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    Uncertainty-Based Approach for Dynamic Aerodynamic Data Acquisition and Analysis (2004)
    In:  CASI
    Details
    Publication Date: 2019-07-13
    Description: Development of improved modeling methods to provide increased fidelity of flight predictions for aircraft motions during flight in flow regimes with large nonlinearities requires improvements in test techniques for measuring and characterizing wind tunnel data. This paper presents a method for providing a measure of data integrity for static and forced oscillation test techniques. Data integrity is particularly important when attempting to accurately model and predict flight of today s high performance aircraft which are operating in expanded flight envelopes, often maneuvering at high angular rates at high angles-of-attack, even above maximum lift. Current aerodynamic models are inadequate in predicting flight characteristics in the expanded envelope, such as rapid aircraft departures and other unusual motions. Present wind tunnel test methods do not factor changes of flow physics into data acquisition schemes, so in many cases data are obtained over more iterations than required, or insufficient data may be obtained to determine a valid estimate with statistical significance. Additionally, forced oscillation test techniques, one of the primary tools used to develop dynamic models, do not currently provide estimates of the uncertainty of the results during an oscillation cycle. A method to optimize the required number of forced oscillation cycles based on decay of uncertainty gradients and balance tolerances is also presented.
    Keywords: Aerodynamics
    Type: AIAA Paper 2004-5364 , AIAA Atmospheric Flight Mechanics Conference; Aug 16, 2004 - Aug 19, 2004; Providence, RI; United States
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 2
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    NASA's Learn-to-Fly Project Overview (2018)
    In:  CASI
    Details
    Publication Date: 2019-07-16
    Description: Learn-to-Fly (L2F) is an advanced technology development effort aimed at assessing the feasibility of real-time, self-learning flight vehicles. Specifically, research has been conducted on merging real-time aerodynamic modeling, learning adaptive control, and other disciplines with the goal of using this learn to fly methodology to replace the current iterative vehicle development paradigm, substantially reducing the typical ground and flight testing requirements for air vehicle design. Recent activities included an aggressive flight test program with unique fully autonomous fight test vehicles to rapidly advance L2F technology. This paper presents an overview of the project and key components.
    Keywords: Aircraft Design, Testing and Performance
    Type: NF1676L-28629 , AIAA Aviation and Aeronautics Forum (Aviation 2018); Jul 25, 2018 - Jul 29, 2018; Atlanta, GA; United States
    Format: application/pdf
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    NASA TECHNICAL REPORTS
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