EMC Generation and Propagation in Embedded Grids with Multiple Converters

Author(s):  
Blazej Czerniewski ◽  
Jean-Luc. Schanen ◽  
Pericle Zanchetta
Keyword(s):  
Author(s):  
Tom I-P. Shih ◽  
Yu-Liang Lin ◽  
Andrew J. Flores ◽  
Mark A. Stephens ◽  
Mark J. Rimlinger ◽  
...  

Abstract A pre-processor was developed to assist CFD experts and non-experts in performing steady, three-dimensional Navier-Stokes analysis of a class of inlet-bleed problems involving oblique shock-wave/ boundary-layer interactions on a flat plate with bleed into a plenum through rows of circular holes. With this pre-processor, once geometry (e.g., hole dimensions and arrangement) and flow conditions (e.g., Mach number, boundary-layer thickness, incident shock location) are inputted, it will automatically generate every file needed to perform a CFD analysis from the grid system to initial and boundary conditions. This is accomplished by accessing a knowledge base established by experts who understand both CFD and the class of problems being analyzed. For experts in CFD, this tool greatly reduces the amount of time and effort needed to setup a problem for CFD analysis. It also provides experts with knobs to make changes to the setup if desired. For non-experts in CFD, this tool enables reliable and correct usage of CFD. A typical session on a workstation from data input to the generation of all files needed to perform a CFD analysis involves less than ten minutes. This pre-processor, referred to as AUTOMAT-V2, is an improved version of a code called AUTOMAT. Improvements made include: (1) multi-block structured grids can be patched in addition to being overlapped; (2) embedded grids can be introduced near bleed holes to reduce the number of grid points/cells needed by a factor of up to four; (3) grid systems generated allow up to three levels of multigrid; (4) CFL3D is supported in addition to OVERFLOW, two well-known and highly regarded Navier-Stokes solvers developed at NASA’s Langley and Ames Research Centers; (5) all files needed to run RONNIE for patched grids and MAGGIE for overlapped grids are also generated; and (6) more design parameters can be investigated including the study of micro bleed and effects of flow/hole misalignments.


One of the approaches to the problem of approximating functions with a singularity is the creation of an approximating apparatus based on splines with the same feature. For the wavelet decomposition of spline spaces it is important that the property of the embedding of these spaces is associated with embedding grids. The purpose of this paper is to consider ways of constructing spaces of splines with a predefined singularity and obtain their wavelet decomposition. Here the concept of generalized smoothness is used, within which the mentioned singularity is generalized smooth. This approach leads to the construction of a system of embedded spaces on embedded grids. A spline-wavelet decomposition of mentioned spaces is presented. Reconstruction formulas are done


2008 ◽  
Vol 47-50 ◽  
pp. 511-514 ◽  
Author(s):  
Adrian Bejan ◽  
Sylvie Lorente

Here we draw attention to the development of smart materials with embedded vasculatures that provide multiple functionality: volumetric cooling, self-healing, mechanical strength, etc. Vascularization is achieved by using tree-shaped (dendritic) and grid-shaped flow architectures. As length scales become smaller, dendritic vascularization provides dramatically superior volumetric bathing and transport properties than the use of bundles of parallel microchannels. Embedded grids of channels provide substantially better volumetric bathing when the channels have multiple diameters that are selected optimally and put in the right places. Two novel dendritic architectures are proposed: trees matched canopy to canopy, and trees that alternate with upside down trees. Both have optimized length scales and layouts. Flow architectures are derived from principle, in accordance with constructal theory, not by mimicking nature.


2021 ◽  
Vol 14 (3) ◽  
pp. 690-705
Author(s):  
D. Dewar ◽  
A. Formentini ◽  
K. Li ◽  
P. Zanchetta ◽  
P. Wheeler

1996 ◽  
Vol 127 (2) ◽  
pp. 412-423 ◽  
Author(s):  
A.G. Kravchenko ◽  
P. Moin ◽  
R. Moser

2010 ◽  
Vol 15 (5) ◽  
pp. 555-560
Author(s):  
Ling Li ◽  
Mingshun Yuan ◽  
Quanming Li

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