Abstract
The two main goals in core fuel loading pattern design optimization are maximizing the core effective multiplication factor (Keff) in order to extract the maximum energy, and keeping the local power peaking factor (Pq) lower than a predetermined value to maintain fuel integrity. In this research, a new strategy based on Particle Swarm Optimization (PSO) algorithm has been developed to optimize the fuel core loading pattern in a typical VVER. The PSO algorithm presents a simple social model by inspiration from bird collective behavior in finding food. A modified version of PSO algorithm for discrete variables has been developed and implemented successfully for the multi-objective optimization of fuel loading pattern design with constraints of keeping Pq lower than a predetermined value and maximizing Keff. This strategy has been accomplished using WIMSD and CITATION calculation codes. Simulation results show that this algorithm can help in the acquisition of a new pattern without contravention of the constraints.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
