FAST- Full Airframe Sensing Technology for Hypersonic Aerodynamics Measurements

Abstract

We propose a consortium to develop a new paradigm in aerodynamic sensing - Full Airframe Sensing Technology or FAST -which holds promise for providing novel measurements of the distributed aerodynamic loads over a vehicle during ground or flight test. The FAST concept exploits the fact that the aerodynamic forces acting on the vehicle deform its shape, and thus information about the aerodynamic state is encoded in the shape of the airframe. If the shape or deformation is inferred from a set of distributed sensors, then it is theoretically possible to determine the loads that caused the deformation. We propose an innovative solution to this inverse problem, by which we exploit tools from scientific machine learning to speed up processing.

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 07, 2023
Source ID
FA95502110089

Entities

People

  • Noel Clemens

Organizations

  • Air Force Office of Scientific Research
  • United States Air Force
  • University of Texas at Austin

Tags

Readers

  • Aerodynamics/Aeronautics.
  • Image Processing and Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Hypersonics