scholarly journals Bistatic scattering from three-dimensional target on perfectly conducting rough surface by using G-SMFSIA/CAG

2011 ◽  
Vol 60 (1) ◽  
pp. 010301
Author(s):  
Ji Wei-Jie ◽  
Tong Chuang-Ming
Keyword(s):  
Rough Surface ◽  
Three Dimensional ◽  
Bistatic Scattering

Reliable approach for bistatic scattering of three-dimensional targets from underlying rough surface based on parabolic equation

2018 ◽  
Vol 27 (7) ◽  
pp. 074102
Author(s):  
Dong-Min Zhang ◽  
Cheng Liao ◽  
Liang Zhou ◽  
Xiao-Chuan Deng ◽  
Ju Feng
Keyword(s):  
Parabolic Equation ◽  
Rough Surface ◽  
Three Dimensional ◽  
Bistatic Scattering

Reduced Rough-Surface Parametrization for Use With the Discrete-Element Model

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
Stephen T. McClain ◽  
Jason M. Brown
Keyword(s):  
Heat Transfer ◽  
Rough Surface ◽  
Surface Characterization ◽  
Rough Surfaces ◽  
Roughness Element ◽  
Three Dimensional ◽  
Element Model ◽  
Discrete Element ◽  
Diameter Distribution ◽  
Discrete Element Model

The discrete-element model for flows over rough surfaces was recently modified to predict drag and heat transfer for flow over randomly rough surfaces. However, the current form of the discrete-element model requires a blockage fraction and a roughness-element diameter distribution as a function of height to predict the drag and heat transfer of flow over a randomly rough surface. The requirement for a roughness-element diameter distribution at each height from the reference elevation has hindered the usefulness of the discrete-element model and inhibited its incorporation into a computational fluid dynamics (CFD) solver. To incorporate the discrete-element model into a CFD solver and to enable the discrete-element model to become a more useful engineering tool, the randomly rough surface characterization must be simplified. Methods for determining characteristic diameters for drag and heat transfer using complete three-dimensional surface measurements are presented. Drag and heat transfer predictions made using the model simplifications are compared to predictions made using the complete surface characterization and to experimental measurements for two randomly rough surfaces. Methods to use statistical surface information, as opposed to the complete three-dimensional surface measurements, to evaluate the characteristic dimensions of the roughness are also explored.

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