Application of Micro Genetic Algorithms and Neural Networks for Airfoil Design Optimization

Abstract

Genetic algorithms are versatile optimization tools suitable for solving multi-disciplinary optimization problems in aerodynamics where the design parameters may exhibit multi- modal or non-smooth variations. However, the fitness evaluation phase of the algorithms casts a large overhead on the computational requirement and is particularly acute in aerodynamic problems where time-consuming CFD methods are needed for evaluating performance. Methods and strategies to improve the performance of basic genetic algorithms are important to enable the method to be useful for complicated three-dimensional or multi-disciplinary problems. Two such methods are studied in the present work: micro genetic algorithms and artificial neural networks. Both methods are applied to inverse and direct airfoil design problems and the resulting improvement in efficiency is noted and discussed.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2000
Accession Number
ADP010519

Entities

People

  • Daniel C. Tse
  • Louis Y. Chan

Organizations

  • National Research Council Canada

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Coding
  • Computational Fluid Dynamics
  • Computer Graphics
  • Computer Programming
  • Computer Science
  • Computers
  • Engineering
  • Fluid Dynamics
  • Genetic Algorithms
  • Mach Number
  • Neural Networks
  • Optimization
  • Pressure Distribution
  • Real Numbers
  • Test And Evaluation
  • Three Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Fluid Dynamics (CFD)

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Biotechnology