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[en] A Pressurized Water Reactor core must be reloaded every time the fuel burnup reaches a level when it is not possible to sustain nominal power operation. The nuclear core fuel reload optimization consists in finding a burned-up and fresh-fuel-assembly loading pattern that maximizes the number of effective full power days, minimizing the relationship cost/benefit. This problem is NP-hard, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Besides that, the problem is non-linear and its search space is highly discontinual and multimodal. In this work a parallel computational system based on Ant Colony System (ACS) called Artificial-Ant-Colony Networks is used to solve the nuclear reactor core fuel reload optimization problem, with compatibles heuristics. ACS is a system based on artificial agents that uses the reinforcement learning technique and was originally developed to solve the Traveling Salesman Problem, which is conceptually similar to the nuclear fuel reload problem. (author)
Source
Associacao Brasileira de Energia Nuclear, Rio de Janeiro, RJ (Brazil); [vp.]; ISBN 978-85-99141-02-1; 
; 2007; [6 p.]; INAC 2007 International nuclear atlantic conference. Nuclear energy and energetic challenges for 21st. century. 15. Brazilian national meeting on reactor physics and thermal hydraulics; Santos (Brazil); 30 Sep - 5 Oct 2007; 8. Brazilian national meeting on nuclear applications; Santos (Brazil); 30 Sep - 5 Oct 2007; Available from the Library of the Brazilian Nuclear Energy Commission, Rio de Janeiro, in electronic form; 9 refs., 1 fig., 1 tab. Code: R171012.pdf
; 2007; [6 p.]; INAC 2007 International nuclear atlantic conference. Nuclear energy and energetic challenges for 21st. century. 15. Brazilian national meeting on reactor physics and thermal hydraulics; Santos (Brazil); 30 Sep - 5 Oct 2007; 8. Brazilian national meeting on nuclear applications; Santos (Brazil); 30 Sep - 5 Oct 2007; Available from the Library of the Brazilian Nuclear Energy Commission, Rio de Janeiro, in electronic form; 9 refs., 1 fig., 1 tab. Code: R171012.pdf