Reduction of Random Variables in EMC Uncertainty Simulation Model

作者

  • Jinjun Bai Department of Electrical Engineering, Dalian Maritime University, Dalian, 116026, China
  • Yixuan Wan Department of Electrical Engineering, Dalian Maritime University, Dalian, 116026, China
  • Ming Li Aviation Industry Corporation of China (AVIC), AVIC Aero Polytechnol Estab, Beijing 100028, Peoples R China
  • Gang Zhang Harbin Inst Technol, Sch Elect Engn & Automat, Harbin Institute of Technology,Harbin 150001, Peoples R China
  • Xin He Harbin Inst Technol, Sch Elect Engn & Automat, Harbin Institute of Technology,Harbin 150001, Peoples R China

##plugins.pubIds.doi.readerDisplayName##:

https://doi.org/10.13052/2022.ACES.J.370903

关键词:

Electromagnetic Compatibility, Uncertainty Analysis Method, Dimensional Disaster, Sensitivity Analysis Method, Random Variable

摘要

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.

##plugins.generic.usageStats.downloads##

##plugins.generic.usageStats.noStats##

##submission.authorBiographies##

##submission.authorWithAffiliation##

Jinjun Bai received his B.Eng. degree in Electrical Engineering and Automation in 2013, and a Ph.D. degree in electrical engineering in 2019 from the Harbin Institute of Technology, Harbin, China.

He is now a lecturer at Dalian Maritime University. His research interests include uncertainty analysis methods in EMC simulation, EMC problem of electric vehicles, and the validation of CEM.

##submission.authorWithAffiliation##

Yixuan Wan is working toward a Master’s Degree in Electrical Engineering. Her current research on simulation of electromagnetic radiation is related to electric vehicles. She is now engaged in electric vehicle cable harness crosstalk simulation.

##submission.authorWithAffiliation##

Ming Li was born in Tai’an, China, in 1982. He received B.Sc. and Ph.D.degrees from Beihang University, Beijng, China, in 2004 and 2010, respectively. He is currently a senior engineer at AVIC Aero Polytechnology Establishment, Beijing, China. His research interests include equipment environmental effects analysis and simulation.

##submission.authorWithAffiliation##

Gang Zhang was born in Tai’an, China, in 1984. He received a B.Sc. in Electrical Engineering from China University of Petroleum, Dongying, China, in 2007, and M.Sc. and Ph.D. degrees in Electrical Engineering from Harbin Institute of Technology (HIT), Harbin, China, in 2009 and 2014, respectively.

He is currently an Associate Professor in electrical engineering at Harbin Institute of Technology, Harbin, China, and a Visiting Professor at University of L’Aquila, L’Aquila, Italy. His research interests include electrical contact theory, uncertainty analysis of electromagnetic compatibility, and the validation of CEM.

##submission.authorWithAffiliation##

Xin Hex was born in Wenshan, China, in 1996. He received B.S. and M.Sc. degrees in Electrical Engineering from Harbin Institute of Technology, China, in 2019 and 2021, respectively, where he is currently pursuing a Ph.D. degree. His research interests include cable fault detection and location, and finite element simulation.

参考

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.

##submission.downloads##

已出版

2022-09-30

##submission.howToCite##

[1]
J. . Bai, Y. . Wan, M. . Li, G. . Zhang, 和 X. . He, 《Reduction of Random Variables in EMC Uncertainty Simulation Model》, ACES Journal, 卷 37, 期 09, 页 941–947, 9月 2022.

期

2022: Vol 37 Iss 9

栏目

General Submission

##category.category##