Optimization methods for computer-aided design of artificial dielectric lens antennas

2010 ◽  
Author(s):  
Arij Farhat ◽  
Eddy Jehamy ◽  
Michel Ney
Keyword(s):  
Computer Aided Design ◽  
Optimization Methods ◽  
Lens Antennas ◽  
Artificial Dielectric ◽  
Computer Aided ◽  
Dielectric Lens ◽  
Aided Design

Comparison between Optimization Method and Experience-Based Method in Soil Conservation

2013 ◽  
Vol 726-731 ◽  
pp. 3811-3817
Author(s):  
Yuan Feng ◽  
Ji Xian Wang
Keyword(s):  
Slope Stability ◽  
Soil Conservation ◽  
Computer Aided Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Interaction Function ◽  
The Standard Model ◽  
Computer Aided ◽  
Slice Method ◽  
Aided Design

The analysis of the slope stability is important in soil conservation. To analyze the slope stability, optimization methods were coded and compared with the traditional experience-based methods. Furthermore, the results were visualized in the program, so that the user can easily check the results and can designate an area, in which the program seeks the center and radius of the most hazardous slide arc. Moreover, the graphic interaction function was implemented in the program. In addition, the Standard Model One, recommended by ACAD (The Association for Computer Aided Design), was calculated by the program, of which the results (safety factor Ks=0.95~0.96) were smaller than the official recommend value (Ks=1). It is because that the traditional slice method, which neglects the normal stress and shear stress between the slices, was applied for calculation of Ks.

scholarly journals Computer-Aided Design of Microfluidic Circuits

2020 ◽  
Vol 22 (1) ◽  
pp. 285-307 ◽  
Author(s):  
Elishai Ezra Tsur
Keyword(s):  
Computer Aided Design ◽  
Integrated Design ◽  
Optimization Methods ◽  
Performance Requirements ◽  
Computer Aided ◽  
Paper Based Microfluidics ◽  
Specification Synthesis ◽  
Description Languages ◽  
Aided Design

Microfluidic devices developed over the past decade feature greater intricacy, increased performance requirements, new materials, and innovative fabrication methods. Consequentially, new algorithmic and design approaches have been developed to introduce optimization and computer-aided design to microfluidic circuits: from conceptualization to specification, synthesis, realization, and refinement. The field includes the development of new description languages, optimization methods, benchmarks, and integrated design tools. Here, recent advancements are reviewed in the computer-aided design of flow-, droplet-, and paper-based microfluidics. A case study of the design of resistive microfluidic networks is discussed in detail. The review concludes with perspectives on the future of computer-aided microfluidics design, including the introduction of cloud computing, machine learning, new ideation processes, and hybrid optimization.

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