An Optimisation Procedure for the Conceptual Analysis of Different Aerodynamic Configurations

Abstract

This paper addresses the problem to define a methodology for the analysis of the performances of different aircraft configurations in the phase of conceptual design. The proposed approach is based on a numerical optimization procedure where a scalar objective junction, the take-off weight, is minimized. The optimization algorithm has obviously important consequences both from the point of view of the computational times and of the obtained results. For this reason a preliminary discussion is made where various different methodologies are critically compared. Although the best compromise between different approaches is probably given by an integration between a genetic algorithm approach and a classical gradient method, in this phase only the latter procedure has been used to perform the simulations. The methodology takes into account the high number of geometrical parameters and the flight mechanics, requirements involved in the problem. A basic example is described, and the use of the proposed methodology to investigate the effects of different geometrical and technological parameters is discussed.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2000
Accession Number
ADP010504

Entities

People

  • F. Beux
  • G. Lombardi
  • G. Mengali

Organizations

  • University of Pisa

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Aerodynamic Characteristics
  • Aerodynamic Configurations
  • Aircraft Equipment
  • Aircrafts
  • Airframes
  • Algorithms
  • Aspect Ratio
  • Computational Fluid Dynamics
  • Computational Science
  • Evolutionary Algorithms
  • Fluid Dynamics
  • Fuel Consumption
  • Genetic Algorithms
  • Geometry
  • Mathematical Models
  • Mechanics
  • Optimization

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Biotechnology