OPTIMIZATION OF FUEL ASSEMBLY CONFIGURATIONS IN VVER-1000 REACTORS CONSIDERING FUEL ROD CLADDING DAMAGE

Abstract

Relevance of the study is due to the growing need to improve the approaches to configuring fuel assembly layouts in VVER-1000 reactors under increased operational loads, with an emphasis on fuel efficiency and the mitigation of fuel rod cladding damage risks.The aim of the article is to propose a discrete model and conceptual optimization method for rearranging fuel assemblies in the VVER-1000 reactor core, taking into account several critical parameters simultaneously – such as thermal load distribution, neutron flux uniformity, mechanical stress on cladding, and predicted fuel burnup.Research methods are based on a logic-analytical approach that includes a review of relevant scientific literature, synthesis of core performance criteria, and the development of a structural framework for a future discrete model using heuristic modeling principles. The study outlines the necessary input parameters and constraints, and presents a conceptual structure for implementing an optimization method– potentially using evolutionary algorithms such as genetic approaches– without focusing on direct algorithmic realization. Research results allow for the justification of a multicriteria approach to optimizing core configurations, addressing key interdependent indicators related to reactor safety, cladding durability, and energy generation uniformity. The main components of the model are identified, offering a foundation for further formal development and testing.Scientific novelty lies in the formulation of a discrete optimization model that integrates variable operational conditions, evolving fuel characteristics, and conflicting performance objectives into a unified decision-making framework for core design.Conclusions confirm the feasibility of developing a multicriteria model for optimizing fuel assembly arrangements, enabling reductions in localized overheating, improved burnup distribution, and enhanced cladding performance over the fuel cycle.Prospects for further research include mathematical formalization of the proposed model, creation of an algorithmic prototype using genetic methods, and validation of solutions using real-world fuel cycle data.