论文标题
随机和后验优化，以减轻恒星设计中的线圈制造错误
Stochastic and a posteriori optimization to mitigate coil manufacturing errors in stellarator design
论文作者
论文摘要
最近在[Wechsung等人中显示了它。 Al。，Proc。纳特。学院。科学。美国，2022年，存在产生磁场的电磁线圈，这些磁场是准对称场的出色近似值，并且具有很好的粒子限制特性。使用基于高斯的过程模型进行线圈扰动，我们研究了制造错误对这些线圈性能的影响。我们表明，即使是相当小的错误也会导致明显的性能下降。尽管随机优化会产生较小的改进，但它无法显着减轻这些错误。作为随机优化的替代方法，我们制定了一个新的优化问题，用于计算线圈位置和电流的最佳调整，而无需更改线圈的形状。这些A-tosterii调整能够通过数量级来减少线圈错误的影响，从而为处理Stellarator设计中的制造公差提供了新的视角。
It was recently shown in [Wechsung et. al., Proc. Natl. Acad. Sci. USA, 2022] that there exist electromagnetic coils that generate magnetic fields which are excellent approximations to quasi-symmetric fields and have very good particle confinement properties. Using a Gaussian process based model for coil perturbations, we investigate the impact of manufacturing errors on the performance of these coils. We show that even fairly small errors result in noticeable performance degradation. While stochastic optimization yields minor improvements, it is not able to mitigate these errors significantly. As an alternative to stochastic optimization, we then formulate a new optimization problem for computing optimal adjustments of the coil positions and currents without changing the shapes of the coil. These a-posteriori adjustments are able to reduce the impact of coil errors by an order of magnitude, providing a new perspective for dealing with manufacturing tolerances in stellarator design.
