TY - GEN

T1 - Mixed Discretization of CFIE in the Framework of MLFMA

AU - Guler, S.

AU - Yücel, A. C.

AU - Bagci, Hakan

AU - Ergül, O.

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2019/3/8

Y1 - 2019/3/8

N2 - The conventional combined-field integral equation (CFIE)using a Galerkin scheme suffers from inaccuracy issues due to the incorrect testing of the identity operator in the magnetic-field integral equation (MFIE). In this contribution, a mixed discretization scheme is used for correct testing of MFIE in the context of CFIE. The projection of testing spaces of EFIE and MFIE onto each other is required while solving CFIE numerically with the mixed discretization scheme. For this purpose, computations of the Gram matrix inversions are required to perform the projection operations. Such an operation can easily become computationally expensive, especially when solving large-scale problems using accelerated algorithms, such as the multilevel fast multipole algorithm (MLFMA). In this work, matrix decomposition methods and iterative solvers are used to solve Gram systems while solving CFIE with the mixed discretization scheme in the framework of MLFMA. The accuracy and efficiency of the results are compared, in the context of large-scale problems.

AB - The conventional combined-field integral equation (CFIE)using a Galerkin scheme suffers from inaccuracy issues due to the incorrect testing of the identity operator in the magnetic-field integral equation (MFIE). In this contribution, a mixed discretization scheme is used for correct testing of MFIE in the context of CFIE. The projection of testing spaces of EFIE and MFIE onto each other is required while solving CFIE numerically with the mixed discretization scheme. For this purpose, computations of the Gram matrix inversions are required to perform the projection operations. Such an operation can easily become computationally expensive, especially when solving large-scale problems using accelerated algorithms, such as the multilevel fast multipole algorithm (MLFMA). In this work, matrix decomposition methods and iterative solvers are used to solve Gram systems while solving CFIE with the mixed discretization scheme in the framework of MLFMA. The accuracy and efficiency of the results are compared, in the context of large-scale problems.

UR - http://hdl.handle.net/10754/652942

UR - https://ieeexplore.ieee.org/document/8597735

UR - http://www.scopus.com/inward/record.url?scp=85060956312&partnerID=8YFLogxK

U2 - 10.23919/PIERS.2018.8597735

DO - 10.23919/PIERS.2018.8597735

M3 - Conference contribution

SN - 9784885523168

SP - 1500

EP - 1505

BT - 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -