A method for identifying global aerodynamic models from flight data in an efficient manner is explained and demonstrated. A novel experiment design technique was used to obtain dynamic flight data over a range of flight conditions with a single flight maneuver. Multivariate polynomials and polynomial splines were used with orthogonalization techniques and statistical modeling metrics to synthesize global nonlinear aerodynamic models directly and completely from flight data alone. Simulation data and flight data from a subscale twin-engine jet transport aircraft were used to demonstrate the techniques. Results showed that global multivariate nonlinear aerodynamic dependencies could be accurately identified using flight data from a single maneuver. Flight-derived global aerodynamic model structures, model parameter estimates, and associated uncertainties were provided for all six nondimensional force and moment coefficients for the test aircraft. These models were combined with a propulsion model identified from engine ground test data to produce a high-fidelity nonlinear flight simulation very efficiently. Prediction testing using a multi-axis maneuver showed that the identified global model accurately predicted aircraft responses.
User Selection Criteria of Airspace Designs in Flexible Airspace Management
2011
10 pages
Report
No indication
English
Transportation Safety , Air Transportation , Airspace , Experiment design , Flight conditions , Aircraft maneuvers , Algorithms , Aircraft control , Transport aircraft , Voronoi diagrams , Boundary conditions , Multivariate statistical analysis , Orthogonality , Parameter identification , Polynomials
AIRSPACE MANAGEMENT SYSTEM, AIRSPACE MANAGEMENT METHOD, AND PROGRAM
European Patent Office | 2020
|Wiley | 2015
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