Fuel loading pattern optimization of ALLEGRO fast reactor using the tabu search metaheuristic technique

Abstract

In this work an improved tabu search (TS) optimization technique is used to find the optimal fuel loading pattern in the experimental fast reactor ALLEGRO. The penalty function method is used to solve this multi-objective combinatorial optimization problem. The objective function and constrains are computed via an interface between the developed optimizer and the ERANOS 2.3 deterministic code. To reduce the running time, the operating cycle simulation is carried out using the diffusion approximation and a 7-group neutron energy structure. The proposed objective function maximizes the k-eff value at the end of cycle, satisfying the maximum power factor, the excess hot reactivity, and the linear heat generation rate constraints. A novel procedure developed in ERANOS to perform fuel assembly swaps for neighbor search is presented. The algorithm performed as expected throughout the iterative process and, unlike other metaheuristics based on local search, did not get stuck in local optima, which is a well-known advantage of tabu search technique.