Recycling-Independent Core Design for the ENHS Fuel Self-Sustaining Reactor

Monti, L.; Lee, Ki-Bog; Fratoni, Massimiliano; Sumini, Marco; Greenspan, E.

doi:10.13182/nse162-01

Cited by 5 publications

(2 citation statements)

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“…diagnostic imaging procedures each year, and this demand is expected to grow. Although there are numerous ways of producing this isotope, the most common is from fission product 99 Mo, which is produced in all nuclear reactors fueled with 235 U (an isotope of uranium) as a fission fragment with a yield of around 6.1%. Molybdenum-99 has a half-life of just over 2.5 days, and it will decay to 99m Tc 87% of the time".…”

Section: Safety Materials and Options For Nonproliferation Aimsmentioning

confidence: 99%

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An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

Nissan

2019

Designs

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An important aspect of managing a nuclear reactor is how to design refuellings, and from the 1980s to the present different artificial intelligence (AI) techniques have been applied to this problem. A section of the reactor core resembles a symmetrical grid; long fuel assemblies are inserted there, some of them new, some of them partly spent. Rods of “burnable poisons” dangle above, ready to be inserted into the core, in order to stop the reactor. Traditionally, manual design was made by shuffling positions in the grid heuristically, but AI enabled to automatically generate families of candidate configurations, under safety constraints, as well as in order to optimize combustion, with longer cycles of operation between shutdown periods, thus delaying the end-of-cycle point (except in France, where shutdown is on an annual basis, and Canada, where individual fuel assemblies are replaced, with no need for shutdown for rearranging the entire batch). Rule-based expert systems, the first being FUELCON,1 were succeeded by projects combining neural and rule-based processing (a symbolic-to-neural compilation of rules we did not implement), and later on, genetic algorithms in FUELGEN.2 In the literature, one also comes across the application of fuzzy techniques, tabu search, cellular automata and simulated annealing, as well as particle swarms. Safety regulations require simulating the results using a parameter prediction tool; this is done using either nodal algorithms, or neural processing.

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Section: Safety Materials and Options For Nonproliferation Aimsmentioning

confidence: 99%

“…Monti et al[99] discussed the design of a core-the Encapsulated Nuclear Heat Source (ENHS) core-in a "fuel self-sustaining reactor". The reference ENHS core considered is fuelled with transuranium from spent fuel that had been used in a light water reactor (LWR) and was then cooled for 10 years, with better results if the cooling period was of nearly 32 years.…”

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confidence: 99%