Parallel PWTD-Accelerated Explicit Solution of the Time Domain Electric Field Volume Integral Equation
KAUST DepartmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2016-03-25
Print Publication Date2016-06
Permanent link to this recordhttp://hdl.handle.net/10754/604702
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AbstractA parallel plane-wave time-domain (PWTD)-accelerated explicit marching-on-in-time (MOT) scheme for solving the time domain electric field volume integral equation (TD-EFVIE) is presented. The proposed scheme leverages pulse functions and Lagrange polynomials to spatially and temporally discretize the electric flux density induced throughout the scatterers, and a finite difference scheme to compute the electric fields from the Hertz electric vector potentials radiated by the flux density. The flux density is explicitly updated during time marching by a predictor-corrector (PC) scheme and the vector potentials are efficiently computed by a scalar PWTD scheme. The memory requirement and computational complexity of the resulting explicit PWTD-PC-EFVIE solver scale as ( log ) s s O N N and ( ) s t O N N , respectively. Here, s N is the number of spatial basis functions and t N is the number of time steps. A scalable parallelization of the proposed MOT scheme on distributed- memory CPU clusters is described. The efficiency, accuracy, and applicability of the resulting (parallelized) PWTD-PC-EFVIE solver are demonstrated via its application to the analysis of transient electromagnetic wave interactions on canonical and real-life scatterers represented with up to 25 million spatial discretization elements.
CitationParallel PWTD-Accelerated Explicit Solution of the Time Domain Electric Field Volume Integral Equation 2016:1 IEEE Transactions on Antennas and Propagation
SponsorsThis work was supported in part by the AFOSR/NSSEFF Program under Award FA9550-10-1-0180 and the National Science Foundation (NSF) under Grant CCF 1116082.