Nuclear In-core Fuel Reload Design: The Trajectory of a Sequence of Projects

Nissan, Ephraim

doi:10.1007/978-3-642-45321-2_14

Cited by 2 publications

(1 citation statement)

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“…See Figure 5. Relevant publications on FUELCON include [145,163,[169][170][171][172][173][174][175][176]. In particular, [176], a 100-page paper, also covers FUELGEN.…”

Section: Expert Systemsmentioning

confidence: 99%

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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“…See Figure 5. Relevant publications on FUELCON include [145,163,[169][170][171][172][173][174][175][176]. In particular, [176], a 100-page paper, also covers FUELGEN.…”

Section: Expert Systemsmentioning

confidence: 99%