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Numerical Calibration of a Low-Speed sUAS Flush Air Data System

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  • 1 Smead Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, Colorado
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Abstract

A method using computational fluid dynamics to numerically calibrate a flush air data system is presented. A small unmanned aircraft system (sUAS) has been equipped with a flush air data system and experimentally tested. The flush air data system uses computational fluid dynamics to train neural networks and is validated using the in-flight data that were previously collected. Results of the flight validation are presented, along with ways to improve the accuracy of the system. Several different calibration approaches are presented and compared with each other. The best-case results with the in-flight calibration are 0.59° and 0.66° for angle of attack and sideslip, respectively, whereas the best-case results when calibrated with computational fluid dynamics data are 0.78° and 0.90°. It is also possible to estimate other air data parameters, such as dynamic pressure, static pressure, and density, with neural networks, but the direct calculation is more accurate. Calibrating the system numerically, such as with the use of computational fluid dynamics, removes the need for any calibration flights. Although not as accurate as the in-flight calibration, numerical calibration is possible and can save the user time and expense.

Current affiliation: Ball Aerospace & Technologies Corp., Boulder, Colorado.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Roger Laurence, roger.laurenceiii@colorado.edu

Abstract

A method using computational fluid dynamics to numerically calibrate a flush air data system is presented. A small unmanned aircraft system (sUAS) has been equipped with a flush air data system and experimentally tested. The flush air data system uses computational fluid dynamics to train neural networks and is validated using the in-flight data that were previously collected. Results of the flight validation are presented, along with ways to improve the accuracy of the system. Several different calibration approaches are presented and compared with each other. The best-case results with the in-flight calibration are 0.59° and 0.66° for angle of attack and sideslip, respectively, whereas the best-case results when calibrated with computational fluid dynamics data are 0.78° and 0.90°. It is also possible to estimate other air data parameters, such as dynamic pressure, static pressure, and density, with neural networks, but the direct calculation is more accurate. Calibrating the system numerically, such as with the use of computational fluid dynamics, removes the need for any calibration flights. Although not as accurate as the in-flight calibration, numerical calibration is possible and can save the user time and expense.

Current affiliation: Ball Aerospace & Technologies Corp., Boulder, Colorado.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Roger Laurence, roger.laurenceiii@colorado.edu
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