Model of Unsteady Aerodynamic Coefficients of a Delta Wing Aircraft at High Angles of Attack

Abstract

Studies on the subject of near-stall or post-stall flight have a direct impact on the evaluation of flight safety, on performance in terms of landing distance, and on increases in maneuverability. For several years, new flow control concepts have been studied, and some of them have proven effective in overcoming the difficulties (e.g., loss of control in yaw, asymmetry of forebody, roll instability) involving flight at high angles of attack. At high angles of attack, unsteady aerodynamic forces have to be taken into account since they can account for up to 30% of the maximum aerodynamic lift and can induce strong changes in flight stability. It is necessary to have a precise model of aerodynamic forces and moment to allow the design of efficient control laws and to evaluate the capabilities of a fighter in term of maneuverability. To develop such a model, a specific experimental data set is necessary. Several approaches for modeling the longitudinal aerodynamic coefficients of a fighter aircraft at high angles of attack, including the unsteady effects, are presented. One traditional approach has unsteady effects modeled by means of transfer functions. Another approach uses an internal variable descriptive of the flow field: the vortical state of the flow on the wing. Application of these approaches to modeling using a neural network is presented. (11 figures, 6 refs.)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2003
Accession Number
ADA419038

Entities

People

  • L. Planckaert

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Abstracts
  • Aerodynamic Forces
  • Aircrafts
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Control Surfaces
  • Delta Wings
  • Differential Equations
  • Flow
  • High Angles
  • Neural Networks
  • Recurrent Neural Networks
  • Rotation
  • Swept Wings
  • Test Facilities
  • Transfer Functions
  • Vehicles

Fields of Study

  • Physics

Readers

  • Aerospace Engineering
  • Computational Modeling and Simulation
  • Fluid Mechanics and Fluid Dynamics.

Technology Areas

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