Multidisciplinary Design Optimization and Data Mining for Transonic Regional-Jet Wing

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 high-fidelity 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. An adaptive range multi-objective 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 nondominated 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 nongull wing geometry, although the present multidisciplinary design optimization results showed the inverted gull wings as nondominated solutions. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance and to achieve 3.6% improvement in the block fuel compared to the original geometry designed in the conventional manner.

Design Rule Extraction Using Multiobjective Design Exploration

“…Further examples in the literature can be found in Obayashi et al [10] and Takenaka et al [17] who provided an overview of collaborative works for a Regional Jet design, Chiba et al [26] and Kumano et al [27] who gave an account of the MDO system development for the main wing, and Hatanaka et al [28] and Kumano et al [29] who described the MDO system for engine-airframe integration. Examples of winglet design were proposed by Takenaka et al [30] and integrated aeroelastic simulations were also performed in the works by Kumano et al [31] and Morino et al [32].…”

Multiobjective Evolutionary Algorithms in Aeronautical and Aerospace Engineering

“…The authors obtained 75 nondominated solutions on which a data mining method was applied, using ANOVA and SOM methods, in order to reduce them to a set containing only 24 solution from which the designer was able to select only one. 6) Chiba et al [18] addressed the MDO problem of a wing shape for a transonic regional-jet aircraft. In this case, three objective functions were minimized: a) block fuel for a required airplane's mission; b) maximum takeoff weight; and c) difference in the drag coefficient between transonic and subsonic flight conditions.…”

“…Another possible option for improving efficiency is to adopt knowledge extraction techniques and then reuse this information during the evolutionary search. Although such techniques have been normally used in an a posteriori manner (adopting self-organizing maps and ANOVA, as in [18], [125], [126], and [159]), it is also possible to use them as an a priori technique. For example, Gräning et al [55] successfully applied this type of approach to the single-objective optimization of 3-D turbine blade geometries.…”

Multidisciplinary optimization of an NLF forward swept wing in combination with aeroelastic tailoring using CFRP

“…Multidisciplinary wing optimizations for realistic aircraft configurations under consideration of static aeroelasticity have been shown for example by Piperni et al [10] for a large business jet and by Chiba et al [11] for a regional jet.…”