Abstract

Multi-objective genetic algorithm (MOGA) and a data mining technique were applied to the multidisciplinary design optimization (MDO) of a hybrid rocket. In this study, a swirling-oxidizer-type hybrid rocket engine with a single cylindrical grain port was designed. MOGA was applied to solve the multi-objective problem using real-number cording and the Pareto ranking method. In this study, two objective functions were considered: one maximized the flight altitude and the other minimized the gross weight. Each objective function was empirically estimated. Many non-dominated solutions were obtained using MOGA, and a trade-off was observed between the two objective functions. To better understand the design problem, the MOGA results were visualized using a scatter plot matrix (SPM) and a data mining technique.

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