Uncertainty Quantification for Maxwell's Eigenproblem based on Isogeometric Analysis and Mode Tracking

TL;DR

This paper presents an uncertainty quantification workflow for Maxwell's eigenproblem in superconducting cavities, combining isogeometric analysis, mode tracking, and stochastic collocation to accurately assess sensitivity to geometric deformations.

Contribution

It introduces a novel eigenvalue tracking method ensuring solution consistency across stochastic collocation points using isogeometric analysis for precise geometry representation.

Findings

Efficient eigenpair matching across stochastic samples.

Accurate sensitivity analysis of electromagnetic modes.

Parallelizable approach for uncertainty quantification.

Abstract

The electromagnetic field distribution as well as the resonating frequency of various modes in superconducting cavities used in particle accelerators for example are sensitive to small geometry deformations. The occurring variations are motivated by measurements of an available set of resonators from which we propose to extract a small number of relevant and independent deformations by using a truncated Karhunen-Lo\`eve expansion. The random deformations are used in an expressive uncertainty quantification workflow to determine the sensitivity of the eigenmodes. For the propagation of uncertainty, a stochastic collocation method based on sparse grids is employed. It requires the repeated solution of Maxwell's eigenvalue problem at predefined collocation points, i.e., for cavities with perturbed geometry. The main contribution of the paper is ensuring the consistency of the solution, i.e., matching the eigenpairs, among the various eigenvalue problems at the stochastic collocation points. To this end, a classical eigenvalue tracking technique is proposed that is based on homotopies between collocation points and a Newton-based eigenvalue solver. The approach can be efficiently parallelized while tracking the eigenpairs. In this paper, we propose the application of isogeometric analysis since it allows for the exact description of the geometrical domains with respect to common computer-aided design kernels, for a straightforward and convenient way of handling geometrical variations and smooth solutions.