Optimization of a Stern–Gerlach Magnet by Magnetic Field–Circuit Coupling and Isogeometric Analysis

Pels, Andreas; Bontinck, Zeger; Corno, Jacopo; Gersem, Herbert De; Schöps, Sebastian

doi:10.1109/tmag.2015.2462806

Cited by 14 publications

(16 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We consider a real-world application, in particular a Rabi-type Stern-Gerlach magnet (see Figure 8(a)), similar to the one described in [27] and further studied in [36,37]. This magnet is currently in use at KU Leuven.…”

Section: Stern-gerlach Magnetmentioning

confidence: 99%

“…All computations are performed using a linearized two-dimensional model of the magnet's crosssection, as in [36]. The magnet's pole region, denoted with Ω p , is the only domain which is spatially resolved.…”

Section: Stern-gerlach Magnetmentioning

confidence: 99%

“…Region Ω (2) p refers to the air gap inside the magnet's pole region, while regions Ω (1) p and Ω (3) p to the regions on the left and right of the air gap, respectively, as in Figure 8(b). The contributions of the remaining yoke part and the coils are taken into account by a field-circuit coupling and a magnetic equivalent circuit [36]. More precisely, in a first step, the magnetic vector potential and the magnetic flux through the iron yoke are computed for the entire geometry.…”

Section: Stern-gerlach Magnetmentioning

confidence: 99%

See 2 more Smart Citations

Assessing the Performance of Leja and Clenshaw-Curtis Collocation for Computational Electromagnetics With Random Input Data

Loukrezis

Gersem

2019

Int. J. UncertaintyQuantification

Self Cite

View full text Add to dashboard Cite

We consider the problem of quantifying uncertainty regarding the output of an electromagnetic field problem in the presence of a large number of uncertain input parameters. In order to reduce the growth in complexity with the number of dimensions, we employ a dimensionadaptive stochastic collocation method based on nested univariate nodes. We examine the accuracy and performance of collocation schemes based on Clenshaw-Curtis and Leja rules, for the cases of uniform and bounded, non-uniform random inputs, respectively. Based on numerical experiments with an academic electromagnetic field model, we compare the two rules in both the univariate and multivariate case and for both quadrature and interpolation purposes. Results for a real-world electromagnetic field application featuring high-dimensional input uncertainty are also presented.keywordsdimension adaptivity, Clenshaw-Curtis, computational electromagnetics, Leja, sparse grids, stochastic collocation, uncertainty quantification.

show abstract

Section: Stern-gerlach Magnetmentioning

confidence: 99%

Section: Stern-gerlach Magnetmentioning

confidence: 99%

Section: Stern-gerlach Magnetmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the Performance of Leja and Clenshaw-Curtis Collocation for Computational Electromagnetics With Random Input Data

Loukrezis

Gersem

2019

Int. J. UncertaintyQuantification

Self Cite

View full text Add to dashboard Cite

show abstract

“…It should be noted that we consider a linear material here with reluctivity ν = 1/(µ 0 µ r ), where µ 0 = 4π10 −7 H/m and µ r = 1000, respectively. This explains deviations in the results in the order of 2% compared to [8], where a nonlinear material was considered. A constant current of 2600 A is imposed.…”

Section: Stern-gerlach Magnetmentioning

confidence: 93%

“…Assuming that |∇u 0 | > 0 in Ω 0 , (37) is well-defined as u 0 is smooth. During the design phase, the aim is to maximize F τ , while minimizing the field inhomogeneity [8].…”

Section: Derivative Of Magnetic Flux Densitymentioning

confidence: 99%

A defect corrected finite element approach for the accurate evaluation of magnetic fields on unstructured grids

Rmer

Schps

Gersem

2017

Journal of Computational Physics

Self Cite

View full text Add to dashboard Cite

In electromagnetic simulations of magnets and machines one is often interested in a highly accurate and local evaluation of the magnetic field uniformity. Based on local post-processing of the solution, a defect correction scheme is proposed as an easy to realize alternative to higher order finite element or hybrid approaches. Radial basis functions (RBF)s are key for the generality of the method, which in particular can handle unstructured grids. Also, contrary to conventional finite element basis functions, higher derivatives of the solution can be evaluated, as required, e.g., for deflection magnets. Defect correction is applied to obtain a solution with improved accuracy and adjoint techniques are used to estimate the remaining error for a specific quantity of interest. Significantly improved (local) convergence orders are obtained. The scheme is also applied to the simulation of a Stern-Gerlach magnet currently in operation.

show abstract

Quadrupole magnet design based on genetic multi-objective optimization

Diehl,

Tresckow,

Scholtissek

et al. 2023

Electr Eng

View full text Add to dashboard Cite

This work suggests to optimize the geometry of a quadrupole magnet by means of a genetic algorithm adapted to solve multi-objective optimization problems. To that end, a non-domination sorting genetic algorithm known as NSGA-III is used. The optimization objectives are chosen such that a high magnetic field quality in the aperture of the magnet is guaranteed, while simultaneously the magnet design remains cost-efficient. The field quality is computed using a magnetostatic finite element model of the quadrupole, the results of which are post-processed and integrated into the optimization algorithm. An extensive analysis of the optimization results is performed, including Pareto front movements and identification of best designs.

show abstract

Optimization of a Stern–Gerlach Magnet by Magnetic Field–Circuit Coupling and Isogeometric Analysis

Cited by 14 publications

References 16 publications

Assessing the Performance of Leja and Clenshaw-Curtis Collocation for Computational Electromagnetics With Random Input Data

Assessing the Performance of Leja and Clenshaw-Curtis Collocation for Computational Electromagnetics With Random Input Data

A defect corrected finite element approach for the accurate evaluation of magnetic fields on unstructured grids

Quadrupole magnet design based on genetic multi-objective optimization

Contact Info

Product

Resources

About