基于FEM/Kriging近似模型结合进化算法的表贴式高速永磁电机转子强度优化

Rotor Strength Optimization of Surface Mount High Speed Permanent Magnet Motor Based on FEM/Kriging Approximate Model and Evolutionary Algorithm

针对目前大功率高速永磁电机多采用的表贴式转子结构强度优化问题,以一台1.12 MW、18 000 r/min的表贴式高速永磁电机转子为优化对象,建立其结构参数化模型及应力场有限元仿真模型。将工况温度、护套厚度、永磁体厚度以及过盈量设置为优化参数,以永磁体和护套的法向及切向应力的最大值尽可能小为优化目标展开优化设计。对比分析两种技术路线:技术路线一采用进化算法(EA)调用有限元模型(FEM)进行优化设计;技术路线二对拉丁超立方法取得的样本空间进行拟合得到Kriging近似模型,基于近似模型结合EA算法进行优化设计。优化设计结果表明,技术路线一的优化结果更好;技术路线二更快速高效,大量样本点的集中分布情况可以反映优化参数与优化目标量间的关系。故实际工程优化问题应结合两种技术路线,初步寻优阶段采用技术路线二精确参数区间,进而采用技术路线一展开优化取得最优设计。

Due to the advantages of small size, high power density, high efficiency, can be directly connected with high-speed load, eliminating the traditional mechanical growth device, reduce system noise and other advantages, high-speed permanent magnet motor has been widely used in high-speed load and distributed power generation system, and has broad development prospects. Due to the high requirements on the rotor strength of high-speed permanent magnet motors, it is an urgent problem to find a method to efficiently optimize the rotor strength. However, for the finite element model(FEM) of the complex structure, it often takes dozens or even hundreds of iterative calculations to obtain the optimal solution, which greatly affects the development process of the motor.This paper took the 1.12 MW, 18 000 r/min surface-mounted high-speed permanent magnet motor rotor as the optimization object, and established its structural parameter model and stress field finite element simulation model. The operating temperature, sheath thickness, permanent magnet thickness and interference were set as the optimization parameters, and the optimal design is carried out with the maximum value of normal and tangential stress of permanent magnet and sheath as small as possible, so as to achieve the purpose of improving the rotor strength of high-speed permanent magnet motor. Combined with the currently commonly used optimization methods, two typical technical routes were specifically designed: the technical route 1 adopted the evolutionary algorithm(EA) combined with FEM to optimize and design. The maximum radial and tangential stresses of permanent magnet and sheath under different structural parameters and temperature parameters were taken as the optimization target quantity in the EA, and a various of parameter combinations were taken out and FEM was called for optimization iterative calculation. Technical route 2 selected the optimization parameters through the Latin hypercube sampling method, and used the FEM to find the corresponding the maximum value of the permanent magnet stress and the jacket stress was used as the output parameter to form the sample space. The sample space was fitted to obtain the Kriging approximation model that was used to replace the traditional FEM solver. Based on the approximate model, the subsequent optimization design process combined with the EA was carried out.Through the analysis and comparison of the two optimization methods, the following conclusions were drawn: technical route 1 can obtain the optimal solutions of sheath thickness, permanent magnet thickness,temperature and interference that reduce the maximum radial and tangential stress of the permanent magnet and the maximum radial stress of the sheath. It shows that the optimization method based on FEM combined with EA has a certain optimization effect, but there are also problems of few optimization attempts and long optimization time. Compared with technical route 1, technical route 2 is not limited by the number of samples, the calculation time is shorter, and the calculation efficiency is higher. When there are too many optimization parameters, there may be a fitting precision low lead to optimal design issue such as difference of accuracy, but based on the distribution of sample points in the sample space can intuitively reflect the optimization of the relationship between parameters and the optimization goal, applicable to the initial optimization phase. In addition, the optimal parameters of the calculation results of the optimal design and the optimal target quantities obtained by the two optimization methods are close, indicating that the EA can more accurately obtain the structural parameters that improve the strength of the rotor.The optimization problem in actual engineering should combine two optimization technical routes. In the preliminary optimization stage, the technical route 2 is used to accurately optimize the parameter interval, and then the technical route 1 is used to expand the optimization to obtain the optimal design.