scholarly journals Near-Field to Far-Field RCS Prediction on Arbitrary Scanning Surfaces Based on Spherical Wave Expansion

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7199
Author(s):  
Woobin Kim ◽  
Hyeong-Rae Im ◽  
Yeong-Hoon Noh ◽  
Ic-Pyo Hong ◽  
Hyun-Sung Tae ◽  
...  
Keyword(s):  
Near Field ◽  
Linear Equations ◽  
Spherical Wave ◽  
Scattered Wave ◽  
Far Field ◽  
Field Region ◽  
System Of Linear Equations ◽  
Wave Expansion ◽  
Field Transformation ◽  
Spherical Wave Expansion

Near-field to far-field transformation (NFFFT) is a frequently-used method in antenna and radar cross section (RCS) measurements for various applications. For weapon systems, most measurements are captured in the near-field area in an anechoic chamber, considering the security requirements for the design process and high spatial costs of far-field measurements. As the theoretical RCS value is the power ratio of the scattered wave to the incident wave in the far-field region, a scattered wave measured in the near-field region needs to be converted into field values in the far-field region. Therefore, this paper proposes a near-field to far-field transformation algorithm based on spherical wave expansion for application in near-field RCS measurement systems. If the distance and angular coordinates of each measurement point are known, the spherical wave functions in an orthogonal relationship can be calculated. If each weight is assumed to be unknown, a system of linear equations as numerous as the number of samples measured in the near electric field can be generated. In this system of linear equations, each weight value can be calculated using the iterative least squares QR-factorization method. Based on this theory, the validity of the proposed NFFFT is verified for several scatterer types, frequencies and measurement distances.

Near-Field Far-Field Transformation Utilizing 2-D Plane-Wave Expansion for Monostatic RCS Extrapolation

2016 ◽  
Vol 15 ◽  
pp. 1971-1974 ◽  
Author(s):  
Shuntaro Omi ◽  
Toru Uno ◽  
Takuji Arima ◽  
Takao Fujii ◽  
Yujiro Kushiyama
Keyword(s):  
Plane Wave ◽  
Near Field ◽  
Far Field ◽  
Plane Wave Expansion ◽  
Wave Expansion ◽  
Field Transformation

Fast Stokesian dynamics

2019 ◽  
Vol 878 ◽  
pp. 544-597 ◽  
Author(s):  
Andrew M. Fiore ◽  
James W. Swan
Keyword(s):  
Saddle Point ◽  
Large Scale ◽  
Near Field ◽  
Linear Equations ◽  
Far Field ◽  
System Of Linear Equations ◽  
Stokesian Dynamics ◽  
Brownian Force ◽  
Saddle Point Matrix ◽  
Stochastic Forces

We present a new method for large scale dynamic simulation of colloidal particles with hydrodynamic interactions and Brownian forces, which we call fast Stokesian dynamics (FSD). The approach for modelling the hydrodynamic interactions between particles is based on the Stokesian dynamics (SD) algorithm (J. Fluid Mech., vol. 448, 2001, pp. 115–146), which decomposes the interactions into near-field (short-ranged, pairwise additive and diverging) and far-field (long-ranged many-body) contributions. In FSD, the standard system of linear equations for SD is reformulated using a single saddle point matrix. We show that this reformulation is generalizable to a host of particular simulation methods enabling the self-consistent inclusion of a wide range of constraints, geometries and physics in the SD simulation scheme. Importantly for fast, large scale simulations, we show that the saddle point equation is solved very efficiently by iterative methods for which novel preconditioners are derived. In contrast to existing approaches to accelerating SD algorithms, the FSD algorithm avoids explicit inversion of ill-conditioned hydrodynamic operators without adequate preconditioning, which drastically reduces computation time. Furthermore, the FSD formulation is combined with advanced sampling techniques in order to rapidly generate the stochastic forces required for Brownian motion. Specifically, we adopt the standard approach of decomposing the stochastic forces into near-field and far-field parts. The near-field Brownian force is readily computed using an iterative Krylov subspace method, for which a novel preconditioner is developed, while the far-field Brownian force is efficiently computed by linearly transforming those forces into a fluctuating velocity field, computed easily using the positively split Ewald approach (J. Chem. Phys., vol. 146, 2017, 124116). The resultant effect of this field on the particle motion is determined through solution of a system of linear equations using the same saddle point matrix used for deterministic calculations. Thus, this calculation is also very efficient. Additionally, application of the saddle point formulation to develop high-resolution hydrodynamic models from constrained collections of particles (similar to the immersed boundary method) is demonstrated and the convergence of such models is discussed in detail. Finally, an optimized graphics processing unit implementation of FSD for mono-disperse spherical particles is used to demonstrated performance and accuracy of dynamic simulations of $O(10^{5})$ particles, and an open source plugin for the HOOMD-blue suite of molecular dynamics software is included in the supplementary material.

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