designs Review 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 1 London, UK; ephraim.nissan@hotmail.co.uk 2 Formerly of the Department of Computing, Goldsmiths College of the University of London, New Cross, London SE14 6NW, UK Received: 14 February 2019; Accepted: 23 May 2019; Published: 15 July 2019   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, 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. 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. Keywords: AI applications to nuclear engineering; in-core fuel management problem; AI tools for designing refuellings 1. Introduction 1.1. 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 also to other people involved in computer applications within engineering. These aims strongly shape the presentation. In particular, Sections 1.3 and 2 of this paper are intended to especially appeal to such readers who are not familiar with nuclear engineering. In Section 1, such concepts are introduced, which will be important for understanding Section 3, “Computer Tools for Designing Fuel Reload Conﬁgurations”, which itself surveys how various techniques historically associated with artiﬁcial intelligence (AI) Designs 2019, 3, 37; doi:10.3390/designs3030037 www.mdpi.com/journal/designs