Abstract
Multidisciplinary design optimization of solid-fueled multistage space launch vehicles requires complex highdimensional simulation models. To improve on the ability to efficiently explore and approximate large subspaces of these models, this research develops a new set of experimental designs for metamodeling with support vector regression. We propose to Latinize and improve the orthogonality of Hammersley sequence sampling for creating nearly orthogonal and excellent space-filling designs. Multiple measures are used to assess the quality of designed experiments. The designs are used to create metamodels of trajectory simulation of space launch vehicles using support vector regression. A hybrid genetic algorithm uses metamodels to identify minimum-launch-weight space launch vehicles. This approach resulted in an overall rapid and efficient design optimization scheme.
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