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Semi-Empirical Model for the Prediction of Surface Vibration Amplitudes of Kilowatt Class Electric MotorsThe ongoing integration of outrunner brushless electric motors into drones and Urban Air Mobility aircraft presents a need for electric motor acoustic predictions suitable for system-level studies. A motor-noise prediction model allows motor acoustics to be incorporated into early vehicle design trade studies. An ongoing effort to develop a motor-noise prediction model, for integration into other system-level tools, is underway. The model is comprised of three main elements which predict the electromagnetic forcing, the induced structural vibrations, and the resulting acoustic propagation. This work discusses the structural vibration element and describes a method to predict surface vibration amplitudes of the motor rotor. The surface displacement amplitude is a requirement for the acoustic propagation element. To accurately predict the rotor displacement, accurate damping values for the rotor structure are required. An experimental investigation of damping values for 1-5kW class motors are presented. A parametric sweep and least-squares-curve fit to a finite element analysis yields a simple curve that can predict vibration amplitude based on a particular driving frequency and mode shape.
Document ID
20250005692
Acquisition Source
Glenn Research Center
Document Type
Conference Paper
Authors
Jordan D. Cluts
(Glenn Research Center Cleveland, United States)
Brenda S. Henderson
(Glenn Research Center Cleveland, United States)
Gregory R. Busch
(Glenn Research Center Cleveland, United States)
Date Acquired
May 30, 2025
Subject Category
Acoustics
Meeting Information
Meeting: AIAA Aviation Forum
Location: Las Vegas, NV
Country: US
Start Date: July 21, 2025
End Date: July 25, 2025
Sponsors: American Institute of Aeronautics and Astronautics
Funding Number(s)
WBS: 664817.02.03.03.02.01
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Technical Review
NASA Peer Committee
Keywords
Electric Motor Noise
Prediction Tools
Modelling
eVTOL
Urban Air Mobility (UAM)
UAM
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