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Author:

Zhu, Xiaojie (Zhu, Xiaojie.) | Ma, Xikui (Ma, Xikui.) | Shao, Jinghui (Shao, Jinghui.) | Yin, Shuli (Yin, Shuli.)

Indexed by:

SCIE EI

Abstract:

A novel numerical method, referred to as the Riccati precise-integration time-domain (Riccati-PITD) method, is proposed to reduce the memory requirement of the conventional PITD method. In the Riccati-PITD method, the electromagnetic field components of the transverse electric/transverse magnetic (TE/TM) wave are arranged in a matrix for constructing the Riccati matrix differential equations (RDEs) about Maxwell's curl equations. Then the precise integration technique is adopted for the numerical integration of the RDEs. Theoretical analyses about the memory requirements of the conventional and the Riccati-PITD methods are presented. It is found that the memory requirement can be more greatly reduced by solving the electromagnetic-field components in the form of the matrices in the Riccati-PITD method rather than in the form of the vectors in the conventional PITD method. Numerical examples are also given to confirm that the proposed method has significant advantages over the conventional PITD method with respect to the execution time and the memory requirement.

Keyword:

Finite difference methods memory requirement precise-integration time-domain (PITD) method Riccati matrix differential equations (RDEs)

Author Community:

  • [ 1 ] [Zhu, Xiaojie; Ma, Xikui; Shao, Jinghui; Yin, Shuli] Xi An Jiao Tong Univ, Sch Elect Engn, State Key Lab Elect Insulat & Power Equipment, Xian 710049, Peoples R China

Reprint Author's Address:

  • [Zhu, Xiaojie]State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi'an; 710049, China;;

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Source :

IEEE TRANSACTIONS ON MAGNETICS

ISSN: 0018-9464

Year: 2020

Issue: 2

Volume: 56

1 . 7

JCR@2020

1 . 7 0 0

JCR@2020

ESI Discipline: PHYSICS;

ESI HC Threshold:54

JCR Journal Grade:2

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count: 3

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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