Hybrid Optimization Framework with Proper-Orthogonal-Decomposition-Based Order Reduction and Design-Space Evolution Scheme

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Hybrid Optimization Framework with Proper-Orthogonal-Decomposition-Based Order Reduction and Design-Space Evolution Scheme

Ghoman, Satyajit S. / Wang, Zhicun / Chen, P. C. / Kapania, Rakesh K.

This paper describes the development of an innovative hybrid-optimization framework that employs a proper-orthogonal-decomposition-based design-space order-reduction scheme combined with the evolutionary-algorithm technique. The proper-orthogonal-decomposition method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction wherein the proper-orthogonal-decomposition coefficients act as the new shape design variables. The snapshot of the candidate population is updated iteratively using an evolutionary-algorithm technique of fitness-driven retention. Established with the goal of developing a robust optimization scheme, the developed strategy capitalizes on the advantages of the evolutionary algorithm as well as the proper-orthogonal-decomposition-based reduced-order modeling, while overcoming the shortcomings inherent with these techniques. When linked with a streamlined multidisciplinary optimization framework, this hybrid-optimization strategy offers a computationally efficient methodology for problems having a high level of complexity and a challenging design space with a large number of design variables. The developed framework is demonstrated for its robustness on a nonconventional supersonic tailless air-vehicle wing-shape optimization problem.