Modeling of UH-60A Hub Accelerations with Neural Networks

Abstract

Neural network relationships between the full-scale, flight test hub accelerations and the corresponding three N/rev pilot floor vibration components (vertical, lateral, and longitudinal) are studied. The present quantitative effort on the UH-60A Black Hawk hub accelerations considers the lateral and longitudinal vibrations. An earlier study had considered the vertical vibration. The NASA/Army UH-60A Airloads Program flight test database is used. A physics based "maneuver-effect- factor (MEF)," derived using the roll-angle and the pitch-rate, is used. Fundamentally, the lateral vibration data show high vibration levels (up to 0.3 g's) at low airspeeds (for example, during landing flares) and at high airspeeds (for example, during turns). The results show that the advance ratio and the gross weight together can predict the vertical and the longitudinal vibration. However, the advance ratio and the gross weight together cannot predict the lateral vibration. The hub accelerations and the advance ratio can be used to satisfactorily predict the vertical, lateral, and longitudinal vibration. The present study shows that neural network based representations of all three UH-60A pilot floor vibration components (vertical, lateral, and longitudinal) can be obtained using the hub accelerations along with the gross weight and the advance ratio. The hub accelerations are clearly a factor in determining the pilot vibration. The present conclusions potentially allow for the identification of neural network relationships between the experimental hub accelerations obtained from wind tunnel testing and the experimental pilot vibration data obtained from flight testing. A successful establishment of the above neural network based link between the wind tunnel hub accelerations and the flight test vibration data can increase the value of wind tunnel testing.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA480581

Entities

People

  • Sesi Kottapalli

Organizations

  • National Aeronautics and Space Administration

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accelerometers
  • Acoustics
  • Airspeed
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Databases
  • Flight
  • Flight Testing
  • Fuselages
  • Helicopters
  • Iterations
  • Level Flight
  • Neural Networks
  • Physics
  • Test And Evaluation
  • Tilt Rotor Aircraft
  • Wind Tunnels

Readers

  • Aerospace Engineering
  • Control Systems Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks