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An interval finite element method for electromagnetic problems with spatially uncertain parameters

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Abstract

During the manufacturing process of dielectric materials used in electromagnetic engineering, the electromagnetic parameters are often spatially uncertain due to the processing technology, environmental temperature, personal operations, etc. Traditionally, the random field model can be used to measure the spatial uncertainties, but its construction requires a large number of samples. On the contrary, the interval field model only needs the upper and lower bounds of the spatially uncertain parameters, which requires much less samples and furthermore is easy to understand and use for engineers. Therefore, in this paper, the interval field model is introduced to describe the spatial uncertainties of dielectric materials, and then an interval finite element method (IFEM) is proposed to calculate the upper and lower bounds of electromagnetic responses. Firstly, the interval field of the dielectric material is represented by the interval K-L expansion and inserted into the scalar Helmholtz wave equations, and thus the interval equilibrium equations are constructed according to the node-based finite element method. Secondly, a perturbation interval finite element method is developed for calculating the upper and lower bounds of electromagnetic responses such as the electric strength and magnetic strength. Finally, the effectiveness of the proposed method is verified by three numerical examples.

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References

  1. Mac D H, Clenet S, Mipo J C, et al. A priori error indicator in the transformation method for problems with geometric uncertainties. IEEE Trans Magn, 2013, 49: 1597–1600

    Google Scholar 

  2. Chauvière C, Hesthaven J S, Lurati L. Computational modeling of uncertainty in time-domain electromagnetics. SIAM J Sci Comput, 2006, 28: 751–775

    MathSciNet  MATH  Google Scholar 

  3. Tenuti L, Anselmi N, Rocca P, et al. Minkowski sum method for planar arrays sensitivity analysis with uncertain-but-bounded excitation tolerances. IEEE Trans Antennas Propagat, 2017, 65: 167–177

    Google Scholar 

  4. Bdour T, Reineix A. Global sensitivity analysis and uncertainty quantification of radiated susceptibility in PCB using nonintrusive polynomial chaos expansions. IEEE Trans Electromagn Compat, 2016, 58: 939–942

    Google Scholar 

  5. Özbayat S, Janaswamy R. Assessment of adaptive sparse grid collocation methods in wave propagation environments with uncertainty. IEEE Trans Antennas Propagat, 2014, 62: 6354–6364

    MathSciNet  MATH  Google Scholar 

  6. Liu M, Gao Z, Hesthaven J S. Adaptive sparse grid algorithms with applications to electromagnetic scattering under uncertainty. Appl Numer Math, 2011, 61: 24–37

    MathSciNet  MATH  Google Scholar 

  7. Chauvire C, Hesthaven J S, Wilcox L C. Efficient computation of RCS from scatterers of uncertain shapes. IEEE Trans Antennas Propagat, 2007, 55: 1437–1448

    Google Scholar 

  8. Edwards R S, Marvin A C, Porter S J. Uncertainty analyses in the finite-difference time-domain method. IEEE Trans Electromagn Compat, 2010, 52: 155–163

    Google Scholar 

  9. Kane Y. Numerical solution of initial boundary value problems involving maxwell’s equations in isotropic media. IEEE Trans Antennas Propagat, 1966, 14: 302–307

    MATH  Google Scholar 

  10. Austin A C M, Sood N, Siu J, et al. Application of polynomial chaos to quantify uncertainty in deterministic channel models. IEEE Trans Antennas Propagat, 2013, 61: 5754–5761

    Google Scholar 

  11. **u D, Karniadakis G E. The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM J Sci Comput, 2002, 24: 619–644

    MathSciNet  MATH  Google Scholar 

  12. Nobile F, Tempone R, Webster C G. A sparse grid stochastic collocation method for partial differential equations with random input data. SIAM J Numer Anal, 2008, 46: 2309–2345

    MathSciNet  MATH  Google Scholar 

  13. Shen J, Yang H, Chen J. Stochastic analysis of variations of electromagnetic properties of composite mixtures. Electron Lett, 2010, 46: 127–129

    Google Scholar 

  14. Pyrialakos G, Papadimopoulos A, Thedoros Z, et al. A curvilinear stochastic-FDTD algorithm for 3-D EMC problems with media uncertainties. COMPEL, 2015, 34: 1637–1651

    Google Scholar 

  15. Rossi M, Stockman G J, Rogier H, et al. Stochastic analysis of the efficiency of a wireless power transfer system subject to antenna variability and position uncertainties. Sensors, 2016, 16: 1100

    Google Scholar 

  16. Gilbert M S, Teixeira F L. A small-perturbation automatic-differentiation method for determining uncertainty in computational electromagnetics. IEEE Trans Antennas Propagat, 2012, 60: 5305–5314

    MathSciNet  MATH  Google Scholar 

  17. Li P, Jiang L J. Uncertainty quantification for electromagnetic systems using ASGC and DGTD method. IEEE Trans Electromagn Compat, 2015, 57: 754–763

    Google Scholar 

  18. Salas-Natera M A, Rodrez-Osorio M A. Analytical evaluation of uncertainty on active antenna arrays. IEEE Trans Aerosp Electron Syst, 2012, 48: 1903–1913

    Google Scholar 

  19. Ganta S S, van Veen B D, Hagness S C. On the accuracy of polynomial models in stochastic computational electromagnetics simulations involving dielectric uncertainties. Antennas Wirel Propag Lett, 2017, 16: 2594–2597

    Google Scholar 

  20. Wang Z H, Jiang C, Ruan X X, et al. Uncertainty propagation analysis of T/R modules. Int J Comput Methods, 2019, 16: 1850105

    MATH  Google Scholar 

  21. Druesne F, Hallal J, Lardeur P, et al. Modal stability procedure applied to variability in vibration from electromagnetic origin for an electric motor. Finite Elem Anal Des, 2016, 122: 61–74

    Google Scholar 

  22. Mayhan J. Effects of random phase errors at Kamp resulting from a composite material radome. IEEE Trans Antennas Propagat, 1976, 24: 356–365

    Google Scholar 

  23. Ghanem R G, Spanos P D. Stochastic Finite Elements: A Spectral Approach. Heidelberg, Berlin: Springer 1991

    MATH  Google Scholar 

  24. Greene M S, Liu Y, Chen W, et al. Computational uncertainty analysis in multiresolution materials via stochastic constitutive theory. Comput Method Appl Mech Eng, 2011, 200: 309–325

    MathSciNet  MATH  Google Scholar 

  25. Schwab C, Todor R A. Karhunen-Loève approximation of random fields by generalized fast multipole methods. J Comput Phys, 2006, 217: 100–122

    MathSciNet  MATH  Google Scholar 

  26. Wang N, Cheng J, Pyatakov A, et al. Multiferroic properties of modified BiFeO3-PbTiO3-based ceramics: Random-field induced release of latent magnetization and polarization. Phys Rev B, 2005, 72: 104434

    Google Scholar 

  27. Zhu H, Zhang L M. Characterizing geotechnical anisotropic spatial variations using random field theory. Can Geotech J, 2013, 50: 723–734

    Google Scholar 

  28. Most T, Bucher C. Probabilistic analysis of concrete cracking using neural networks and random fields. Probab Eng Mech, 2007, 22: 219–229

    Google Scholar 

  29. Sasikumar P, Suresh R, Vijayaghosh P K, et al. Experimental characterisation of random field models for CFRP composite panels. Compos Struct, 2015, 120: 451–471

    Google Scholar 

  30. Ni B Y. Interval process and interval field models and application in uncertainty analysis of structures (in Chinese). Dissertation of Doctoral Degree. Changsha: Hunan University, 2017

    Google Scholar 

  31. Kuzuoglu M, Ozgun O. Combining perturbation theory and transformation electromagnetics for finite element solution of Helmholtz-type scattering problems. J Comput Phys, 2014, 274: 883–897

    MATH  Google Scholar 

  32. Harrington R F. Time-Harmonic Electromagnetic Fields. New York: McGraw-Hill 1961

    Google Scholar 

  33. ** J M. The Finite Element Method in Electromagnetics. New York: Wiley-IEEE Press 2002

    MATH  Google Scholar 

  34. ** J M. Theory and Computation of Electromagnetic Fields. Hoboken, NJ: Wiley 2010

    Google Scholar 

  35. Díaz R D, Rico R, García-Castillo L E, et al. Parallelizing a hybrid finite element-boundary integral method for the analysis of scattering and radiation of electromagnetic waves. Finite Elem Anal Des, 2010, 46: 645–657

    MathSciNet  Google Scholar 

  36. Soares Jr. D, Leal D R M. Electromagnetic wave propagation analysis by an explicit adaptive technique based on connected space-time discretizations. Finite Elem Anal Des, 2018, 141: 1–16

    Google Scholar 

  37. Xu K, Ding D Z, Fan Z H, et al. FSAI preconditioned CG algorithm combined with GPU technique for the finite element analysis of electromagnetic scattering problems. Finite Elem Anal Des, 2011, 47: 387–393

    Google Scholar 

  38. Jiang C, Ni B Y, Han X, et al. Non-probabilistic convex model process: A new method of time-variant uncertainty analysis and its application to structural dynamic reliability problems. Comput Method Appl Mech Eng, 2014, 268: 656–676

    MathSciNet  MATH  Google Scholar 

  39. Jiang C, Han X, Lu G Y, et al. Correlation analysis of non-probabilistic convex model and corresponding structural reliability technique. Comput Method Appl Mech Eng, 2011, 200: 2528–2546

    MATH  Google Scholar 

  40. Sudret B, Der Kiureghian A. Stochastic finite element methods and reliability: A state-of-the-art report. Research Report. Berkeley: Department of Civil and Environmental Engineering, University of California, 2000

    Google Scholar 

  41. Hansen E R. On solving systems of equations using interval arithmetic. Math Comp, 1968, 22: 374–384

    MathSciNet  MATH  Google Scholar 

  42. Stefanou G. The stochastic finite element method: Past, present and future. Comput Method Appl Mech Eng, 2009, 198: 1031–1051

    MATH  Google Scholar 

  43. Kleiber M, Hien T D. The Stochastic Finite Element Method: Basic Perturbation Technique and Computer Implementation. Weinheim: Wiley 1992

    MATH  Google Scholar 

  44. Long X Y, Jiang C, Yang C, et al. A stochastic scaled boundary finite element method. Comput Method Appl Mech Eng, 2016, 308: 23–46

    MathSciNet  Google Scholar 

  45. Haldar A, Mahadevan S. Reliability Assessment Using Stochastic Finite Element Analysis. New York: John Wiley 2000

    Google Scholar 

  46. Neumaier A. Interval Methods for Systems of Equations. New York: Cambridge University Press 1990

    MATH  Google Scholar 

  47. **a B, Yu D. Modified interval perturbation finite element method for a structural-acoustic system with interval parameters. J Appl Mech, 2013, 80: 041027

    Google Scholar 

  48. Lü H, Shangguan W B, Yu D. Uncertainty quantification of squeal instability under two fuzzy-interval cases. Fuzzy Sets Syst, 2017, 328: 70–82

    MathSciNet  Google Scholar 

  49. Lü H, Shangguan W B, Yu D. An imprecise probability approach for squeal instability analysis based on evidence theory. J Sound Vib, 2017, 387: 96–113

    Google Scholar 

  50. Lü H, Shangguan W B, Yu D. A unified approach for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties. Mech Syst Signal Process, 2017, 93: 281–298

    Google Scholar 

  51. Ramahi O M, Mittra R. Finite-element analysis of dielectric scatterers using the absorbing boundary condition. IEEE Trans Magn, 1989, 25: 3043–3045

    Google Scholar 

  52. Wang L, **ong C, Wang X, et al. A dimension-wise method and its improvement for multidisciplinary interval uncertainty analysis. Appl Math Model, 2018, 59: 680–695

    MathSciNet  Google Scholar 

  53. Wang L, **ong C, Hu J, et al. Sequential multidisciplinary design optimization and reliability analysis under interval uncertainty. Aerosp Sci Technol, 2018, 80: 508–519

    Google Scholar 

  54. Wang L, Liang J, Yang Y W, et al. Time-dependent reliability assessment of fatigue crack growth modeling based on perturbation series expansions and interval mathematics. Theor Appl Fract Mech, 2018, 95: 104–118

    Google Scholar 

Download references

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Correspondence to Chao Jiang.

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This work was supported by the National Science Fund for Distinguished Young Scholars (Grant No. 51725502) and the Major Program of National Science Foundation of China (Grant No. 51490662).

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Wang, Z., Jiang, C., Ni, B. et al. An interval finite element method for electromagnetic problems with spatially uncertain parameters. Sci. China Technol. Sci. 63, 25–43 (2020). https://doi.org/10.1007/s11431-019-9671-7

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  • DOI: https://doi.org/10.1007/s11431-019-9671-7

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