Modeling and trajectory planning for acoustically aware aircraft with propeller phase control
Patterson, Andrew
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/113994
Description
Title
Modeling and trajectory planning for acoustically aware aircraft with propeller phase control
Author(s)
Patterson, Andrew
Issue Date
2021-12-02
Director of Research (if dissertation) or Advisor (if thesis)
Hovakimyan, Naira
Doctoral Committee Chair(s)
Hovakimyan, Naira
Committee Member(s)
Salapaka, Srinivasa
Stipanovic, Dusan
Voulgaris, Petros
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
UAV, Aircraft Noise, Control, Planning
Abstract
Autonomous air vehicles need to be aware of the noise they are creating and be able to adjust their flight operations to meet acoustic constraints. Despite advancements in noise estimation and reduction, acoustic models for vehicles are still limited. These models are either too complex for planning or too simple to capture important components of vehicle noise. As a result of this simplicity, planning-based noise reduction methods are limited to adjusting flight plans by moving away from acoustic constraints or slowing down the vehicle speed. This research presents a framework for predicting and reducing the noise created by distributed electric propulsion vehicles. The presented framework uses an acoustic model that captures the directivity and interference of the vehicle's propellers. This model is used to plan trajectories that meet acoustic constraints without changing the vehicle path or speed.
To predict the vehicle noise, we first introduce a machine learning method that approximates the noise generated by each propeller for a given flight condition. This learning method is trained on a computationally expensive acoustic model. The approximation is then combined with a vehicle model and a dynamically feasible trajectory generation method to predict the sound pressure levels created by the vehicle trajectory. The approximation achieves less than 6% maximum error on the cross-validation dataset.
To reduce the environmental noise without changing the vehicle path, we demonstrate a propeller synchronization method that causes the propeller noises to interfere destructively. This method is called phase control and is validated for two electrically powered propellers in an anechoic chamber. In this test, noise is reduced by more than 15 dB. Further, the method is demonstrated in simulation, where a ten-propeller vehicle must pass between two acoustic constraints. A surrogate optimization procedure is applied to the phase selection problem for this more complex scenario. The optimized phase angles produce a sound pressure reduction of approximately 17 dB, satisfying the constraints without changing the vehicle speed or spatial trajectory.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
