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

Daisuke Sasaki, Shigeru Obayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)


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.

Original languageEnglish
Title of host publication16th AIAA Computational Fluid Dynamics Conference
PublisherAmerican Institute of Aeronautics and Astronautics Inc.
ISBN (Print)9781624100864
Publication statusPublished - 2003
Externally publishedYes
Event16th AIAA Computational Fluid Dynamics Conference 2003 - Orlando, FL, United States
Duration: 2003 Jun 232003 Jun 26

Publication series

Name16th AIAA Computational Fluid Dynamics Conference


Other16th AIAA Computational Fluid Dynamics Conference 2003
Country/TerritoryUnited States
CityOrlando, FL

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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