Convergence Determination of EMC Uncertainty Simulation Based on the Improved Mean Equivalent Area Method

Authors

  • Jinjun Bai College of Marine Electrical Engineering Dalian Maritime University, Dalian, 116026, China
  • Jingchao Sun Traction & Control State Key Lab CRRC Dalian R&D Co., Ltd, Dalian, 116052, China
  • Ning Wang College of Marine Electrical Engineering Dalian Maritime University, Dalian, 116026, China

DOI:

https://doi.org/10.13052/2021.ACES.J.361108

Keywords:

EMC Simulation, Uncertainty Analysis, Convergence Determination, improved Mean Equivalent Area Method, Stochastic Reduced Order Models

Abstract

Uncertainty analysis plays a significant role in electromagnetic compatibility (EMC) simulation, but suffers from convergence determination thereby reducing simulation accuracy and computational efficiency. In this paper, an improved mean equivalent area method is proposed to enhance calculation accuracy. It shows that, using a benchmark example, the proposed method successfully achieves the convergence determination of the stochastic reduced order models (SROMs), and realizes further promotion of uncertainty analysis method.

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Author Biographies

Jinjun Bai , College of Marine Electrical Engineering Dalian Maritime University, Dalian, 116026, China

Jinjun Bai received the B.Eng. degree in electrical engineering and automation in 2013, and Ph.D. degree in electrical engineering in 2019 from the Harbin Institute of Technology, Harbin, China. At present, Mr. Bai is 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.

Jingchao Sun, Traction & Control State Key Lab CRRC Dalian R&D Co., Ltd, Dalian, 116052, China

Jingchao Sun received the B.Eng. and M.Eng. degrees in electrical engineering from Dalian Maritime University, Dalian, China, in 2009 and 2012, respectively, where she is currently pursuing the Ph.D. degree. She is currently an Electrical Engineer with the Dalian Electric Traction Research and Development Center, China CNR Corporation Ltd., Dalian. Her current research interests include unmanned crafts and their intelligent modeling and control.

Ning Wang, College of Marine Electrical Engineering Dalian Maritime University, Dalian, 116026, China

Ning Wang received his B.Eng. degree in Marine Engineering and the Ph.D. degree in control theory and engineering from the Dalian Maritime University, Dalian, China in 2004 and 2009, respectively. From September 2008 to September 2009, he was financially supported by China Scholarship Council to work as a joint-training PhD student at the Nanyang Technological University (NTU), Singapore. In view of his significant research at NTU, he received the Excellent Government-funded Scholars and Students Award in 2009. From August 2014 to August 2015, he worked as a Visiting Scholar at the University of Texas at San Antonio. His research interests include self-learning modeling and control, unmanned (marine) vehicles, machine learning, and autonomous systems. Dr. Wang received the Nomination Award of Liaoning Province Excellent Doctoral Dissertation, and also won the State Oceanic Administration Outstanding Young Scientists in Marine Science and Technology, the China Ocean Engineering Science and Technology Award (First Prize), the Liaoning Province Award for Technological Invention (First Prize), the Liaoning Province Award for Natural Science (Second Prize), the Liaoning Youth Science and Technology Award (Top10 Talents), the Liaoning BaiQianWan Talents (First Level), the Liaoning Excellent Talents (First Level), the Science and Technology Talents the Ministry of Transport of the P. R. China, the Youth Science and Technology Award of China Institute of Navigation, and the Dalian Leading Talents. He has authored three books, and more than 100 SCI-indexed journal papers. He currently serves as a member of IEEE TC on Industrial Informatics, the American Society of Mechanical Engineers (ASME), the Society of Naval Architects and Marine Engineers (SNAME), the Chinese Association of Automation (CAA), and the China’s Society of Naval Architecture and Marine Engineering (CSNAME). He has been Leading Guest Editors of the International Journal of Fuzzy Systems, the International Journal of Advanced Robotics System, the Advances in Mechanical Engineering, and the International Journal of Vehicle Design. He currently serves as Editorial Board Members and Associate Editors of the International Journal of Fuzzy Systems, the Electronics (MDPI), the Journal of Electrical Engineering & Technology, the Cyber-Physical Systems (Taylor-Francis), the Frontiers in Robotics and AI, and the International Journal of Aerospace System Science and Engineering.

References

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Published

2021-12-30

How to Cite

[1]
J. . Bai, J. . Sun, and N. . Wang, “Convergence Determination of EMC Uncertainty Simulation Based on the Improved Mean Equivalent Area Method”, ACES Journal, vol. 36, no. 11, pp. 1446–1452, Dec. 2021.

Issue

2021: Vol 36 Iss 11

Section

General Submission