四倾斜镜学生项目望远镜的对准和热漂移方面

G. Fütterer, M. Wagner, L. Bauer, S. Wittl
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引用次数: 1

摘要

德根多夫理工学院(DIT)及其应用自然科学和工业工程学院向学生传授广泛的知识。澄清看似孤立的知识领域之间存在的相互关系是一个永久的过程。为了将其付诸实践,一项望远镜建设工程开始了。内部学生项目的基地是Teisnach的技术园区,该园区将包括望远镜光学在内的高精度光学器件的工艺开发、生产和测量能力捆绑在一起。第一个光学设计是基于1989年M. Brunn1, 2发表的参数空间子集(后来由D. Stevick建立为f/12-system3),使用直径为400毫米的主镜M1。f/8系统在0.7°的整个视场范围内提供的Strehl比SR≥0.8。即使这看起来足够,制造公差、调整公差、热漂移和位置变化也会大大降低Strehl比。为了获得可靠的可接受公差值,进行了统计蒙特卡罗分析。作为结果,管的设计被改变,新的镜子安装的设计开始。这样做是为了达到所需的刚度。采用有限元分析方法对碳纤维增强聚合物(CFRP)和FeNi36两种新型钢管设计进行了试验。此外,还验证了基于深度学习的像差检测的实用性。利用卷积神经网络(CNN)对星图进行分析,得到泽尼克多项式。描述了目前的发展状况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Alignment and thermal drift aspects of a four-tilted-mirror student project telescope
The Deggendorf Institute of Technology (DIT) and its Faculty of Applied Natural Sciences and Industrial engineering transfer a broad spectrum of knowledge to the students. The clarification of the interrelations that exist between seemingly isolated fields of knowledge is a permanent process. In order to put this into practice, a telescope construction project was started. The base of the in-house student project is the Technology Campus in Teisnach, which bundles capacities for process development, production and measurement of high-precision optics, including telescope optics. A first optical design, which is based on a subset of the parameter space published in 1989 by M. Brunn1, 2 (later built by D. Stevick as f/12-system3 ), made use of a primary mirror M1 with a diameter of 400 mm. An f/8-system provide a Strehl ratio SR ≥ 0.8 over an entire field of view of 0.7° deg. Even if this seems to be sufficient, manufacturing tolerances, adjustment tolerances, thermal drift and positional changes considerably reduce the Strehl ratio. In order to obtain reliable values of acceptable tolerances, statistical Monte Carlo analyses had been carried out. As consequences, the tube design was changed and the design of new mirror mounts started. This was done to achieve the required stiffness. The new tube designs, one based on carbon-fiber-reinforced polymer (CFRP) and one based on FeNi36, had been tested by using FEM analysis. In addition, the practicability of deep learning based aberration detection was tested. Zernike polynomials obtained by analyzing the star images with a Convolutional Neuronal Network (CNN). The current state of the development is described.
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