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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.

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Reliability-based design optimization (RBDO) has been used for optimizing engineering systems in presence of uncertainties in design variables, system parameters or both of them. RBDO involves reliability analysis, which requires a large amount of computational effort, especially in real-world application. To moderate this issue, a novel and efficient Surrogate-Assisted RBDO approach is proposed in this article. The computational intelligence and decomposition based RBDO procedures are combined to develop a fast RBDO method. This novel method is based on the artificial neural networks as a surrogate model and Sequential Optimization and Reliability Assessment (SORA) method as RBDO method. In SORA, the problem is decoupled into sequential deterministic optimization and reliability assessment. In order to improve the computational efficiency and extend the application of the original SORA method, an Augmented SORA (ASORA) method is proposed in this article. In developed method, A criterion is used for identification of inactive probabilistic constraints and refrain the satisfied constraints from reliability assessment to decrease computational costs associated with probabilistic constraints. Further, the variations of shifted vectors obtained for satisfied constraints are controlled to be exactly equal to zero for the next RBDO iteration. Several mathematical examples with different levels of complexity and a practical engineering example are solved and results are discussed to demonstrate efficiency and accuracy of the proposed methods.

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Deriving the accurate dynamic model of robots is pivotal for robot design, control, calibration, and fault detection. To derive an accurate dynamic model of robots, all the terms affecting the robotchr('39')s dynamics are necessary to be considered, and the dynamic parameters of the robot must be identified with appropriate physical insight. In this paper, first, the kinematics of the ARAS-Diamond spherical parallel robot, which has been developed for vitreoretinal ophthalmic surgery, are investigated, then by presenting a formulation based on the principle of virtual work, a linear form of robot dynamics is derived, and the obtained results are validated in SimMechanics environment. Furthermore, other terms affecting the robot dynamics are modeled, and by using the linear regression form of the robot dynamics with the required physical bounds on the parameters, the identification process is accomplished adopting the least-squares method with appropriate physical consistency. Finally, by using the criteria of the normalized root mean squared error (NRMSE) and using different trajectories, the accuracy of the identified dynamic parameters is evaluated. The experimental validation results demonstrate a good fitness for the actuator torques (about 75 percent),  and a positive mass matrix in the entire workspace, which allows us to design the common model-based controllers such as the computer torque method, for precise control of the robot in vitreoretinal ophthalmic surgery.