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Study on machine learning for generation of finite element models of electrical machines

用于生成电机有限元模型的机器学习研究

基本信息

批准号：
15560233
负责人：
IGARASHI Hajime
金额：
$ 2.37万
依托单位：
Hokkaido University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15560233/
关键词：
Mesh Generation Finite Element Method Genetic Algorithm Machine Learning 電磁界解析機械学習 CAE

项目摘要

In this research, we have developed a new method to realize automatic generation of finite element meshes for electric machines. In this method, an initial course mesh is prepared, and element properties P are computed where P is a linear combination of element features such as magnitude of magnetic induction at the element, distance from the nearest corner of magnetic material, distance from the nearest current source and so on. The element with largest P is subdivided into two finer elements. And this process is repeated until the number of elements equals a prescribed number. The error in the finite element analysis on the obtained mesh is then computed. This error strongly depends on the mesh quality, which is dependent on the weights in the linear combination of the element features. We optimize the weights using the genetic algorithm. When the weights are optimized, this method can be applied for other similar finite element models of electrical machines.It is shown that the resu … More ltant finite element mesh using the above method, called the simple method, often have flat elements which are inadequate for finite element analysis. To resolve this difficulty, we introduce mesh control techniques so that the number of subdivision of element edges is determined from their length and a criterion to choose elements to be subdivided. The finite element meshes obtained using this method, called the mesh control method, are shown to be better than those obtained using the simple method.Although the mesh quality is improved by the mesh control method, it becomes worse when the initial course mesh includes flat elements. It is difficult to overcome this problem as long as the mesh is generated from the initial mesh. For this reason, we introduce another method where the density of nodes is determined using the above mentioned machine learning, and elements are generated using the Delaunay triangulation on the basis of the nodes obtained above. This method is shown to improve the mesh quality. Less

在本研究中，我们开发了一种实现电机有限元网格自动生成的新方法。该方法制备初始过程网格，计算单元属性P，其中P是单元处磁感应强度、到磁性材料最近角的距离、到最近电流源的距离等元素特征的线性组合。P值最大的元素被细分为两个更小的元素。这个过程不断重复，直到元素的数量等于一个规定的数量。然后计算所得网格的有限元分析误差。这种误差很大程度上取决于网格质量，而网格质量又取决于元素特征线性组合中的权重。我们使用遗传算法优化权重。当权重优化后，该方法可应用于其他类似电机有限元模型。结果表明，采用上述方法（称为简单法）得到的有限元网格中往往存在平面单元，不适合进行有限元分析。为了解决这一难题，我们引入了网格控制技术，根据元素边缘的长度和选择要细分的元素的标准来确定元素边缘的细分次数。用这种方法得到的有限元网格，称为网格控制方法，比用简单方法得到的网格要好。虽然网格控制方法提高了网格质量，但当初始过程网格中包含平面元素时，网格质量会变差。只要从初始网格生成网格，就很难克服这个问题。因此，我们引入另一种方法，使用上述机器学习确定节点的密度，并在上述获得的节点的基础上使用Delaunay三角剖分生成元素。该方法可以提高网格质量。少