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September 12, 2025AerospaceOpen Access

Aerodynamic Design Optimization for Flying Wing Gliders Based on the Combination of Artificial Neural Networks and Genetic Algorithms

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Authors

DTDat T. TranKPKhiem Van PhamANAnh Tuan Nguyen

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Overview

This method combines artificial neural networks and genetic algorithms to enhance aerodynamic performance in flying wing gliders, indicating significant trade-offs.

Key Points

  • Optimal aerodynamic designs were generated for flying wing gliders, improving both flight endurance and range.
  • The RG15 airfoil design achieves a maximum glide ratio of 24.1 while maintaining a static margin of 5.1%.
  • Introducing a stability constraint resulted in high-performance configurations, demonstrating a trade-off between stability and performance.
  • The methodology harnesses artificial neural networks and genetic algorithms to simplify and enhance the design process.

Cite This Study

Tran et al. (2025) studied this question.

synapsesocial.com/papers/68d44b3031b076d99fa547dfhttps://doi.org/10.3390/aerospace12090818
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