Piotr Putek

The initial value problem for a model of magnetic Micro-Electro-Mechanical System (magMEMS) with current-carrying conductors is considered. The model equation is a conservative MEMS oscillator due to the presence of the singular term corresponding to the magnetic force between two current-conducting wires. If the excitation parameter is below a certain threshold, the actuator oscillates. Fourier approximation of periodic solutions is enhanced by neural network space handling for both the limiting case where the geometric parameter approaches zero and the general case with arbitrary geometric parameter values. The method is validated through numerical comparisons with high-precision solutions, and its limitations are identified during the verification of experimental results. The findings can be useful for the design of magMEMS models.

Quadrupole resonators (QPRs) serve to characterize superconducting samples. Like any cavity, during operation, they will deviate from the design geometry for various reasons. Those deviations can be static, stemming from manufacturing variations reflected in the manufacturing tolerances, or dynamic, such as electromagnetic radiation pressure (Lorentz detuning) or microphonics. As a result, a QPR’s measurement accuracy and general operation can be severely limited. In particular, during operation, it became evident that the third operating mode of typical QPRs is mainly affected. In this work, by solving the underlying multiphysics problem with random input parameters, we predict the predominant sources of significant measurement bias in surface resistance. On the one hand, we employ the stochastic collocation method compound with the polynomial chaos expansion (PC-SCM) to quantify uncertainties in the physical model governed by a coupled electro-stress-heat problem. On the other hand, we explore the perturbation analysis to calculate the mean-worst-scenario bound of the merit functions due to the first-order truncation of the Taylor expansion around mean parameter values. The developed method allows us to study the effect of a small nonlinear deformation on the performance of the QPR. Finally, we discuss the simulation results and their implication for the operational conditions of the QPRs.

For a precise determination of the rf properties of superconducting materials, a calorimetric measurement is carried out with the aid of a so-called quadrupole resonator (QPR). This procedure is affected by certain systematic measurement errors with various sources of uncertainties. In this paper, to reduce the impact of geometrical uncertainties on the measurement bias, the modified steepest descent method is used for the multiobjective shape optimization of a QPR in terms of an expectation measure. Thereby, variations of geometrical parameters are modeled by the polynomial chaos expansion technique. Then, the resulting Maxwell’s eigenvalue problem with random input data is solved using the polynomial chaos-based stochastic collocation method. Furthermore, to assess the contribution of the particular geometrical parameters, the variance-based sensitivity analysis is proposed. This allows for modifying the steepest descent algorithm, which results in reducing the computational load needed to find optimal solutions. Finally, optimization results in the form of an efficient approximation of the Pareto front for a three-dimensional model of the QPR are shown.

For a precise determination of the radio frequency (RF) properties of superconducting materials, a calorimetric measurement is carried out with the aid of a so-called Quadrupole Resonator (QPR). This procedure is affected by certain systematic measurement errors with various sources of uncertainties. In this paper, to reduce the impact of geometrical uncertainties on the measurement bias, the modified steepest descent method is used for the multi-objective shape optimization of a QPR {in terms of an expectation measure}. Thereby, variations of geometrical parameters are modeled by the Polynomial Chaos (PC) expansion technique. Then, the resulting Maxwell's eigenvalue problem with random input data is solved using the PC-based stochastic collocation method (PC-SCM). Furthermore, to assess the contribution of the particular geometrical parameters, the variance-based sensitivity analysis is proposed. This allows for modifying the steepest descent algorithm, which results in reducing the computational load needed to find optimal solutions. Finally, optimization results in the form of an efficient approximation of the Pareto front for a three dimensional (3D) model of the QPR are shown.