Using Cyber-Physical systems to study the dynamics of unsteady leading edge vortices with cross-stream flows

Abstract

Leading Edge Vortex (LEV) formation and shedding from a wing undergoing unsteady motions remains an issue of central importance in aerodynamics and of relevance to structural design of flight vehicles, flight stability and control. In addition to influencing aerodynamic force production, the LEV affects the onset and character of aeroelastic instabilities associated with the fluid interacting with the flexible wing. Both the LEV dynamics and the details of the aeroelastic instabilities are sensitive to many parameters, including Reynolds number, stiffness, damping, and wing planform; thus far, scaling laws for LEV formation and instability have been difficult to formulate.The work performed under this proposal is to develop technology for both military and civil application. The proposed work will focus on the LEV dynamics and aeroelastic instabilities associated with swept wings—wings in which there is a significant cross flow velocity. An experimental approach will be used to study LEVs on swept wings free to pitch and heave. Experimental work using Cyber-Physical systems will be conducted at Brown University using Particle Image Velocimetry (PIV) to characterize and track the LEV formation and trajectory. The work will be conducted in collaboration with researchers at the US Air Force Academy, who will conduct related experiments and numerical simulations. Advanced data analysis techniques employing Dynamic Mode Decomposition and machine learning techniques will be used to assistin model development and validation of numerical simulations.

Document Details

Document Type
DoD Grant Award
Publication Date
Jun 11, 2018
Source ID
FA95501810322

Entities

People

  • Kenneth S. Breuer

Organizations

  • Air Force Office of Scientific Research
  • Brown University
  • United States Air Force

Tags

Fields of Study

  • Physics

Readers

  • Aerodynamics/Aeronautics.
  • Fluid Mechanics and Fluid Dynamics.

Technology Areas

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