This paper presents a case study of applying Genetic Algorithm (GA) coupled with Monte Carlo N-Particle Transport (MCNP) and PARET codes for a thermal-hydraulic and safety analysis to optimize the fuel reload for the TRIGA Mark II Moroccan research reactor. Based on the radial distribution of the ²³⁸U burnup ratio inside the reactor core, the five most burned fuel elements were replaced by others fresh fuel elements (12 % wt of uranium) using the Multi-Objective Genetic Algorithms (MOGA) method. Three aspects for the fuel reload optimization were considered in this study including 1) maximization of the effective multiplication factor (K_eff), 2) minimization of maximum Centre Fuel Temperature (CFT) and 3) maximization of the Departure from Nuclear Boiling Ratio (DNBR).

The GA programming process developed in this work was adapted to handle the constraints concerning the safety limits for the successive core configurations (CCs) automatically generated by the code. MOGA method works with an elitist selection based on the Binary Tournament Selection (BTS) method, a modified two-point crossover and a simple mutation operator. The results obtained indicate that the MOGA can successfully find an optimal CC with a K_eff of 1.03498, a maximum CFT of 554 °C and a DNBR of 2.94 when five fresh fuel elements are inserted. The variation of neutron fluxes with respect to radial distance for the best CC and the fresh core was illustrated.

本文介绍了将遗传算法（GA）结合蒙特卡罗N粒子传输（MCNP）和PARET代码进行热工液压和安全性分析以优化TRIGA Mark II摩洛哥研究堆燃料重载的案例研究。基于反应堆堆芯内部²³⁸ U燃耗比的径向分布，使用多目标遗传算法（MOGA）方法将燃烧最严重的五个燃料元件替换为其他新鲜燃料元件（铀的12 wt％）。在这项研究中，考虑了三个方面的燃料重载优化，包括：1）有效乘数因子（K _eff）的最大化，2）最高中心燃料温度（CFT）的最小化和3）核沸腾比（DNBR）的最大化）。

在这项工作中开发的GA编程流程适用于处理有关由代码自动生成的连续核心配置（CC）的安全限制的约束。MOGA方法与基于二进制锦标赛选择（BTS）方法的精英选择，改进的两点交叉法和简单的变异算子一起工作。获得的结果表明，当插入五个新鲜燃料元件时，MOGA可以成功找到最佳CC，K _eff为1.03498，最大CFT为554°C，DNBR为2.94。说明了最佳CC和新鲜堆芯中子通量相对于径向距离的变化。