摘要
板弹簧是核电厂反应堆燃料组件的重要部件,其性能好坏直接关系燃料组件的服役安全性。本文考虑板弹簧材料弹塑性本构关系和大变形等复杂非线性因素,利用ANSYS软件对板弹簧进行了多尺寸耦合约束下的参数化建模,实现了模型几何、六面体网格和接触对的自动建立,并以此分析了板弹簧关键参数对板弹簧特性的影响。基于MATLAB并行计算库,构建了基于粒子群智能优化算法的板弹簧特性多参数优化平台,以板弹簧设计刚度曲线和最小塑性变形为目标,对板弹簧板厚、变截面位置和圆弧过渡区形式进行了优化。结果表明,基于粒子群的板弹簧多参数智能优化算法,可以显著提升板弹簧的设计效率;在给定板弹簧设计目标曲线和板弹簧参数范围内,该算法可以在较少的迭代次数内获得满足设计目标的结构参数,对核反应堆板弹簧工程设计具有较好的指导意义。
The leaf spring is an important part of a nuclear fuel assembly.Its performance is directly related to the service safety of fuel assemblies.Considering the complex non-linear factors of leaf spring materials,such as the elastic-plastic constitutive relation and large deformation,the authors,using the ANSYS code,performs the parametric modeling of leaf spring under the constraint of multi-scale coupling.The model geometry,hexahedral mesh and contact pairs of the leaf spring are established automatically.On this basis,the authors study and analyze the influence of key parameters on the characteristics of leaf springs.Depending on the MATLAB parallel computing library,the authors build a multi-parameter optimization platform for leaf spring characteristics based on intelligent particle swarm optimization(IPSO)algorithm.Then,the authors use this platform to optimize the leaf thickness,variable cross-section position and arc-shaped transition zone form over the design stiffness curve and minimum plastic deformation of the leaf spring.As demonstrated by the results,the intelligent multi-parameter optimization algorithm based on particle swarm can help improve the leaf spring design efficiency greatly;and within the given leaf spring design target curve and the leaf spring parameters,this algorithm can provide structural parameters satisfying the design target via a less times of iteration,which provide a good guidance on the nuclear reactor leaf spring engineering.
作者
何大明
李垣明
蒲曾坪
吴兴文
李伟
He Daming;Li Yuanming;Pu Zengping;Wu Xingwen;Li Wei(Science and Technology on Reactor System Design Technology Laboratoory,Nuclear Power Institute of China,Chengdu,610213,China;School of Mechanical Engineering,Southwest Jiaotong University,Chengdu,610031,China;State Key Laboratory of Traction Power,Southwest Jiaotong University,Chengdu,610031,China)
出处
《核动力工程》
EI
CAS
CSCD
北大核心
2021年第5期261-265,共5页
Nuclear Power Engineering