This paper reports on comprehensive efforts on uncertainty quantification and global sensitivity analysis for accelerator cavity design. As a case study object the TESLA shaped superconducting cavities, as produced for the European X-ray Free Electron Laser (EXFEL), are selected. The choice for these cavities is explained by the available measurement data that can be leveraged to substantiate the simulation model. Each step of the manufacturing chain is documented together with the involved uncertainties. Several of these steps are mimicked on the simulation side, e.g., by introducing a random eigenvalue problem. The uncertainties are then quantified numerically and in particular the sensitivities give valuable insight into the system behavior. We also compare these findings to purely statistical studies carried out for the manufactured cavities. More advanced, adaptive, surrogate modeling techniques are adopted, which are crucial to incorporate a large number of uncertain parameters. The main contribution is the detailed comparison and fusion of measurement results for the EXFEL cavities on the one hand and simulation based uncertainty studies on the other hand. After introducing the quantities of physical interest for accelerator cavities and the Maxwell eigenvalue problem, the details on the manufacturing of the EXFEL cavities and measurements are reported. This is followed by uncertainty modeling with quantification studies.

本文报告了关于加速器腔体设计不确定性量化和整体灵敏度分析的综合性工作。作为案例研究对象，选择了为欧洲X射线自由电子激光器（EXFEL）生产的TESLA形超导腔。这些腔的选择由可用的测量数据解释，可以利用这些数据来证实仿真模型。记录了制造链的每个步骤以及所涉及的不确定性。例如，通过引入随机特征值问题，可以在仿真方面模拟这些步骤中的几个步骤。然后对不确定性进行数值量化，尤其是灵敏度可对系统行为提供宝贵的见解。我们还将这些发现与对制造型腔进行的纯粹统计研究相比较。采用了更先进的，自适应的替代建模技术，这对于合并大量不确定参数至关重要。主要贡献在于一方面是EXFEL腔的测量结果的详细比较和融合，另一方面是基于仿真的不确定性研究。在介绍了加速器腔的物理关注量和麦克斯韦特征值问题后，报告了EXFEL腔的制造和测量的详细信息。接下来是带有定量研究的不确定性建模。主要贡献在于一方面是EXFEL腔的测量结果的详细比较和融合，另一方面是基于仿真的不确定性研究。在介绍了加速器腔的物理关注量和麦克斯韦特征值问题后，报告了EXFEL腔的制造和测量的详细信息。接下来是带有定量研究的不确定性建模。主要贡献在于一方面是EXFEL腔的测量结果的详细比较和融合，另一方面是基于仿真的不确定性研究。在介绍了加速器腔的物理关注量和麦克斯韦特征值问题后，报告了EXFEL腔的制造和测量的详细信息。接下来是带有定量研究的不确定性建模。