Low-boom design optimization for SST canard-wing-fuselage configuration

Daisuke Sasaki, Shigeru Obayashi

研究成果: Conference contribution

8 被引用数 (Scopus)

抄録

In this paper, the performance of Adaptive Range Multi-Objective Genetic Algorithm (ARMOGA), which has been developed for reducing a number of function evaluations, is examined by using three analytical test problems. These test problems are also solved by a widely-used Multi-Objective Evolutionary Algorithm (MOEA), NSGA2, and two gradient-based methods, Sequential Quadratic Programming (SQP) and Dynamic Hill Climber (DHC) for comparison. ARMOGA is found to locate a Pareto front with a small number of function evaluations comparable to DHC. To utilize the present ARMOGA, an automated design system of low-boom Supersonic Transport (SST) configuration has been developed. To reduce the sonic boom for supersonic flight effectively with minimizing the drag, SST wing-fuselage configurations equipped with a canard are considered. The resulting system automatically generates unstructured grids around SST canard-wing-fuselage configuration.

本文言語English
ホスト出版物のタイトル16th AIAA Computational Fluid Dynamics Conference
出版社American Institute of Aeronautics and Astronautics Inc.
ISBN(印刷版)9781624100864
DOI
出版ステータスPublished - 2003
イベント16th AIAA Computational Fluid Dynamics Conference 2003 - Orlando, FL, United States
継続期間: 2003 6 232003 6 26

出版物シリーズ

名前16th AIAA Computational Fluid Dynamics Conference

Other

Other16th AIAA Computational Fluid Dynamics Conference 2003
CountryUnited States
CityOrlando, FL
Period03/6/2303/6/26

ASJC Scopus subject areas

  • Fluid Flow and Transfer Processes
  • Energy Engineering and Power Technology
  • Engineering (miscellaneous)
  • Aerospace Engineering
  • Automotive Engineering
  • Mechanical Engineering

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