Different approaches for the optimization of a hybrid rocket engine, which is used as upper stage of a small launcher, are compared in terms of solution accuracy and computational time. High-fidelity approaches, which employ an evolutionary algorithm to optimize the engine design and an indirect optimization method for the ascent trajectory, provide the globally optimum design, but require long computational times. Low-fidelity approaches, which only carry out the evolutionary optimization of the engine design and use a suboptimal control law for the trajectory, exhibit remarkably shorter computational times, but only provide a suboptimal design; estimations of optimum performance and optimal design parameters are obtained with errors that reach values about 10 %. Nevertheless low-fidelity approaches are appealing to explore widely the solution space: interesting sub-optimal solutions can be identified that can also be used as a guess for high-fidelity approaches.
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