Reduction of Random Variables in EMC Uncertainty Simulation Model
DOI:
https://doi.org/10.13052/2022.ACES.J.370903Keywords:
Electromagnetic Compatibility, Uncertainty Analysis Method, Dimensional Disaster, Sensitivity Analysis Method, Random VariableAbstract
To improve the reliability of simulation results, uncertainty analysis methods were developed in the Electromagnetic Compatibility (EMC) field. Random variables are used to describe random events. The more random variables you have, the less efficient the simulation is. Therefore, many high-accuracy methods have the problem of dimensional disaster, which means the calculation efficiency decreases exponentially with the increase of the number of random variables. A random variable reduction strategy based on sensitivity analysis method is proposed in this paper, so as to improve the computational efficiency of the global uncertainty analysis method.
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Y. Zhang, C. Liao, R. Huan, Y. Shang, and H. Zhou, “Analysis of nonuniform transmission lines with a perturbation technique in time domain,” IEEE Transactions on Electromagnetic Compatibility, vol. 62, no. 2, pp. 542-548, 2020.
P. Manfredi, D. Ginste, I. Stievano, D. De Zutter, and F. Canavero, “Stochastic transmission line analysis via polynomial chaos methods: An overview,” IEEE Electromagnetic Compatibility Magazine, vol. 6, no. 3, pp. 77-84, 2017.
P. Manfredi, D. Vande. Ginste, D. De Zutter, and F. G. Canavero, “Generalized decoupled polynomial chaos for nonlinear circuits with many random parameters,” IEEE Microwave & Wireless Components Letters, vol. 25, no. 8, pp. 505-507, 2015.
Z. Zhang, T. A. EI-Moselhy, I. M. Elfadel, and L. Daniel, “Stochastic testing method for transistor-level uncertainty quantification based on generalized polynomial chaos,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 32, no. 10, pp. 1533-1545, 2013.
S. A. Pignari, G. Spadacini, and F. Grassi, “Modeling field-to-wire coupling in random bundles of wires,” IEEE Electromagnetic Compatibility Magazine, vol. 6, no. 3, pp. 85-90, 2017.
H. Xie, J. F. Dawson, J. Yan, A. C. Marvin, and M. P. Robinson, “Numerical and analytical analysis of stochastic electromagnetic fields coupling to a printed circuit board trace,” IEEE Transactions on Electromagnetic Compatibility, vol. 62, no. 4, pp. 1128-1135, 2020.
Z. Fei, Y. Huang, J. Zhou, and Q. Xu, “Uncertainty quantification of crosstalk using stochastic reduced order models,” IEEE Transactions on Electromagnetic Compatibility, vol. 59, no. 1, pp. 228-239, 2016.
R. S. Edwards, A. C. Marvin, and S. J. Porter, “Uncertainty analyses in the finite difference time domain method,” IEEE Transactions on Electromagnetic Compatibility, vol. 52, no. 1, pp. 155-163, 2010.
T. Wang, Y. Gao. L. Gao, C. Liu, J. X. Wang, and Z. Y. An, “Statistical analysis of crosstalk for automotive wiring harness via the polynomial chaos method,” Journal of the Balkan Tribological Association, vol. 22, no. 2, pp. 1503-1517, 2016.
J. Bai, G. Zhang, D. Wang, A. P. Duffy, and L.Wang, “Performance comparison of the sgm and the scm in emc simulation,” IEEE Transactions on Electromagnetic Compatibility, vol. 58, no. 6, pp. 1739-1746, Apr. 2016.
J. Bai, G. Zhang, A. P. Duffy, and L. Wang, “Dimension-reduced sparse grid strategy for a stochastic collocation method in emc software,” IEEE Transactions on Electromagnetic Compatibility, vol. 60, no. 1, pp. 218-224, 2018.
D. Xiu, E. Karniadakis, and George, “The wiener-askey polynomial chaos for stochastic differential equations,” SIAM Journal on Scientific Computing, vol. 24, no. 2, pp. 619-644, 2002.
J. Bai, L. Wang, D. Wang, A. P. Duffy, and G. Zhang, “Validity evaluation of the uncertain emc simulation results,” IEEE Transactions on Electromagnetic Compatibility, vol. 59, no. 3, pp. 797-804, Jun. 2017.