Modeling UAVs using CFD and Machine Learning Methods

Wayne W. Farrell, Michael P. Kinzel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

To design the control systems needed for aerial vehicles various low order aerodynamic and dynamics models are developed to capture the dominate handling characteristics of the vehicle. These models however can generally take on the form of simplified look-up tables which provide static aerodynamic load data and in more advanced cases loads due to oscillation in a particular degree of freedom. Therefore, these methods do not capture much of the complex nonlinear behaviors that occur outside of static and quasi-steady behavior. In this work a nonlinear dynamic model of a UAV will be developed using controlled flight responses from select unsteady CFD simulations. Machine learning techniques are employed to capture discrepancies and compare against a simple linear model and the CFD results. Overall, this work will demonstrate a method to create fast medium-fidelity reduced order dynamic models of aerial vehicles with complex geometries using small sets of costly high-fidelity CFD models suitable for control system design.

Original languageEnglish (US)
Title of host publicationAIAA SciTech Forum 2022
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624106316
DOIs
StatePublished - 2022
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022 - San Diego, United States
Duration: Jan 3 2022Jan 7 2022

Publication series

NameAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022

Conference

ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022
Country/TerritoryUnited States
CitySan Diego
Period1/3/221/7/22

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

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