IEEE Transactions on Antennas and Propagation | Vol.65, Issue.1 | | Pages 401-404
An Impedance Transfer Function Formulation for Reduced-Order Macromodels of Subgridded Regions in FDTD
Recently, model order reduction was applied to the macromodel of a subgridded region in the method of finite-difference time domain (FDTD), where an admittance transfer function formulation was used to model materials with electric conductivity. In this communication, we develop an impedance transfer function formulation for the reduced-order macromodel of a subgridded region; this formulation may be used to model materials exhibiting magnetic conductivity. The two macromodel types may be used together, in a complementary fashion, to model fine-featured subgridded regions in FDTD that contain materials exhibiting both electric and magnetic conductivity. Additionally, this communication greatly extends the range of both the deterministic and the stochastic macromodeling techniques recently developed in FDTD, thus enabling the solution of a much wider range of problems in computational electromagnetics.
Original Text (This is the original text for your reference.)
An Impedance Transfer Function Formulation for Reduced-Order Macromodels of Subgridded Regions in FDTD
Recently, model order reduction was applied to the macromodel of a subgridded region in the method of finite-difference time domain (FDTD), where an admittance transfer function formulation was used to model materials with electric conductivity. In this communication, we develop an impedance transfer function formulation for the reduced-order macromodel of a subgridded region; this formulation may be used to model materials exhibiting magnetic conductivity. The two macromodel types may be used together, in a complementary fashion, to model fine-featured subgridded regions in FDTD that contain materials exhibiting both electric and magnetic conductivity. Additionally, this communication greatly extends the range of both the deterministic and the stochastic macromodeling techniques recently developed in FDTD, thus enabling the solution of a much wider range of problems in computational electromagnetics.
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