Keywords: accident classification, core design, nuclear fuel reload, particle swarm optimisation, PSO, surveillance tests planning, nuclear engineering, nuclear power, nuclear energy, Brazil, optimal design, nuclear accidents
Particle swarm optimisation applied to nuclear engineering problems
Evolutionary computation (EC) techniques, and more specifically genetic algorithms (GA) and their variations, have been efficiently applied to many complex problems found in the nuclear engineering field. Such methods have been shown to be robust and efficient, but highly time consuming. Other population-based methods have been proposed as alternatives to these traditional EC techniques. The Particle Swarm Optimisation (PSO) technique has been shown to be faster and many times more efficient than GA. Motivated by that, investigations concerning applications of PSO to nuclear engineering problems have started in the Brazilian Nuclear Engineering Institute (IEN/CNEN) and Federal University of Rio de Janeiro (UFRJ). This paper describes applications of PSO to four classical nuclear engineering problems: (i) nuclear fuel reload, (ii) core design optimisation, (iii) surveillance tests planning and (iv) accident classification. Computational experiments demonstrate that PSO can be efficiently applied to the problems studied. Moreover, the results described are comparable with, or even better than, some good results (obtained by GA) found in the literature.