Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization

AIAA Journal ◽  
2001 ◽  
Vol 39 (12) ◽  
pp. 2233-2241 ◽  
Author(s):  
Timothy W. Simpson ◽  
Timothy M. Mauery ◽  
John J. Korte ◽  
Farrokh Mistree
Author(s):  
Gregory Kott ◽  
Gary A. Gabriele ◽  
Jacob Korngold

Abstract This paper describes the application of multidisciplinary design optimization to the power stage design of a power converter. The decomposition of the power stage design into an electrical and a loss subsystem is developed. The Sequential Global Approximation method is the non-hierarchic algorithm used to optimize the power stage design problem. Results of the non-hierarchic formulation compared to the non-decomposed formulation show a decrease of 63 percent in total system iterations required to converge to the optimal solution. Local and global move limits of 28 percent were found to provide the best performance for this problem. The successful implementation and results of applying multidisciplinary design optimization to power stage design allows the extension of the research to incorporate other disciplines. Our goal is to include all disciplines to completely model the design of a power converter. The details of power stage design problem formulation are provided to be used as a test problem in multidisciplinary design optimization research.


Author(s):  
Gregory Kott ◽  
Gary A. Gabriele ◽  
Jacob Korngold

Abstract This paper describes the application of multidisciplinary design optimization to the power stage design of a power converter. Multidisciplinary design is used to integrate the electrical, loss, and thermal analyses into one system problem. The Sequential Global Approximation method, a non-hierarchic algorithm, is used to optimize the power stage design problem. The code used for the thermal analysis, COSMOS/M, runs externally to the Sequential Global Approximation algorithm. A comparison of the results of the non-hierarchic formulation and the non-decomposed formulation shows a 67 percent decrease in total system iterations and a 12 percent decrease in total finite element analyses required.


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