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

Ephraim Nissan

doi:10.3390/designs3030037

Abstract

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.

Highlights

Let it be clear that as far as I am aware, only some of the potentially relevant techniques from artificial intelligence have been applied to the in-core fuel management problem within nuclear engineering
We clarify right away that artificial intelligence as relevant for this article is of the kind that is closely associated with optimisation problems
Heuristic optimisation differs from traditional operations research in that what one looks for is good solutions, rather than the very best, if the search for the very best is forbiddingly costly or at any rate impractical

Summary

Aims

This article provides an overview of artificial intelligence applications to an economically important problem in nuclear engineering, this being in-core fuel management: how to design fuel reloads (refuellings) into the core of a nuclear reactor, after the fuel was partly depleted. I strive to be understood by, and to offer something interesting to, both nuclear engineers, and computer scientists, and possibly to other people involved in computer applications within engineering. A political concern has long been lest materials resulting from the pacific use of nuclear reactor plants be diverted for military purposes. Overall, this is the rationale behind attempts to promote so-called thorium reactors. Consider that the structure of this paper is as follows: Abstract

A pressurized reactor

Symmetry

Findings

A Survey of the Application of Other Techniques