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Considering uncertainties in the design process is one of the most important factors to achieve reasonable and reliable results. In this article, a collaborative structure, which is a multidisciplinary design optimization, is combined with a robust design approach to design an optimum and robust launch vehicle, while considering the effects of uncertainties. First, a liquid-fuel vehicle is designed under two disciplines to send a 1200 kg mass to the 750 km orbit from the earth surface with 50.7◦ orbital inclination, using the collaborative structure. It should be said that the first discipline includes three subsystems that are engine design, geometry design and estimating the mass. Also, the second discipline includes three subsystems that are pitch program, aerodynamic calculations and trajectory simulation. Then, the optimum collaborative output is combined with the robust design in a multi-objective model to achieve the final vehicle configuration. The results show that the calculated mass of the first stage of the project using the collaborative robust design process is 3 tons heavier than the calculated mass using optimum collaborative design approach and the engines working time is increased. The overall size of the launch vehicle is increased too. The outputs of each subsystem have been evaluated and also, the overall results have been compared with another design process, i.e. MDF. This comparison shows the acceptable accuracy of the proposed approach.

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In this paper, conceptual design of a General Aviation Aircraft (GAA) is explained as a multi-objective Multidisciplinary Design Optimization (MDO). In the early sizing phase, preliminary aircraft configuration is defined based on a predetermined requirements and statistical Study. Afterwards, conceptual design disciplines are developed and integrated based on Multidisciplinary Design Feasibility (MDF) structure to improve the aircraft performance. The MDF loop is established by implementing a multidisciplinary analysis which includes disciplines as engine selection, weight and sizing, aerodynamics, performance and stability. In this design process, Constraints and algorithms are considered based on the Gudmundsson design approach. Design variables are selected carefully using sensitivity analysis on design objectives (i.e. reducing the weight and increasing the range). In order to obtain a feasible design, static stability constraints are considered. The NSGA-II multi-objective evolutionary optimization algorithm is utilized to demonstrate a set of possible answers in the form of the Pareto front. By selecting different engines and illustrating the Pareto fronts resulted from optimization process, the feasibility and effectiveness of rapid GAA conceptual design is demonstrated.