Soft Computing (14327643)24(16)pp. 12429-12448
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. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Journal of Aerospace Technology and Management (21759146)10
In this study, an efficient methodology is proposed for robust design optimization by using preference function and fuzzy logic concepts. In this method, the experience of experts is used as an important source of information during the design optimization process. The case study in this research is wing design optimization of Boeing 747. Optimization problem has two objective functions (wing weight and wing drag) so that they are transformed into new forms of objective functions based on fuzzy preference functions. Design constraints include transformation of fuel tank volume and lift coefficient into new constraints based on fuzzy preference function. The considered uncertainties are cruise velocity and altitude, which Monte Carlo simulation method is used for modeling them. The non-dominated sorting genetic algorithm is used as the optimization algorithm that can generate set of solutions as Pareto frontier. Ultimate distance concept is used for selecting the best solution among Pareto frontier. The results of the probabilistic analysis show that the obtained configuration is less sensitive to uncertainties. © 2018, Journal of Aerospace Technology and Management. All rights reserved.
Chinese Journal of Aeronautics (10009361)31(12)pp. 2248-2259
This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization (FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designer experiences during the design optimization process by fuzzy preference functions. In this study, two optimizations are done for Predator MQ-1 Unmanned Aerial Vehicle (UAV): (A) deterministic optimization and (B) robust optimization. In both problems, minimization of takeoff weight and drag is considered as objective functions, which have been optimized using Non-dominated Sorting Genetic Algorithm (NSGA). In the robust design optimization, cruise altitude and velocity are considered as uncertainties that are modeled by the Monte Carlo Simulation (MCS) method. Aerodynamics, stability and control, mass properties, performance, and center of gravity are used for multidisciplinary analysis. Robust design optimization results show 46% and 42% robustness improvement for takeoff weight and cruise drag relative to optimal design respectively. © 2018 Chinese Society of Aeronautics and Astronautics
