Uncertainty Quantification and Sensitivity Analysis in Electrical Machines With Stochastically Varying Machine Parameters

Offermann, Peter; Mac, Hung; Nguyen, Thu Trang; Clénet, S.; Gersem, Herbert De; Hameyer, Kay

doi:10.1109/tmag.2014.2354511

Cited by 31 publications

(17 citation statements)

References 12 publications

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“…The influence of the dimension and material characteristics variability on the performances of an electrical machines produced in mass is also studied when the number of random parameters is about a dozen [29,30]. The aim is to propose a methodology based on a stochastic approach to assess the influence of the variability of the manufacturing process on the performances of the electrical machines which can be applied in robust design.…”

Section: Applicationsmentioning

confidence: 99%

Approximation Methods to Solve Stochastic Problems in Computational Electromagnetics

Clénet

2016

Scientific Computing in Electrical Engineering

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. To cite this version :Stephane CLÉNET -Approximation Methods to Solve Stochastic Problems in Computational Electromagnetics -2016Any correspondence concerning this service should be sent to the repository Administrator : archiveouverte@ensam.eu Approximation Methods to solve Stochastic Problems in Computational ElectromagneticsStéphane ClénetAbstract To account for uncertainties on model parameters, the stochastic approach can be used. The model parameters as well as the outputs are then random fields or variables. Several methods are available in the literature to solve stochastic models like sampling methods, perturbation methods or approximation methods. In this paper, we propose an overview on the solution of stochastic problems in computational electromagnetics using approximation methods. Some applications will be presented in order to illustrate the possibilities offered by the approximation methods but also their current limitations due to the curse of dimensionality. Finally, recent numerical techniques proposed in the literature to face the curse of dimensionality are presented for non-intrusive and intrusive approaches.

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Section: Applicationsmentioning

confidence: 99%

Approximation Methods to Solve Stochastic Problems in Computational Electromagnetics

Clénet

2016

Scientific Computing in Electrical Engineering

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“…The issue is then to be able to model the variability of the dimensions with random variables. Until now, the stochastic approach is generally applied with random parameters modelled from expertise and almost no measurement to characterize the dispersion [11]. In this paper, we propose to quantify, using a stochastic approach, the influence on a claw pole machine performances of the dimension dispersions introduced by a process of fabrication.…”

Section: Study Of the Influence Of The Fabrication Process Imperfectimentioning

confidence: 99%

Study of the Influence of the Fabrication Process Imperfections on the Performance of a Claw Pole Synchronous Machine Using a Stochastic Approach

Liu

Mac²,

Clénet

et al. 2016

IEEE Trans. Magn.

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In mass production, fabrication processes of electrical machines are not perfectly repeatable with time, leading to dispersions on the dimensions which are not equal to their nominal values. The issue is then to link the dispersions on the dimensions which are uncertain to the performances of electrical machines in order to evaluate their influence. To deal with uncertainty, there is a growing interest in the stochastic approach, which consists in modelling the uncertain parameters with random variables. In fact, this approach enables to quantify the influence of the variability of the uncertain parameters on the variability of the quantities of interest. In this paper, a stochastic approach coupled with a 3D Finite Element model is used to study the influence of the fabrication process imperfections like the rotor eccentricity and the stator deformation on the performances of a claw pole synchronous machine.

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“…In the context of this work, a further reason for investigating and improving the LS-PCE method is its popularity in the setting of EM simulations. [33][34][35][36][37] The approximation accuracy of the PCE is crucially affected by the choice of the polynomial space P M . This is especially relevant in high-dimensional approximations because of the fact that the dimension of P M grows very fast with the number of RVs, which constitutes a manifestation of the so-called curse of dimensionality.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive least squares polynomial chaos expansions in high‐frequency applications

Loukrezis

Galetzka

Gersem

2020

Int J Numerical Modelling

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We present an algorithm for computing sparse, least squares-based polynomial chaos expansions, incorporating both adaptive polynomial bases and sequential experimental designs. The algorithm is employed to approximate stochastic high-frequency electromagnetic models in a black-box way, in particular, given only a dataset of random parameter realizations and the corresponding observations regarding a quantity of interest, typically a scattering parameter. The construction of the polynomial basis is based on a greedy, adaptive, sensitivity-related method. The sequential expansion of the experimental design employs different optimality criteria, with respect to the algebraic form of the least squares problem. We investigate how different conditions affect the robustness of the derived surrogate models, that is, how much the approximation accuracy varies given different experimental designs. It is found that relatively optimistic criteria perform on average better than stricter ones, yielding superior approximation accuracies for equal dataset sizes. However, the results of strict criteria are significantly more robust, as reduced variations regarding the approximation accuracy are obtained, over a range of experimental designs. Two criteria are proposed for a good accuracy-robustness trade-off.keywordspolynomial chaos, surrogate modeling, high-frequency electromagnetic devices, least squares regression, adaptive basis, sequential experimental design

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Uncertainty Quantification and Sensitivity Analysis in Electrical Machines With Stochastically Varying Machine Parameters

Cited by 31 publications

References 12 publications

Approximation Methods to Solve Stochastic Problems in Computational Electromagnetics

Approximation Methods to Solve Stochastic Problems in Computational Electromagnetics

Study of the Influence of the Fabrication Process Imperfections on the Performance of a Claw Pole Synchronous Machine Using a Stochastic Approach

Robust adaptive least squares polynomial chaos expansions in high‐frequency applications

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