Multidisciplinary design optimization of an aircraft by using knowledge-based systems

A new strategy for solving multidisciplinary design optimization problems is presented in this paper. The main idea of this approach is based on the use of designer experiences and attention to his/her preferences during design optimization which is implemented using a concept called the fuzzy preference function. Two important advantages of this approach are: (1) using the experiences of expert people during optimization and (2) transforming a constrained multiobjective design optimization problem into an unconstrained single-objective design optimization problem. The multidisciplinary design optimization of an unmanned aerial vehicle (UAV) is considered to show the performance of the proposed methodology. The optimization problem in this case study is a constrained two-objective problem (minimization of takeoff weight and drag of the cruise phase), and the genetic algorithm (GA) is utilized as the optimizer. Performance, weight, aerodynamics, center of gravity, trim and dynamic stability are the considered modules in the multidisciplinary analysis that are modeled using empirical and semiempirical equations. The optimization results show that the proposed strategy has been able to offer an optimal design where has higher performance relative to other methods from the point of view of objective functions, low computational cost and simplicity of implementation.