LEARNING TO FLY: USING DISTRIBUTED PRESSURE SENSING AND NETWORK STRATEGIES FOR CONTROL IN GUSTY ENVIRONMENTS
Abstract
Swept wing Air Force platforms such as Unmanned Combat Aerial Vehicle (UCAVs) have a particular challenge in maintaining speed and control in unsteady, or gusty, environments or when performing dramatic maneuvers. The flow around the wings of these vehicles is unsteady and inherently three-dimensional that can create ultimately destabilizing aerodynamic loads/moments. In order to address this problem, the present collaborative project will experimentally investigate the relationship between the aerodynamic loads/moments, the surface pressure distribution, and the surrounding vortex structures in the flow field on two-dimensional (2D) and three-dimensional (3D) wing models in gusty environments. In particular, this work will use recent advances in networking strategies to create mathematical network representations of a finite set of surface pressure taps and map the behaviour of signals from those pressure taps to the aerodynamic loads/moments on the wing models. Likewise, Lagrangian vortex identification methods will be used to discern significant features of the flow field, the behaviour of which will also be mapped to the loads/moments history on the wings using network strategies. With established networks that map surface pressure and/or vortex features to aerodynamic loads, work will begin on a robust interconnected network that links the two. This linked network representation would yield a new framework for designing flow control strategy, as it will be able to predict loads, moments, and surrounding flow structure from a finite set of instantaneous surface pressure measurements. This framework will make it possible for flow control algorithms to address the root causes of destabilizing aerodynamic loads/moments – the developing large-scale vortex structures – before potentially catastrophic effects ensue. Consequently, the outcomes of this work will efficiently improve the robustness and stability of the next generation of UCAVs.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2021
- Source ID
- FA95502010086
Entities
People
- David E. Rival
Organizations
- Air Force Office of Scientific Research
- United States Air Force