CN115630556A - A motor topology optimization method based on vertex method - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及电机技术领域,尤其涉及一种电机拓扑优化的设计方法。The invention relates to the technical field of motors, in particular to a design method for topology optimization of motors.
背景技术Background technique
随着现代工业的不断革新与快速发展,电机作为常用的机电能量转换设备遍布在国民经济的各个领域。随着对电机性能要求的不断提高,以前的拓扑结构难以满足要求。因此,急需在拓扑结构上的创新来满足对电机日益增加的性能需求。With the continuous innovation and rapid development of modern industry, motors, as commonly used electromechanical energy conversion equipment, are distributed in various fields of the national economy. With the continuous improvement of motor performance requirements, the previous topology is difficult to meet the requirements. Therefore, innovations in topology are urgently needed to meet the increasing performance requirements of motors.
目前,对电机拓扑的优化主要为对一些新的拓扑结构进行参数化,再使用智能算法对其进行优化。这样的方法存在三个主要问题:首先,想要研究出新型拓扑结构,这对研究人员的要求极高,并且会耗费大量时间进行实验仿真。其次,新的拓扑结构虽然能够满足电机对性能的要求,但其结构十分复杂,很难抽象出参数来表示其拓扑结构。最后,即使能够使用一些参数表示其形状,优化的结果也受限于初始结构,难以产生新的结构形状。这意味着在优化过程中难以从局部最优解跳出,进入全局最优解。At present, the optimization of motor topology is mainly to parameterize some new topological structures, and then use intelligent algorithms to optimize them. There are three main problems with such an approach: First, it is extremely demanding for researchers to study new topologies, and it takes a lot of time to perform experimental simulations. Secondly, although the new topology can meet the performance requirements of the motor, its structure is very complex, and it is difficult to abstract parameters to represent its topology. Finally, even if some parameters can be used to represent its shape, the optimized result is limited by the initial structure, and it is difficult to generate new structural shapes. This means that it is difficult to jump out of the local optimal solution and enter the global optimal solution during the optimization process.
综上,现有技术未能实现不依赖现有结构、设计出具有高性能电机的目标,本专利提出了一种使用顶点表示电机形状,通过智能算法优化顶点位置来实现拓扑结构的自由演变的方法。In summary, the existing technology fails to achieve the goal of designing a high-performance motor without relying on the existing structure. This patent proposes a method that uses vertices to represent the shape of the motor, and optimizes the position of the vertices through an intelligent algorithm to realize the free evolution of the topology. method.
发明内容Contents of the invention
技术问题:本发明的发明目的是针对上述背景技术的不足,提供一种基于顶点法的电机拓扑优化方法,通过改变每一个分区域中材料的顶点参数来改变材料的形状,从而提升电机性能,解决现有电机设计与优化过程中对从业人员要求高和依赖原有拓扑结构的问题。Technical problem: The purpose of the invention is to address the shortcomings of the above-mentioned background technology and provide a motor topology optimization method based on the vertex method, which changes the shape of the material by changing the vertex parameters of the material in each sub-region, thereby improving the performance of the motor. It solves the problems of high requirements for practitioners and dependence on the original topology in the process of existing motor design and optimization.
技术方案:本发明为实现上述发明目的采用一种基于顶点法的电机拓扑优化方法,具体如下:Technical solution: The present invention adopts a motor topology optimization method based on the vertex method in order to achieve the above-mentioned purpose of the invention, specifically as follows:
该方法把电机的设计区域看作是不同形状材料的组合,用材料各顶点坐标参数表示每种材料的形状,再通过优化方法优化各顶点坐标参数即顶点位置,从而优化电机的拓扑结构,达到优化电机性能的目标。This method regards the design area of the motor as a combination of different shapes of materials, uses the coordinate parameters of each vertex of the material to represent the shape of each material, and then optimizes the coordinate parameters of each vertex, that is, the position of the vertex, to optimize the topological structure of the motor and achieve The goal of optimizing motor performance.
所述电机拓扑优化方法具体包括如下步骤:The motor topology optimization method specifically includes the following steps:
S1、在二维坐标系中将电机的设计区域划分为m块分区域,其中第i块分区域的顶点数量设置为,,将每个顶点的横纵坐标作为参数,共有q个参数,;S1. In the two-dimensional coordinate system, the design area of the motor is divided into m block sub-areas, wherein the number of vertices of the i-th block sub-area is set to , , taking the horizontal and vertical coordinates of each vertex as parameters, there are q parameters in total, ;
S2、将同一分区域内的某一种材料边沿的各顶点连接成一个多边形,将该多边形作为该分区域内一种材料的形状,该分区域内剩下部分为另一种材料;根据各个分区域内材料的分布确定电机拓扑结构;S2. Connect the vertices on the edge of a certain material in the same sub-region to form a polygon, and use the polygon as the shape of a material in the sub-region, and the remaining part in the sub-region is another material; according to each The distribution of material within the subregion determines the motor topology;
S3、根据设计要求选择需要优化的目标,使用智能优化算法对顶点位置坐标参数进行优化,从而改变电机的拓扑结构,得到优化后新的顶点位置坐标;S3. Select the target to be optimized according to the design requirements, and use the intelligent optimization algorithm to optimize the vertex position coordinate parameters, thereby changing the topology of the motor and obtaining the optimized new vertex position coordinates;
S4、根据优化后新的顶点位置坐标得到同一分区域内的某一种材料形状,确定电机拓扑,使用有限元法计算该种拓扑的电机的性能,判断性能是否满足设计要求,若满足,则设计完成;否则返回步骤S1。S4. Obtain a certain material shape in the same sub-region according to the new optimized vertex position coordinates, determine the motor topology, use the finite element method to calculate the performance of the motor with this topology, and judge whether the performance meets the design requirements. If so, then The design is completed; otherwise, return to step S1.
所述在二维坐标系中将电机的设计区域划分为m块分区域,划分方式分为均匀划分和不均匀划分,若设计区域为定子齿部区域,采取均匀划分的方式;若设计区域为转子铁芯,采用不均匀划分的方式。In the two-dimensional coordinate system, the design area of the motor is divided into m block sub-areas, and the division method is divided into uniform division and uneven division. If the design area is the stator tooth area, the uniform division method is adopted; if the design area is The rotor core is divided unevenly.
所述电机的m块分区域内,第i块分区域用以个点为顶点的多边形区域来表示其材料部分,若该材料占满该分区域,则该分区域仅有一种材料。In the m sub-regions of the motor, the i-th sub-region is used for A polygonal area whose vertices are vertices is used to represent its material part. If the material occupies the sub-area, there is only one material in the sub-area.
所述的材料为硅钢片、永磁体或空气。Said material is silicon steel sheet, permanent magnet or air.
所述使用智能优化算法对顶点位置坐标参数进行优化具体是:初始生成多组随机的坐标参数x,x为q维向量,计算目标函数f(x),然后根据f(x)的值和具体使用的智能算法改变x的值,重新计算f(x)的值,进入下一轮迭代;只需要定义好x的取值范围和目标函数f(x),就能自动地找到f(x)的最小值对应的参数x。The optimization of the vertex position coordinate parameters using an intelligent optimization algorithm is specifically: initially generating multiple groups of random coordinate parameters x, where x is a q-dimensional vector, calculating the objective function f(x), and then according to the value of f(x) and the specific The intelligent algorithm used changes the value of x, recalculates the value of f(x), and enters the next iteration; only need to define the value range of x and the objective function f(x), and f(x) can be found automatically The minimum value of corresponds to the parameter x.
所述根据设计要求选择需要优化的目标,是根据具体的工程实际需求选择一个或多个优化目标,将拓扑优化问题建模成单目标或多目标优化问题,用以下数学表达式表示:The selection of the target to be optimized according to the design requirements is to select one or more optimization targets according to the actual needs of the project, and the topology optimization problem is modeled as a single-objective or multi-objective optimization problem, which is represented by the following mathematical expression:
其中x为q维向量,y为需要最小化的一组目标函数,为每一个目标函数,n为目标函数的个数,为限制参数x的不等式约束条件,d为不等式约束的个数,为限制参数x的等式约束条件,k为等式约束的个数。Where x is a q-dimensional vector, y is a set of objective functions to be minimized, For each objective function, n is the number of objective functions, is the inequality constraint condition that limits the parameter x, d is the number of inequality constraints, is the equality constraint condition that limits the parameter x, and k is the number of equality constraints.
所述需要优化的目标包括平均转矩、转矩波动、效率和成本;对于转矩波动、成本需要最小化的目标,为其本身;对于平均转矩、效率需要最大化的目标,为其倒数;对于转矩、效率这些和电机电磁性能相关的目标,使用有限元法计算电机的性能;对于成本只和电机的材料使用情况有关的目标,直接根据电机拓扑确定材料的使用情况,直接计算得到。The target that needs to be optimized includes average torque, torque ripple, efficiency and cost; For the target that torque ripple and cost need to be minimized, for itself; for the goal of maximizing average torque and efficiency, Its reciprocal; for the goals related to the electromagnetic performance of the motor, such as torque and efficiency, use the finite element method to calculate the performance of the motor; for the goals whose cost is only related to the material usage of the motor, directly determine the usage of materials according to the topology of the motor, directly calculated.
所述多个优化目标,根据坐标参数得到电机拓扑,计算目标函数,使用有限元法计算电机电磁性能,直接计算得到成本,使用智能优化算法对坐标参数进行优化,根据优化后的参数确定电机最终拓扑结构。The plurality of optimization objectives, obtain the motor topology according to the coordinate parameters, calculate the objective function, use the finite element method to calculate the electromagnetic performance of the motor, directly calculate the cost, and use the intelligent optimization algorithm to optimize the coordinate parameters Carry out optimization, and determine the final topology of the motor according to the optimized parameters.
所述智能算法使用模拟退火算法、遗传算法或粒子群算法。The intelligent algorithm uses simulated annealing algorithm, genetic algorithm or particle swarm algorithm.
有益效果:在使用顶点对每块分区域的材料形状建模后,设计区域的形状将会随着参数变化进行演化,在优化过程中,设计区域的形状不断地生成不同的新方案。因此,本发明相较于传统的电机设计方法,减少了设计者对于电机拓扑设计的干预,所以能够在实现减少人为干预、自动演变电机拓扑结构的同时,达到提高电机性能的目的。具有以下有益效果:Beneficial effect: After using the vertices to model the material shape of each sub-area, the shape of the design area will evolve with the change of parameters. During the optimization process, the shape of the design area will continuously generate different new schemes. Therefore, compared with the traditional motor design method, the present invention reduces the designer's intervention in the motor topology design, so it can achieve the purpose of improving the performance of the motor while reducing human intervention and automatically evolving the motor topology. Has the following beneficial effects:
(1)基于本方法设计出的电机可减少人为干预、不依赖现有的拓扑结构。(1) The motor designed based on this method can reduce human intervention and does not depend on the existing topology.
(2)方法简单,可行性强,适用于各类型电机。(2) The method is simple and feasible, and is applicable to various types of motors.
(3)根据不同的目标性能,具有多种选择,可根据需求选择合适拓扑。(3) According to different target performance, there are many choices, and the appropriate topology can be selected according to the needs.
附图说明Description of drawings
图1为本发明一种基于顶点法的电机拓扑优化的设计方法的流程图。FIG. 1 is a flowchart of a motor topology optimization design method based on the vertex method in the present invention.
图2(a)为设计区域为定子铁芯齿部,划分方式为均匀划分时的示意图,图2(b)为设计区域为电机转子部分,划分方式为不均匀划分的示意图。Figure 2(a) is a schematic diagram when the design area is the stator core teeth, and the division method is uniform division. Figure 2(b) is a schematic diagram when the design area is the motor rotor part, and the division mode is uneven division.
图3为选择转子作为设计区域,划分电机转子区域并且选择顶点个数的示意图。Fig. 3 is a schematic diagram of selecting the rotor as the design area, dividing the motor rotor area and selecting the number of vertices.
图4为优化开始、优化中和优化后的转子示意图。Fig. 4 is a schematic diagram of the rotor at the beginning of optimization, during optimization and after optimization.
图5(a)为整个遗传算法优化过程的平均转矩和波动随优化代数变化的示意图,图5(b)为所有优化结果的分布以及帕累托前沿。Figure 5(a) is a schematic diagram of the average torque and fluctuation of the entire genetic algorithm optimization process as the optimization algebra changes, and Figure 5(b) shows the distribution of all optimization results and the Pareto front.
图6(a)为图5(b)中A点对应的拓扑结构,图6(b)为图5(b)中B点对应的拓扑结构,图6(c)为图5(b)中C点对应的拓扑结构,图6(d)为图5(b)中D点对应的拓扑结构。Figure 6(a) is the topology corresponding to point A in Figure 5(b), Figure 6(b) is the topology corresponding to point B in Figure 5(b), and Figure 6(c) is the topology in Figure 5(b) The topology corresponding to point C, Figure 6(d) is the topology corresponding to point D in Figure 5(b).
具体实施方式Detailed ways
下面结合实例对发明的技术方案进行详细说明。可以理解的是,此处所描述的具体实施例仅用于解释本发明,而非对本发明的限定。另外还需要说明的是,为了便于描述,附图中仅出示了与本发明相关的部分而非全部。The technical scheme of the invention will be described in detail below in conjunction with examples. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings but not all of them.
如图1所示,示例性地展示了利用本发明公开的基于顶点法的电机拓扑优化方法的流程图。As shown in FIG. 1 , it exemplarily shows a flow chart of the motor topology optimization method based on the vertex method disclosed in the present invention.
S1、在二维坐标系中将电机的设计区域划分为m块分区域,其中第i块分区域的顶点数量设置为,,将每个顶点的横纵坐标作为参数,共有q个参数,;S1. In the two-dimensional coordinate system, the design area of the motor is divided into m block sub-areas, wherein the number of vertices of the i-th block sub-area is set to , , taking the horizontal and vertical coordinates of each vertex as parameters, there are q parameters in total, ;
S2、将同一分区域内的某一种材料边沿的各顶点连接成一个多边形,将该多边形作为该分区域内一种材料的形状,该分区域内剩下部分为另一种材料;根据各个分区域内材料的分布确定电机拓扑结构;S2. Connect the vertices on the edge of a certain material in the same sub-region to form a polygon, and use the polygon as the shape of a material in the sub-region, and the remaining part in the sub-region is another material; according to each The distribution of material within the subregion determines the motor topology;
S3、根据设计要求选择需要优化的目标,使用智能优化算法对顶点位置坐标参数进行优化,从而改变电机的拓扑结构,得到优化后新的顶点位置坐标;S3. Select the target to be optimized according to the design requirements, and use the intelligent optimization algorithm to optimize the vertex position coordinate parameters, thereby changing the topology of the motor and obtaining the optimized new vertex position coordinates;
S4、根据优化后新的顶点位置坐标得到同一分区域内的某一种材料形状,确定电机拓扑,使用有限元法计算该种拓扑的电机的性能,判断性能是否满足设计要求,若满足,则设计完成;否则返回步骤S1。S4. Obtain a certain material shape in the same sub-region according to the new optimized vertex position coordinates, determine the motor topology, use the finite element method to calculate the performance of the motor with this topology, and judge whether the performance meets the design requirements. If so, then The design is completed; otherwise, return to step S1.
有限元法是maxwell软件自带的,画出电机模型后就可以直接使用有限元法计算。The finite element method comes with the maxwell software. After drawing the motor model, you can directly use the finite element method to calculate.
优化同步磁阻电机的实例如下:An example of optimizing a synchronous reluctance motor is as follows:
1.同步磁阻电机转子仅由硅钢片和空气构成,本例中材料仅有硅钢片和空气。考虑到对称性,仅需考虑1/8模型,如图3所示,将电机的转子的设计区域划分为8个子区域,考虑到对称性,仅考虑左侧4部分。每部分的空气形状使用6个顶点来表示,每个顶点有2个参数,因此在本例中优化参数有48个。1. The synchronous reluctance motor rotor is only composed of silicon steel sheets and air. In this example, the materials are only silicon steel sheets and air. Considering the symmetry, only the 1/8 model needs to be considered. As shown in Figure 3, the design area of the rotor of the motor is divided into 8 sub-areas. Considering the symmetry, only the left 4 parts are considered. The air shape of each part is represented by 6 vertices, and each vertex has 2 parameters, so there are 48 optimized parameters in this example.
2.将同一分区域内的各顶点连接成一个多边形,将该多边形作为该分区域内空气的形状,该分区域内剩下部分硅钢片;根据各个分区域内材料的分布确定电机拓扑结构。2. Connect the vertices in the same sub-area to form a polygon, and use the polygon as the shape of the air in the sub-area, leaving some silicon steel sheets in the sub-area; determine the motor topology according to the distribution of materials in each sub-area.
3.本例中选择优化目标为平均转矩和转矩波动,使用遗传算法进行优化。在优化过程中,先由遗传算法生成新一代种群(一代种群即为多组顶点的坐标参数);然后在maxwell软件中画出对应拓扑的电机,并计算电机性能;根据性能保留优质种群,并对优质种群进行交叉(把种群中两个优质个体的部分顶点参数加以替换重组从而生成新个体参数)、变异操作(对群体中的个体串的某些坐标参数的值作变动),进入下一次迭代直到达到最大代数限制。3. In this example, the optimization target is selected as the average torque and torque fluctuation, and the genetic algorithm is used for optimization. In the optimization process, a new generation of population is first generated by genetic algorithm (the first generation of population is the coordinate parameters of multiple groups of vertices); then the corresponding topological motor is drawn in the maxwell software, and the performance of the motor is calculated; the high-quality population is retained according to the performance, and Perform crossover on high-quality populations (replace and recombine part of the vertex parameters of two high-quality individuals in the population to generate new individual parameters), mutation operations (change the values of some coordinate parameters of individual strings in the population), and enter the next Iterate until the maximum algebra limit is reached.
4.优化完成后根据优化后新的坐标参数确定电机拓扑。校验其它指标是否满足设计要求。若满足,则优化完成;否则,返回第一步。4. After the optimization is completed, the motor topology is determined according to the new optimized coordinate parameters. Verify that other indicators meet the design requirements. If satisfied, the optimization is completed; otherwise, return to the first step.
图4示例性地展示了优化过程中电机转子的演化过程。Fig. 4 exemplarily shows the evolution process of the motor rotor during the optimization process.
图5(a)示例性地展示了在基于顶点法的电机拓扑优化方法中使用遗传算法作为优化方法、目标函数选择为平均转矩和转矩波动时,每一次迭代过程中平均转矩和转矩波动的平均值随着迭代次数增加而变化的曲线图。图5(b)示例性地展示了在一次完整优化过程中,所有电机性能的分布和帕累托前沿。从图中可以看出,采用本发明可提升电机平均转矩,并且降低转矩波动。Figure 5(a) exemplarily shows that when the genetic algorithm is used as the optimization method in the motor topology optimization method based on the vertex method, and the objective function is selected as the average torque and torque ripple, the average torque and torque fluctuation in each iteration process A plot of the mean value of moment fluctuations as the number of iterations increases. Fig. 5(b) exemplarily shows the distribution and Pareto front of all motor performances in a complete optimization process. It can be seen from the figure that the average torque of the motor can be increased and the torque fluctuation can be reduced by adopting the present invention.
图6(a)、图6(b)、图6(c)、图6(d)示例性地展示了位于帕累托前沿上的点对应的电机转子拓扑。Figure 6(a), Figure 6(b), Figure 6(c), and Figure 6(d) exemplarily show the motor rotor topology corresponding to the points on the Pareto front.
本发明公开的基于顶点法的电机拓扑优化方法,根据目标函数的选择可得到的不同的结果,可根据电机的实际情况、其它性能要求与工艺技术进一步确定最终拓扑。The motor topology optimization method based on the vertex method disclosed in the present invention can obtain different results according to the selection of the objective function, and further determine the final topology according to the actual situation of the motor, other performance requirements and process technology.
以上实施方式只是对本专利的示例性说明,并不限定它的保护范围,本领域技术人员还可以对其局部进行改变,只要没有超出本专利的精神实质,都在本专利的保护范围内。The above embodiments are only exemplary descriptions of this patent, and do not limit its protection scope. Those skilled in the art can also make partial changes to it, as long as they do not exceed the spirit and essence of this patent, they are all within the protection scope of this patent.
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Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105082531A (en) * | 2014-05-24 | 2015-11-25 | 张亮 | Parallel three-dimensional forming method for multiple materials |
CN106650148A (en) * | 2016-12-30 | 2017-05-10 | 北京航空航天大学 | Method of continuum structure non-probabilistic reliability topological optimization under mixed constraints of displacements and stresses |
CN110110413A (en) * | 2019-04-26 | 2019-08-09 | 大连理工大学 | A kind of structural topological optimization method based on yard of material reduction series expansion |
CN112784489A (en) * | 2021-01-25 | 2021-05-11 | 北京航空航天大学 | Efficient dynamic robustness topology optimization method for continuum structure |
CN114741798A (en) * | 2022-03-10 | 2022-07-12 | 湖南大学 | A topology optimization method for motor rotor structure considering electromagnetic and mechanical properties |
CN115374546A (en) * | 2021-05-21 | 2022-11-22 | 达索系统公司 | CAD model parameterization |
-
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Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105082531A (en) * | 2014-05-24 | 2015-11-25 | 张亮 | Parallel three-dimensional forming method for multiple materials |
CN106650148A (en) * | 2016-12-30 | 2017-05-10 | 北京航空航天大学 | Method of continuum structure non-probabilistic reliability topological optimization under mixed constraints of displacements and stresses |
CN110110413A (en) * | 2019-04-26 | 2019-08-09 | 大连理工大学 | A kind of structural topological optimization method based on yard of material reduction series expansion |
CN112784489A (en) * | 2021-01-25 | 2021-05-11 | 北京航空航天大学 | Efficient dynamic robustness topology optimization method for continuum structure |
CN115374546A (en) * | 2021-05-21 | 2022-11-22 | 达索系统公司 | CAD model parameterization |
US20220382930A1 (en) * | 2021-05-21 | 2022-12-01 | Dassault Systemes | Parameterization of cad model |
CN114741798A (en) * | 2022-03-10 | 2022-07-12 | 湖南大学 | A topology optimization method for motor rotor structure considering electromagnetic and mechanical properties |
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