volume | PIER Journals

Abstract

In recent years, uncertainty analysis has become a hot topic in the field of Electromagnetic Compatibility (EMC), and non-intrusive uncertainty analysis methods have been widely applied due to their advantage of obtaining results without modifying the original solver. Among them, the Surrogate Model Method has attracted widespread attention from researchers in the field of EMC due to its high computational efficiency and resistance to the curse of dimensionality. However, the issue of determining the convergence of the surrogate models seriously affects the computational efficiency and convenience of this method in practical applications. To address this issue, a convergence determination method for uncertainty analysis surrogate models based on Mean Equivalent Area Method (MEAM) is proposed in this paper. The complete convergence time of the Surrogate Model Method can be accurately determined through iterative calculation by this method, and the effectiveness of the proposed method is verified by calculating parallel cable crosstalk prediction examples from published literature. Finally, based on the proposed convergence determination method, the real-time convergence determination problem of the Surrogate Model Method is also preliminarily discussed in this paper, and by establishing a polynomial relationship, the real-time convergence of the Surrogate Model Method can be roughly determined.

1. Zhang, Yin, Cheng Liao, Rui Huan, Yuping Shang, and Haijing Zhou, "Analysis of nonuniform transmission lines with a perturbation technique in time domain," IEEE Transactions on Electromagnetic Compatibility, Vol. 62, No. 2, 542-548, 2020. Google Scholar

2. Manfredi, Paolo, Dries Vande Ginste, Igor S. Stievano, Daniël De Zutter, and Flavio G. Canavero, "Stochastic transmission line analysis via polynomial chaos methods: An overview," IEEE Electromagnetic Compatibility Magazine, Vol. 6, No. 3, 77-84, Nov. 2017. Google Scholar

3. Manfredi, Paolo, Dries Vande Ginste, Daniël De Zutter, and Flavio G. Canavero, "Generalized decoupled polynomial chaos for nonlinear circuits with many random parameters," IEEE Microwave and Wireless Components Letters, Vol. 25, No. 8, 505-507, 2015. Google Scholar

4. Zhang, Zheng, Tarek A. El-Moselhy, Ibrahim M. Elfadel, and Luca 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, 1533-1545, 2013. Google Scholar

5. Chen, Joe, Salvador Portillo, Grant Heileman, Ghadeh Hadi, Rusmir Bilalic, Manel Martínez-Ramón, Sameer Hemmady, and Edl Schamiloglu, "Time-varying radiation impedance of microcontroller GPIO ports and their dependence on software instructions," IEEE Transactions on Electromagnetic Compatibility, Vol. 64, No. 4, 1147-1159, 2022. Google Scholar

6. Pignari, Sergio A., Giordano Spadacini, and Flavia Grassi, "Modeling field-to-wire coupling in random bundles of wires," IEEE Electromagnetic Compatibility Magazine, Vol. 6, No. 3, 85-90, Nov. 2017. Google Scholar

7. Wang, Tianhao, Yinhan Gao, Le Gao, Chang-Ying Liu, Juxian Wang, and Zhanyang An, "Statistical analysis of crosstalk for automotive wiring harness via polynomial chaos method," Journal of the Balkan Tribological Association, Vol. 22, No. 2, 1503-1517, 2016. Google Scholar

8. Fei, Zhouxiang, Yi Huang, Jiafeng Zhou, and Qian Xu, "Uncertainty quantification of crosstalk using stochastic reduced order models," IEEE Transactions on Electromagnetic Compatibility, Vol. 59, No. 1, 228-239, 2016. Google Scholar

9. Ren, Ziyan, Jiangang Ma, Yanli Qi, Dianhai Zhang, and Chang-Seop Koh, "Managing uncertainties of permanent magnet synchronous machine by adaptive Kriging assisted weight index Monte Carlo simulation method," IEEE Transactions on Energy Conversion, Vol. 35, No. 4, 2162-2169, 2020. Google Scholar

10. Trinchero, Riccardo, Mourad Larbi, Hakki M. Torun, Flavio G. Canavero, and Madhavan Swaminathan, "Machine learning and uncertainty quantification for surrogate models of integrated devices with a large number of parameters," IEEE Access, Vol. 7, No. 1, 4056-4066, 2018. Google Scholar

11. Cui, Chunfeng and Zheng Zhang, "High-dimensional uncertainty quantification of electronic and photonic IC with non-Gaussian correlated process variations," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 39, No. 8, 1649-1661, 2019. Google Scholar

12. Bai, Jinjun, Lixin Wang, Di Wang, Alistair P. Duffy, and Gang Zhang, "Validity evaluation of the uncertain EMC simulation results," IEEE Transactions on Electromagnetic Compatibility, Vol. 59, No. 3, 797-804, 2017. Google Scholar

13. Stievano, Igor S., Paolo Manfredi, and Flavio G. Canavero, "Stochastic analysis of multiconductor cables and interconnects," IEEE Transactions on Electromagnetic Compatibility, Vol. 53, No. 2, 501-507, 2011. Google Scholar

14. Bai, J., 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, 1739-1746, 2016. Google Scholar

15. Memon, Zain A., Riccardo Trinchero, Paolo Manfredi, Flavio Canavero, Igor S. Stievano, and Yanzhao Xie, "Machine learning for the uncertainty quantification of power networks," IEEE Letters on Electromagnetic Compatibility Practice and Applications, Vol. 2, No. 4, 138-141, 2020. Google Scholar

16. Yu, Z., Z. Qing, and M. Yan, "Application of chaos immune optimization RBF network in dynamic deformation prediction," Geodesy and Geodynamics, Vol. 32, No. 5, 53-57, 2012. Google Scholar

17. Xia, Liangqiong, Penghao Hu, Kunlong Ma, and Long Yang, "Research on measurement modeling of spherical joint rotation angle based on RBF-ELM network," IEEE Sensors Journal, Vol. 21, No. 20, 23118-23124, 2021. Google Scholar

Dr. Bing Hu

Dr. Yujia Song

Dr. Pengxiang Wang

Dr. Shining Lin

Dr. Jinjun Bai