An Efficient EM Simulation Method for the Fast Design and Analysis of Large Antenna Arrays

2015 ◽  
Author(s):  
T. Wan ◽  
Y.L. Shi ◽  
Y.Q. Hu ◽  
T. Hong
Keyword(s):  
Antenna Arrays ◽  
Simulation Method ◽  
Em Simulation

Finite-element analysis of infinite and finite arrays

2012 ◽  
Vol 4 (3) ◽  
pp. 357-364 ◽  
Author(s):  
John B. Manges ◽  
John W. Silvestro ◽  
Kezhong Zhao
Keyword(s):  
Finite Element ◽  
Antenna Arrays ◽  
Domain Decomposition Method ◽  
Simulation Method ◽  
Element Analysis ◽  
Numerical Characterization ◽  
Infinite Array ◽  
Specialized Form ◽  
Transfinite Element ◽  
Element Method

This paper considers and compares the numerical characterization of regular planar antenna arrays from two viewpoints. In the case where the array is sufficiently large, the well-known infinite array idealization applies and a very efficient simulation method is presented which combines array theory with a specialized form of the finite-element method called the transfinite element method (TFEM). Alternatively, a more direct approach is discussed in which the entire antenna array is simulated as a finite structure using recent advances in the domain decomposition method (DDM). Taken together, the two methods provide a comprehensive simulation method for regular arrays from small order to very large order.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

New Simplified Low Cost Simulation Method for SME Manufacturing System

2015 ◽  
Vol 9 (2) ◽  
pp. 206
Author(s):  
Tawfik Benabdallah ◽  
Nor Nait Sadi ◽  
Mustapha Kamel Abdi
Keyword(s):  
Manufacturing System ◽  
Low Cost ◽  
Simulation Method ◽  
Cost Simulation
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