Knowledge discovery in aerodynamic design space for flyback-booster wing using data mining

The data mining has been performed for the aerodynamic design optimization result of two-stage-to-orbit reusable launch vehicle flyback booster wing. Three data mining techniques were used such as self-organizing map, functional analysis of variance, and rough set theory. The optimization problem had four aerodynamic objective functions and 71 design variables regarding wing shape. The optimization obtained the result as the hypothetical design database with 302 all solutions including the 102 non-dominated solutions. Consequently, the knowledge in the design space was acquired regarding the correlation between objective functions, and the influence of the design variables to the objective function, for non-dominated and all evaluated solutions, respectively. The features of three data mining techniques were revealed. Although the combination among three techniques discovered detailed design knowledge, self-organizing map was especially a key technique for knowledge discovery. Moreover, design knowledge from all solutions conserved the information from non-dominated solutions. Data mining was essential to solve multi-objective optimization problem.