Novas Abordagens sobre o Problema de Otimização do Projeto do Núcleo do Reator
DOI:
https://doi.org/10.14295/vetor.v31i1.13498Palavras-chave:
Projeto do núcleo do reator, Otimização, Evolução Diferencial, Invasive Weed Optimization, Many-Objective Evolutionary AlgorithmResumo
O projeto do núcleo de um reator nuclear é um problema que consiste na escolha pertinente de uma série de parâmetros que devem obedecer a algumas restrições técnicas e físicas. Diversos métodos têm sido aplicados na literatura especializada de modo a obter-se soluções ótimas para este problema. O presente trabalho tem como objetivo apresentar um análise comparativa de duas metodologias de otimização, quais sejam: Invasive Weed Optimization e Many-Objective Evolutionary Algorithm.
Downloads
Referências
T. O. M. Muniz and F. B. S. Oliveira, “Modelagem computacional de uma blindagem de nêutrons utilizando métodos determinísticos,” VETOR - Revista de Ciências Exatas e Engenharias, vol. 30, no. 1, p. 15–27, 2020. Available at: https://doi.org/10.14295/vetor.v30i1.12877
D. Rozon and M. Beauet, “Canada deuterium uranium reactor design optimization using three-dimensional generalized perturbation theory,” Nuclear Science and Engineering, vol. 111, pp. 1–12, 1992. Available at: https://doi.org/10.13182/NSE92-A23919
D. J. Kropackzek and P. J. Turinsky, “In-core nuclear fuel management optimization for pressurized water reactors utilizing simulated annealing,” Nuclear Technology, vol. 95, p. 9, 1991. Available at: https://doi.org/10.13182/NT95-1-9
J. Chapot, F. C. Silva, and R. Schirru, “A new approach to the use of genetic algorithms to solve the pressurized water reactor’s fuel management optimization problem,” Annals of Nuclear Energy, vol. 26, no. 7, p. 641, 1999. Available at: https://doi.org/10.1016/S0306-4549(98)00078-4
W. Sacco, N. Henderson, A. Rios-Coelho, M. Ali, and C. Pereira, “Differential evolution algorithms applied to nuclear reactor core design,” Annals of Nuclear Energy, vol. 36, no. 8, pp. 1093–1099, 2009. Available at: https://doi.org/10.1016/j.anucene.2009.05.007
J. Suich and H. Honec, “The hammer system heterogeneous analysis by multigroup methods of exponentials and reactors,” Savannah River Laboratory, 1967. Available at: https://doi.org/10.2172/4440604
R. Storn and K. Price, “Differential evolution – a simple and e cient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. Available at: https://doi.org/10.1023/A:1008202821328
A. R. Mehrabian and C. Lucas, “A novel numerical optimization algorithm inspired from weed colonization,” Ecological informatics, vol. 1, no. 4, pp. 355–366, 2006. Available at: https://doi.org/10.1016/j.ecoinf.2006.07.003
Z. He and G. Yen, “Many-objective evolutionary algorithm: Objective space reduction + diversity improvement,” IEEE Transactions on Evolutionary Computation, vol. 20, no. 1, p. 145-160, 2016. Available at: https://doi.org/10.1109/TEVC.2015.2433266
K. Deb and R. B. Agrawal, “Simulated binary crossover for continuous search space,” Complex Systems, vol. 9, no. 2, pp. 115–148, 1995. Available at: https://www.complex-systems.com/abstracts/v09_i02_a02/
M. M. Raghuwanshi and O. Kakde, “Survey on multiobjective evolutionary and real coded genetic algorithms,” Proceedings of the 8th Asia Paci c symposium on intelligent and evolutionary systems, pp. 150–161, 2004. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.2396&rep=rep1&type=pdf
E. Beale, “Confidence-regions in non-linear estimation,” Journal of the Royal Statistical Society B-Statistical Methodology, vol. 22, no. 1, pp. 41–88, 1960. Available at: http://www.jstor.org/stable/2983877
Downloads
Publicado
Versões
- 2021-11-23 (2)
- 2021-11-18 (1)