Keywords

eVTOL, tailsitter, transition, trajectory, optimization, DEP, electric aircraft

Abstract

Electric vertical takeoff and landing (eVTOL) aircraft take advantage of distributed electric propulsion as well as aerodynamic lifting surfaces to take off vertically and perform long-duration flights. Complex aerodynamic interactions and a hard-to-predict transition maneuver from hover to wing-borne flight are one challenge in their development. To address this, we compare three different interaction models of varying fidelity for optimizing the transition trajectory of a biplane tailsitter. The first model accounts for simplified rotor-on-wing interactions using momentum theory, while the other two account for wing-on-wing interactions using a vortex lattice method and rotor-on-wing aerodynamic interactions using blade element momentum theory. One includes the swirl component of velocity and the other neglects swirl. To determine the trajectory, we perform direct collocation on the lowest fidelity model using gradient-based optimization. To compare them, we use the same control inputs obtained in the optimization to integrate trajectories using the higher fidelity models. We find that the higher fidelity models predict power and stall significantly more conservatively than the lowest fidelity model. We conclude that modeling the swirl velocity of the rotor wake and the selected stall model play a significant role in defining the transition trajectory.

Original Publication Citation

Anderson, R., Willis, J., Johnson, J., Ning, A., and Beard, R., “A Comparison of Aerodynamic Models for Optimizing the Takeoff and Transition of a Bi-wing Tailsitter,” AIAA SciTech Forum, virtual, Jan. 2021. doi:10.2514/6.2021-1008

Document Type

Conference Paper

Publication Date

2021-1

Permanent URL

http://hdl.lib.byu.edu/1877/7356

Publisher

AIAA

Language

English

College

Ira A. Fulton College of Engineering and Technology

Department

Mechanical Engineering

University Standing at Time of Publication

Associate Professor

Share

COinS