从堆芯燃料管理装载模式 (L P)优化中的非确定多项式 (NP)特征和全局寻优要求 ,探讨了遗传算法在 L P优化中的应用。结合 L P优化和遗传算法特点 ,建立了 L P优化的数学模型的矩阵表示。为了解决组件矩阵的有效性问题 ,对组件矩阵进行...从堆芯燃料管理装载模式 (L P)优化中的非确定多项式 (NP)特征和全局寻优要求 ,探讨了遗传算法在 L P优化中的应用。结合 L P优化和遗传算法特点 ,建立了 L P优化的数学模型的矩阵表示。为了解决组件矩阵的有效性问题 ,对组件矩阵进行基于位置的遗传操作 ;考虑到可燃毒物数目的独立性 ,对可燃毒物矩阵进行随机遗传操作 ;对旋向矩阵进行经典遗传操作。类比旅行商问题 ,完成了 L P优化遗传算法编码和解码。并可与相应的堆物理计算程序构成一个完整的堆芯燃料管理程序。展开更多
Fuel reload pattern optimization is essential for attaining maximum fuel burnup for minimization of generation cost while minimizing power peaking factor(PPF).The aim of this work is to carry out detailed assessment o...Fuel reload pattern optimization is essential for attaining maximum fuel burnup for minimization of generation cost while minimizing power peaking factor(PPF).The aim of this work is to carry out detailed assessment of particle swarm optimization(PSO) in the context of fuel reload pattern search. With astronomically large number of possible loading patterns, the main constraints are limiting local power peaking factor, fixed number of assemblies,fixed fuel enrichment, and burnable poison rods. In this work, initial loading pattern of fixed batches of fuel assemblies is optimized by using particle swarm optimization technique employing novel feature of varying inertial weights with the objective function to obtain both flat power profile and cycle k_(eff)>1. For neutronics calculation, PSU-LEOPARD-generated assembly depletiondependent group-constant-based ADD files are used. The assembly data description file generated by PSU-LEOPARD is used as input cross-section library to MCRAC code, which computes normalized power profile of all fuel assemblies of PWR nuclear reactor core. The standard PSO with varying inertial weights is then employed to avoid trapping in local minima. A series of experiments havebeen conducted to obtain near-optimal converged fuelloading pattern of 300 MWe PWR Chashma reactor. The optimized loading pattern is found in good agreement with results found in literature. Hybrid scheme of PSO with simulated annealing has also been implemented and resulted in faster convergence.展开更多
文摘从堆芯燃料管理装载模式 (L P)优化中的非确定多项式 (NP)特征和全局寻优要求 ,探讨了遗传算法在 L P优化中的应用。结合 L P优化和遗传算法特点 ,建立了 L P优化的数学模型的矩阵表示。为了解决组件矩阵的有效性问题 ,对组件矩阵进行基于位置的遗传操作 ;考虑到可燃毒物数目的独立性 ,对可燃毒物矩阵进行随机遗传操作 ;对旋向矩阵进行经典遗传操作。类比旅行商问题 ,完成了 L P优化遗传算法编码和解码。并可与相应的堆物理计算程序构成一个完整的堆芯燃料管理程序。
文摘Fuel reload pattern optimization is essential for attaining maximum fuel burnup for minimization of generation cost while minimizing power peaking factor(PPF).The aim of this work is to carry out detailed assessment of particle swarm optimization(PSO) in the context of fuel reload pattern search. With astronomically large number of possible loading patterns, the main constraints are limiting local power peaking factor, fixed number of assemblies,fixed fuel enrichment, and burnable poison rods. In this work, initial loading pattern of fixed batches of fuel assemblies is optimized by using particle swarm optimization technique employing novel feature of varying inertial weights with the objective function to obtain both flat power profile and cycle k_(eff)>1. For neutronics calculation, PSU-LEOPARD-generated assembly depletiondependent group-constant-based ADD files are used. The assembly data description file generated by PSU-LEOPARD is used as input cross-section library to MCRAC code, which computes normalized power profile of all fuel assemblies of PWR nuclear reactor core. The standard PSO with varying inertial weights is then employed to avoid trapping in local minima. A series of experiments havebeen conducted to obtain near-optimal converged fuelloading pattern of 300 MWe PWR Chashma reactor. The optimized loading pattern is found in good agreement with results found in literature. Hybrid scheme of PSO with simulated annealing has also been implemented and resulted in faster convergence.
