A. Jafarsalehi؛ M. Mirshams؛ R. Emami
دوره 7، شماره 1 ، فروردین 1393، ، صفحه 1-12
چکیده
This paper focuses upon the development of an efficient method for conceptual design optimization of a satellite. There are many option for a satellite subsystems that could be choice, as acceptable solution to implement of a space system mission. Every option should be assessment based on the different criteria such as cost, mass, reliability and technology contraint (complexity). In this research, mass and technology constraints, which have a direct impact on the satellite life cycle cost, are considerd as system level objective function to obtain the system optimal solution during the coceptual ...
بیشتر
This paper focuses upon the development of an efficient method for conceptual design optimization of a satellite. There are many option for a satellite subsystems that could be choice, as acceptable solution to implement of a space system mission. Every option should be assessment based on the different criteria such as cost, mass, reliability and technology contraint (complexity). In this research, mass and technology constraints, which have a direct impact on the satellite life cycle cost, are considerd as system level objective function to obtain the system optimal solution during the coceptual design phase. The approach adopted in this paper is based on a distributed collaborative optimization (CO) framework. At system level, multiobjective optimization goal is to minimize the dry mass of the satellite and, simultaneously, minimize the system technology complexity which is subject to equality constraints. The use of equality constraints at the system level in CO to represent the disciplinary feasible regions, introduces numerical and computational difficulties as the discipline level optima are non-smooth and noisy functions of the system level optimization parameters.To address these difficulties robust optimization algorithms such as genetic algorithms (GA) are used at the system level. The results show that the CO framework has the same level of accuracy as the conventional All-At-Once approaches.
