嵌入式超磁致伸缩构件多场耦合优化

建立了嵌入式超磁致伸缩GMM构件的机、电、磁、热多场优化模型,并采用多目标遗传算法实现了GMM构件的多场耦合模型优化。由GMM构件的一般设计准则和异形孔精密加工工艺要求,确定模型优化目标包括:合理的驱动刚度和较大的抗扭转刚度;驱动线圈效率系数大;空心线圈产生的高强度磁场;减少导磁回路磁阻,使GMM内部磁场强度高;强制水冷腔的散热效率高。优化变量包括:GMM的尺寸、导磁材料的磁导率以及磁回路、线圈、水冷腔体的结构。根据设计要求选取变量范围,采用非支配排序遗传算法(NSGA)在整个参数空间内搜索,得到了GMM构件主要结构参数,并通过试验和磁场仿真验证了结构设计方法的正确性。

Multi-field Coupling Model of Embedded Giant Magnetostrictive Components Optimization

The optimization design model of embedded giant magnetostrictive components (EGMC) was presented with multi-field property of machinery, electric, magnetic and thermal. And the multi-object genetic algorithm was applied to the optimization design of the EGMC. All optimal objects of the model were confirmed by the general design rules and the requirements of non-cylindrical holes in precision processing. The optimal goals of the model were as follows: a suitable flexural rigid of the EGMC, the maximum torsional rigid of the EGMC, the maximum coil efficiency, the maximum magnetic field density inside the coil, the maximum cooling efficiency of the water-cooling cavity, and the maximum magnetic field density in giant magnetostrictive material (GMM) by reducing the magnetic reluctances. The variables needs optimization included the size of GMM, the magnetic permeability of all magnetic materials, the structure of magnetic flux path, coil, and water-cooling cavity. All optimal parameter ranges were determined by application demands. The finest parameters of giant magnetostrictive components were obtained by non-dominated sorting genetic algorithm (NSGA) with space search method, which were proved by the experiments and magnetic field simulations.