A new approach to the use of genetic algorithms to solve the pressurized water reactor's fuel management optimization problem

A new approach to nuclear reactor design optimization using genetic algorithms and regression analysis

“…Previous work related to optimization, in problems related to nuclear engineering using GA includes, core design (Pereira and Lapa, 2003;Pereira et al, 1999;Haibach and Feltus, 1997), plant design (Cantoni et al, 2000), nuclear system availability and maintenance scheduling (Lapa et al, 2000;Marseguerra and Zio, 2000), fuel management (Chapota et al, 1999;Dechaine and Feltus, 1995), and spent fuel management (Omori et al, 1997). However, coupled neutronics-thermal hydraulics problems have not been explored and the effectiveness of GA in solving the coupled problems has not been evaluated.…”

Optimization of U–Th fuel in heavy water moderated thermal breeder reactors using multivariate regression analysis and genetic algorithms

“…However, in the current work, we are focusing on a fuel combination of natural uranium as the fuel and enriched thorium (ThO 2 þ UO 2 ) as the blanket. Previous work related to optimization, in problems related to nuclear engineering using GA includes, core design (Pereira and Lapa, 2003;do Nascimento Abreu Pereira et al, 1999;Haibach and Feltus, 1997), plant design (Cantoni et al, 2000;Kumar and Tsvetkov, 2015), nuclear system availability and maintenance scheduling (Lapa et al, 2000;Marseguerra and Zio, 2000), fuel management (Chapota et al, 1999;Dechaine and Feltus, 1995), and spent fuel management (Omori et al, 1997). However, coupled predictive analysis and optimization using GA in parametric analysis has not been extensively explored in reactor design problems.…”

A cross-entropy method applied to the In-core fuel management optimization of a Pressurized Water Reactor

“…Conversely, several metaheuristics or generic heuristic methods (Taillard et al, 2001) have been successfully investigated for application to the ICFMO such as Simulated Annealing (Kropaczek and Turinsky, 1991), Genetic Algorithms (GA; Poon and Parks, 1992;Chapot et al, 1999), Ant Colonies Optimization (ACO; Machado and Schirru, 2002), Population Based Incremental Learning (PBIL; Machado, 2005), Artificial Bee Colonies (ABC; de Oliveira and Schirru, 2011), and Particle Swarm Optimization (PSO; Meneses et al, 2009a).…”

“…RECNOD is a nodal code based on the works described by Langenbuch et al (1977), Liu et al (1985), and Montagnini et al (1994), applied to optimization surveys in several works (e.g. Chapot et al, 1999;Meneses et al, 2009aMeneses et al, , 2010.…”