Efficient Design Optimization of Microwave Structures Using Adjoint Sensitivity

2014 ◽  
pp. 333-346
Author(s):  
Slawomir Koziel ◽  
Leifur Leifsson ◽  
Stanislav Ogurtsov
Keyword(s):  
Design Optimization ◽  
Efficient Design ◽  
Adjoint Sensitivity ◽  
Microwave Structures

Adaptive Complex Method for Efficient Design Optimization

2007 ◽  
Author(s):  
Marcus Pettersson ◽  
Johan O¨lvander
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Global Optimum ◽  
Direct Search ◽  
Complex Method ◽  
Efficient Design ◽  
Direct Search Methods ◽  
Simulation Based ◽  
Set Of Points ◽  
Quasi Newton

Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

scholarly journals Multi-Level Modeling Methodology for Optimal Design of Electric Machines Based on Multi-Disciplinary Design Optimization

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4173
Author(s):  
Zehua Dai ◽  
Li Wang ◽  
Lexuan Meng ◽  
Shanshui Yang ◽  
Ling Mao
Keyword(s):  
Power Systems ◽  
Design Optimization ◽  
Synchronous Generator ◽  
Electric Machines ◽  
Modeling Method ◽  
System Level ◽  
Superior Performance ◽  
Efficient Design ◽  
Modeling Methodology ◽  
Multi Level

The transportation sector is undergoing electrification to gain advantages such as lighter weight, improved reliability, and enhanced efficiency. As contributors to the safety of embedded critical functions in electrified systems, better sizing of electric machines in vehicles is required to reduce the cost, volume, and weight. Although the designs of machines are widely investigated, existing studies are mostly complicated and application-specific. To satisfy the multi-level design requirements of power systems, this study aims to develop an efficient modeling method of electric machines with a background of aircraft applications. A variable-speed variable-frequency (VSVF) electrically excited synchronous generator is selected as a case study to illustrate the modular multi-physics modeling process, in which weight and power loss are the major optimization goals. In addition, multi-disciplinary design optimization (MDO) methods are introduced to facilitate the optimal variable selection and simplified model establishment, which can be used for the system-level overall design. Several cases with industrial data are analyzed to demonstrate the effectiveness and superior performance of the modeling method. The results show that the proposed practices provide designers with accurate, fast, and systematic means to develop models for the efficient design of aircraft power systems.

Constraint Management of Reduced Representation Variables in Decomposition-Based Design Optimization

2011 ◽  
Vol 133 (10) ◽  
Author(s):  
Michael J. Alexander ◽  
James T. Allison ◽  
Panos Y. Papalambros ◽  
David J. Gorsich
Keyword(s):  
Design Optimization ◽  
Support Vector ◽  
Management Technique ◽  
Efficient Design ◽  
Reduced Representation ◽  
Constraint Management ◽  
Target Cascading ◽  
Simple Bounds ◽  
Domain Description ◽  
Reduced Representations

In decomposition-based design optimization strategies such as analytical target cascading (ATC), it is sometimes necessary to use reduced representations of highly discretized functional data exchanged among subproblems to enable efficient design optimization. However, the variables used by such reduced representation methods are often abstract, making it difficult to constrain them directly beyond simple bounds. This problem is usually addressed by implementing a penalty value-based heuristic that indirectly constrains the reduced representation variables. Although this approach is effective, it leads to many ATC iterations, which in turn yields an ill-conditioned optimization problem and an extensive runtime. To address these issues, this paper introduces a direct constraint management technique that augments the penalty value-based heuristic with constraints generated by support vector domain description (SVDD). A comparative ATC study between the existing and proposed constraint management methods involving electric vehicle design indicates that the SVDD augmentation is the most appropriate within decomposition-based design optimization.

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