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Completed TRL 2 (started at 2, targeting 4)
This project represents a new paradigm in aerodynamic sensing technology applicable to hypersonic flight – 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. In this method, measurements are made indirectly using internal strain, temperature and acceleration sensors mounted to the airframe, and thus the sensors would be protected from the harsh external environment. This same technology holds promise to provide real-time measurements of the aerodynamic state (e.g, lift force, pitching moment) of the vehicle, which are not measurable by other means in flight, and thus could be used to improve the flight control system.
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, even to the point of enabling real-time reconstruction of aerodynamic loads.
The primary objective of the proposed program is to develop the FAST methodology to derive quasi-static distributed and integrated surface loads during hypersonic vehicle ground or flight testing. Implementation of the FAST methodology will require that a high-fidelity thermoelastic structural simulation of the vehicle be developed. The research platform will be a missile-type vehicle, based on a generic hypersonic vehicle concept, which will be designed to have relatively high flexibility and be easily instrumented. The structural simulation will take as input aerodynamic and thermal loading over the vehicle surface, and output the computed resulting structural deformation. We will devise and numerically demonstrate a scientific machine learning methodology that uses the structural simulation with multi-fidelity aerothermal load predictions to learn and represent the map from applied distributed pressure loads to measured vehicle surface deformations. Experiments will provide validation of the structural-response model, and serve as a platform to assess the FAST methodology. Three challenging capstone experiments are proposed to assess the accuracy of the method; one is based on a benchtop model with thermal and mechanical loading, and the others are cold-flow hypersonic wind tunnel experiments.
The technology could open up an entirely new view of sensing that seeks to extract information encoded in the static deformation and vibrational signature of the structure. This technology also has applications to lower-speed aircraft, rotorcraft and rockets, and thus has enormous commercial potential. This project will be restricted to the measurement of quasi-static forces over the vehicle, but it might be possible to extend the concept to use the airframe dynamics to obtain time-dependent information such as laminar-to-turbulent transition, boundary layer separation, engine instability, scramjet-isolator unstart, and transient loads due to gusts and maneuvers.
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