Nonlinear Stochastic Galerkin and Collocation Methods: Application to a Ferromagnetic Cylinder Rotating at High Speed

Year:    2010

Communications in Computational Physics, Vol. 8 (2010), Iss. 5 : pp. 947–975

Abstract

The stochastic Galerkin and stochastic collocation method are two state-of-the-art methods for solving partial differential equations (PDE) containing random coefficients. While the latter method, which is based on sampling, can straightforwardly be applied to nonlinear stochastic PDEs, this is nontrivial for the stochastic Galerkin method and approximations are required. In this paper, both methods are used for constructing high-order solutions of a nonlinear stochastic PDE representing the magnetic vector potential in a ferromagnetic rotating cylinder. This model can be used for designing solid-rotor induction machines in various machining tools. A precise design requires to take ferromagnetic saturation effects into account and uncertainty on the nonlinear magnetic material properties. Implementation issues of the stochastic Galerkin method are addressed and a numerical comparison of the computational cost and accuracy of both methods is performed. The stochastic Galerkin method requires in general less stochastic unknowns than the stochastic collocation approach to reach a certain level of accuracy, however at a higher computational cost.

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cicp.220509.200110a

Communications in Computational Physics, Vol. 8 (2010), Iss. 5 : pp. 947–975

Published online:    2010-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    29

Keywords:   

  1. Reconstruction of the doping profile in Vlasov–Poisson system

    Lai, Ru-Yu | Li, Qin | Sun, Weiran

    Inverse Problems, Vol. 40 (2024), Iss. 11 P.115004

    https://doi.org/10.1088/1361-6420/ad7c78 [Citations: 0]
  2. Inverse radiative transfer with goal-oriented hp-adaptive mesh refinement: adaptive-mesh inversion

    Du, Shukai | Stechmann, Samuel N

    Inverse Problems, Vol. 39 (2023), Iss. 11 P.115002

    https://doi.org/10.1088/1361-6420/acf785 [Citations: 2]
  3. Boundary-layer structures arising in linear transport theory

    Gaggioli, E. L. | Estrada, Laura C. | Bruno, Oscar P.

    Physical Review E, Vol. 110 (2024), Iss. 2

    https://doi.org/10.1103/PhysRevE.110.025306 [Citations: 0]
  4. On diffusive scaling in acousto-optic imaging

    Chung, Francis J | Lai, Ru-Yu | Li, Qin

    Inverse Problems, Vol. 36 (2020), Iss. 8 P.085011

    https://doi.org/10.1088/1361-6420/ab9f85 [Citations: 2]
  5. Stochastic models for the evaluation of magnetisation faults

    Norio Takahashi, Prof. | Offermann, Peter | Hameyer, Kay

    COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 33 (2013), Iss. 1/2 P.245

    https://doi.org/10.1108/COMPEL-10-2012-0210 [Citations: 4]
  6. Inverse transport problem in fluorescence ultrasound modulated optical tomography with angularly averaged measurements

    Li, Wei | Yang, Yang | Zhong, Yimin

    Inverse Problems, Vol. 36 (2020), Iss. 2 P.025011

    https://doi.org/10.1088/1361-6420/ab4609 [Citations: 5]
  7. Solving parameter estimation problems with discrete adjoint exponential integrators

    Römer, Ulrich | Narayanamurthi, Mahesh | Sandu, Adrian

    Optimization Methods and Software, Vol. 33 (2018), Iss. 4-6 P.750

    https://doi.org/10.1080/10556788.2018.1448087 [Citations: 3]
  8. Online learning in optical tomography: a stochastic approach

    Chen, Ke | Li, Qin | Liu, Jian-Guo

    Inverse Problems, Vol. 34 (2018), Iss. 7 P.075010

    https://doi.org/10.1088/1361-6420/aac220 [Citations: 14]
  9. Stochastic Modeling and Regularity of the Nonlinear Elliptic curl--curl Equation

    Römer, Ulrich | Schöps, Sebastian | Weiland, Thomas

    SIAM/ASA Journal on Uncertainty Quantification, Vol. 4 (2016), Iss. 1 P.952

    https://doi.org/10.1137/15M1026535 [Citations: 8]
  10. Experimental spectro-angular mapping of light distribution in turbid media

    Grabtchak, Serge

    Journal of Biomedical Optics, Vol. 17 (2012), Iss. 6 P.067007

    https://doi.org/10.1117/1.JBO.17.6.067007 [Citations: 12]
  11. Recovery of the absorption coefficient in radiative transport from a single measurement

    Acosta, Sebastian

    Inverse Problems & Imaging, Vol. 9 (2015), Iss. 2 P.289

    https://doi.org/10.3934/ipi.2015.9.289 [Citations: 2]
  12. Numerical Approximation of the Magnetoquasistatic Model with Uncertainties

    Uncertainty Quantification

    Römer, Ulrich

    2016

    https://doi.org/10.1007/978-3-319-41294-8_5 [Citations: 0]
  13. Analysis of FE

    Slawomir Wiak, Professor | Offermann, Peter | Hameyer, Kay

    COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 34 (2015), Iss. 2 P.596

    https://doi.org/10.1108/COMPEL-07-2014-0174 [Citations: 0]
  14. Overview of diffuse optical tomography and its clinical applications

    Hoshi, Yoko | Yamada, Yukio

    Journal of Biomedical Optics, Vol. 21 (2016), Iss. 9 P.091312

    https://doi.org/10.1117/1.JBO.21.9.091312 [Citations: 173]
  15. Primal-dual approach to optical tomography with discretized path integral with efficient formulations

    Yuan, Bingzhi | Tamaki, Toru | Raytchev, Bisser | Kaneda, Kazufumi

    Journal of Medical Imaging, Vol. 4 (2017), Iss. 3 P.033501

    https://doi.org/10.1117/1.JMI.4.3.033501 [Citations: 0]