A novel neural/symbolic hybrid approach to heuristically optimized fuel allocation and automated revision of heuristics in nuclear engineering

FUELCON is an expert system for optimized refueling design in nuclear engineering. This task is crucial for keeping down operating costs at a plant without compromising safety. FUELCON proposes sets of alternative configurations of allocation of fuel assemblies that are each positioned in the planar grid of a horizontal section of a reactor core. Results are simulated, and an expert user can also use FUELCON to revise rulesets and improve on his or her heuristics. The successful completion of FUELCON led this research team into undertaking a panoply of sequel projects, of which we provide a meta-architectural comparative formal discussion. In this paper, we demonstrate a novel adaptive technique that learns the optimal allocation heuristic for the various cores. The algorithm is a hybrid of a fine-grained neural network and symbolic computation components. This hybrid architecture is sensitive enough to learn the particular characteristics of the ‘in-core fuel management problem’ at hand, and is powerful enough to use this information fully to automatically revise heuristics, thus improving upon those provided by a human expert.

Safety issues with the South African Pebble Bed Modular Reactor: when were the issues apparent? A briefing paper

This is a briefing report on when the safety issues identified in a July 2008 report by Jülich should have become apparent In July 2008, the German Jülich nuclear research centre published a report entitled ‘A safety re-evaluation of the AVR pebble bed reactor operation and its consequences for future HTR concepts.’ It concluded: ‘pebble bed HTRs require additional safety related R&D effort and updating of safety analyses before construction.’