The report presents the novel genetic algorithm (GA) developed to improve fuel cycle performance and in-core fuel management process. The primary purpose was to develop GA dedicated to solving core loading pattern (LP) problem with predefined core operation constraints. Particular focus was put on maximizing the length of the fuel cycle, but presented solutions allow to limit or control the magnitude of excess reactivity, population and type of fuel assemblies, fissile material mass and inventory of burnable absorbers. GA was implemented in a new computational framework coupled with PARCS3.2 core simulator. The Pressurized Water Reactor (PWR) based on the MIT BEAVRS Benchmark was used as the demonstration case, and developed tools were applied to improve the first fuel cycle. Several test simulations were performed, including population size, a number of generations, mutation level and other aspects of GA. New variance control method, was proposed and tested. It was compared with the standard roulette and rank method, showing improved convergence. Four test cases were studied with different constraints put on the fuel assemblies population, mass of fissile materials, burnable absorbers and limiting excess reactivity. The obtained results show that the new algorithm works properly and can allow designing efficient fuel loading pattern with increased fuel cycle length.

该报告介绍了开发用于改善燃料循环性能和堆芯燃料管理过程的新型遗传算法（GA）。主要目的是开发专用于解决具有预定义的核心操作约束的核心加载模式（LP）问题的GA。特别关注的重点是最大程度地延长燃料循环的时间，但是提出的解决方案可以限制或控制过量反应的程度，燃料组件的数量和类型，易裂变材料的质量以及可燃吸收剂的库存。GA在与PARCS3.2核心模拟器结合的新计算框架中实现。以MIT BEAVRS基准为基础的压水堆（PWR）被用作演示案例，并使用了开发的工具来改善第一个燃料循环。进行了一些测试模拟，包括人口规模，GA的许多世代，突变水平和其他方面。提出并测试了一种新的方差控制方法。它与标准轮盘赌和排名方法进行了比较，显示出更好的收敛性。研究了四个测试案例，这些案例对燃料组件的数量，易裂变材料的质量，可燃吸收剂和限制过度反应性施加了不同的限制。所得结果表明，该新算法工作正常，可以设计出具有更长燃料循环长度的高效燃料装载模式。可燃吸收剂并限制了过度的反应性。所得结果表明，该新算法工作正常，可以设计出具有更长燃料循环长度的高效燃料装载模式。可燃吸收剂并限制了过度的反应性。所得结果表明，该新算法工作正常，可以设计出具有更长燃料循环长度的高效燃料装载模式。