Minimizing Power Peaking Factor of BEAVRS-based Reactor Using Polar Bear Optimization Algorithms

Amatullah, Amila and Agung, Alexander and Arif, Agus (2021) Minimizing Power Peaking Factor of BEAVRS-based Reactor Using Polar Bear Optimization Algorithms. In: International Energy Conference, ASTECHNOVA 2021.

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Abstract

Fuel loading pattern optimization is a complex problem because there are so many possibilities for combinatorial solutions, and it will take time to try it one by one. Therefore, the Polar Bear Optimization Algorithm was applied to find an optimum PWR loading pattern based on BEAVRS. The desired new fuel loading pattern is the one that has the minimum Power Peaking Factor (PPF) value without compromising the operating time. Operating time is proportional to the multiplication factor (k eff ). These parameters are usually contradictive with each other and will make it hard to find the optimum solution. The reactor was modelled with the Standard Reactor Analysis Code (SRAC) 2006. Fuel pins and fuel assemblies are modelled with the PIJ module for cell calculations. One-fourth symmetry was used with the CITATION X-Y module for core calculations. The optimization was done with 200 populations and 50 iterations. The PPF value for the selected solution should never exceed 2.0 in every burn-up step. Out of 28 solutions, the best optimal fuel loading pattern had a maximum value PPF of 1.458 and a k eff of 0.916 at day 760 of calculated time (corresponding to a cycle length of 479 days). Therefore, the maximum PPF value was 27.1 lower than the safety factor, and the same operating time as the standard loading pattern has been achieved. © Published under licence by IOP Publishing Ltd.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Cited by: 1; All Open Access, Gold Open Access
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering > Nuclear and Physics Engineering Department
Depositing User: Sri JUNANDI
Date Deposited: 24 Oct 2024 00:49
Last Modified: 24 Oct 2024 00:49
URI: https://ir.lib.ugm.ac.id/id/eprint/3741

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