Optimizing Shape Design of Magnetic Pole Contour using a Special Mesh Generator

Sowmyanarayanan Krishnakumar, S. Hoole
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Abstract

First order optimization methods, while being powerful and rapidly convergent, suffer from the fact that as the descriptive geometric parameters change from iteration to iteration, corresponding to these new geometries, new meshes need to be implemented. Correspondingly the new topologies of the meshes introduce non-physical jumps in the object function. These jumps are seen as physical minima by the optimization algorithm and slow down and even prevent the identification of the true global minimum. Different starting points have been used with some amelioration but there has been no satisfactory solution to this problem. To overcome this problem, a special mesh generator has been introduced earlier. This generator allows us to move a node which is tied directly to a parameter without changing connectivity of nodes in the mesh topology. This procedure yields C1 continuity of the object functions. Thus, gradient optimization methods can be efficiently used for shape optimization problems. In this paper, the scheme is implemented optimizing the shape of magnetic pole contour with important implications for the problem and the accuracy of its solutions.
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利用专用网格发生器优化磁极轮廓形状设计
一阶优化方法虽然功能强大,收敛速度快,但由于描述几何参数在迭代过程中不断变化,需要对这些新的几何形状进行相应的网格划分。相应地,网格的新拓扑在目标函数中引入了非物理跳跃。这些跳跃被优化算法视为物理最小值,并且减慢甚至阻止了真正全局最小值的识别。使用了不同的起点并进行了一些改进,但没有令人满意的解决方案。为了克服这个问题,一种特殊的网格生成器已经在前面介绍过。这个生成器允许我们移动一个节点,它直接绑定到一个参数,而不改变网格拓扑中节点的连通性。这个过程产生对象函数的C1连续性。因此,梯度优化方法可以有效地用于形状优化问题。本文实现了磁极轮廓形状的优化方案,对该问题及其解的精度具有重要意义。
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