High-fidelity multidisciplinary design optimization of aerostructural wing shape for regional jet

Kazuhisa Chiba, Shigeru Obayashi, Kazuhiro Nakahashi, Hiroyuki Morino

Research output: Contribution to journalConference articlepeer-review

12 Citations (Scopus)

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. 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 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 inverted 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.

Original languageEnglish
Pages (from-to)1012-1035
Number of pages24
JournalCollection of Technical Papers - AIAA Applied Aerodynamics Conference
Volume2
DOIs
Publication statusPublished - 2005
Event23rd AIAA Applied Aerodynamics Conference - Toronto, ON, Canada
Duration: 2005 Jun 62005 Jun 9

ASJC Scopus subject areas

  • Engineering(all)

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