E-ISSN: 2277-6184 | P-ISSN: 2321-6514 | Peer-Reviewed Journal (Refereed Journal) | Hybrid Open Access
Journal of Nuclear Engineering & Technology Journal of Nuclear Engineering & Technology [2277-6184(e)]Â is a peer-reviewed hybrid open access journal launched in 2011 and focused on the rapid publication of fundamental research papers on all areas of Nuclear Engineering & Nuclear …
jonet maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
Prof. Chechenin Nikolai Gavrilovich, The head of the Department
SKOBELTSYN INSTITUTE OF NUCLEAR PHYSICS LOMONOSOV MOSCOW STATE UNIVERSITY, Moscow, Russia,
Email :
Institutional Profile Link: http://www.sinp.msu.ru/en/users/341
Reliable fault detection is an essential requirement for safe and stable nuclear reactor operation because abnormal variations in temperature, pressure, coolant flow, neutron flux, and other process variables can develop rapidly during transient conditions.
Nuclear power systems contain safety-critical components whose degradation can affect availability, reliability, and maintenance planning. Conventional maintenance strategies are largely based on scheduled inspection, threshold monitoring, and condition-based assessment, which may not fully exploit the large volume of continuously generated plant data.
Nuclear reactor safety assessment requires accurate characterization of the coupled interaction between neutron population dynamics and thermal feedback during steady-state operation and transient conditions.
Thorium-232, a naturally abundant and fertile element, is increasingly regarded as a viable alternative to uranium-235 as the basis of future nuclear fuel cycles.
The management and characterization of radioactive waste represent a pivotal challenge for the global energy sector, requiring the convergence of advanced physics, material science, and computational intelligence.
Nuclear medicine, encompassing PET, SPECT, and targeted radionuclide therapy, generates high-dimensional, quantitative data uniquely suited for machine learning (ML) analysis.
