High-Fidelity Multidisciplinary Design Optimization of Aerostructural Wing Shape for Regional Jet

Abstract

A large-scale, real-world application of Evolutionary Multi-Objective Optimization is reported. The Multidisciplinary Design Optimization among aerodynamics, structures, and aeroelasticity of the wing of a transonic regional jet aircraft was performed using highfidelity evaluation models. Euler and Navier-Stokes solvers were employed for aerodynamic evaluation. The commercial software NASTRAN was coupled with a Computational Fluid Dynamics solver for the structural and aeroelastic evaluations. Adaptive Range MultiObjective Genetic Algorithm was employed as an optimizer. The objective functions were minimizations of block fuel and maximum takeoff weight in addition to drag divergence between transonic and subsonic flight conditions. As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives. Moreover, all solutions evaluated during the evolution were analyzed using a Self-Organizing Map as a Data Mining technique to extract key features of the design space. One of the key features found by Data Mining was the non-gull wing geometry, although the present MDO results showed the reverse-gull wings as non-dominated solutions. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance and to achieve 3.6 percent improvement in the block fuel compared to the original geometry designed in the conventional manner.

Multi-Objective Design Exploration and Its Application to Regional-Jet Wing Design

“…[6][7][8][9] EAs have been also applied to single-objective and multi-objective aerospace design optimization problems. 2,[10][11][12][13] This approach to finding many Pareto solutions works fine as it is, however, only when the number of objectives remains small (usually two, three at most, as shown in Fig. 3).…”

Multi-Objective Design Exploration for Aerodynamic Configurations

“…8. The fitness value (F i ) of individual i is assigned based on the following equation: (17) where N is the number of solutions, and μ(R i ) is the number of solutions in rank R i . Thereafter, the standard sharing approach is adopted to prevent choosing similar solutions as parents and to maintain diversity of the population.…”

1821 Discussion of Clustering and Network Inversion for Multi-Objective Genetic Algorithms

“…Recently, multi-objective genetic algorithms have been applied to real-world problems and the good results were derived [1,2]. However, in real-world problems, it usually takes a large amount of time to evaluate one parameter set.…”