Aerodynamics of Low Aspect Ratio Wings at Low Reynolds Numbers with Applications to Micro Air Vehicle Design and Optimization

Abstract

The recent interest in the development of small UAVs and micro air vehicles has revealed a need for a more thorough understanding of the aerodynamics of small airplanes flying at low speeds. In response to this need, the present work provides a comprehensive study of the lift, drag, and pitching moment characteristics of wings of low aspect ratio operating at low Reynolds numbers. Wind tunnel tests of wings with aspect ratios between 0.5 and 2.0 and with four distinct wing planforms have been conducted at chord-Reynolds numbers in the range of 70,000 to 200,000. In addition, the effect of leading edge shape and fuselage bodies has been studied. As an example of an application of this wind tunnel data, the experimental results are used as part of an aerodynamic analysis procedure. This procedure is incorporated into a genetic algorithm optimization program that generates optimum MAV configurations given certain requirements and constraints. Results obtained by use of this optimization procedure have revealed that useful and accurate design-optimization tools can be developed based on the experimental data presented within this report.

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Document Details

Document Type
Technical Report
Publication Date
Nov 30, 2001
Accession Number
ADA397533

Entities

People

  • Gabriel E. Torres
  • Thomas J. Mueller

Organizations

  • University of Notre Dame

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Aerodynamic Characteristics
  • Aerodynamic Configurations
  • Aircrafts
  • Airfoils
  • Airframes
  • Airplanes
  • Algorithms
  • Aspect Ratio
  • Control Systems
  • Fixed Wing Aircraft
  • Genetic Algorithms
  • Geometry
  • Horizontal Stabilizers
  • Micro Air Vehicles
  • Unmanned Aerial Vehicles
  • Vehicle Design
  • Wind Tunnels

Fields of Study

  • Physics

Readers

  • Aerial Unmanned Vehicle Swarm Micro Periodontal Dentistry.
  • Aerodynamics/Aeronautics.
  • Computational Fluid Dynamics (CFD)

Technology Areas

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