
- Experimental and modeling verification and validation for nuclear fuel systems;
- Domain-knowledge-informed and scientific-data-driven artificial intelligence, machine learning, and deep learning applied to nuclear fuel system performance analysis.
Dr. Luca Capriotti
Dr. Tiankai Yao
Dr. Pavel Medvedev
Guest Editors
Manuscript Submission Information
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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
- nuclear fuel
- fuel performance
- fuel performance analysis
- advanced methods
