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Generating optimal reloading patterns for CNPGS Unit-3 core using multi-objective elitist teaching-learning-based optimizer
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AI
DOI：10.1016/j.pnucene.2024.105507.png)
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摘要
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Pressurized Water Reactors (PWRs) management presents the core reloading pattern optimization as a significant problem that contributes to the improvement of reactor productivity and optimal fuel utilization with considering safety conditions. In present study, Multi-Objective Elitist Teaching-Learning-Based Optimization (MOETLBO) technique is suggested to cope up the multi-objective reloading optimization problem of the Chashma Nuclear Power Generating Station (CNPGS) unit-3 core. A multivariable objective function is designed to evaluate the quality of each loading pattern while maximizing critical boron concentration (CBC), minimizing the power peaking factor (PPF), and optimally enhancing the cycle length while ensuring adequate safety margins and design limits. It has been found that the equilibrium cycle can be extended to 16.07 extended full power days (EFPDs) while maintaining the PPF and CBC within design limits. To validate the effectiveness of TLBO, the optimized loading pattern of the equilibrium core was evaluated using the deterministic computer code DONJON5, for neutronic parameter analysis. The results show that the algorithm proposed in this study is a promising approach for reloading pattern optimization in CNPGS unit-3, offering potential improvements in reactor cycle length while ensuring safety and enhancing overall performance.
Keyword:
TLBO
CNPGS
Power peaking factor
DONJON
Cycle length extension
Critical boron concentration
期刊
IF:
3.2
论文数:
5.6K
被引数:
10.0K
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