Engineering topics

Jordan D. Cluts

Publications and source records attributed to Jordan D. Cluts.

2 records

NASA NTRSDate not supplied

Noise Measurements from Ground Tests of the Moog SureFly Vehicle

Noise measurements from a ground test of a small vertical lift research vehicle are presented. The proof-of-concept all-electric vehicle called “SureFly” was developed by Moog, Inc. A cooperative effort between NASA and Moog, Inc. has led to one of the first acoustic test datasets from an Urban Air Mobility (UAM) vehicle being developed for passenger and cargo. Results show propeller and possibly motor tones are important for the overall noise levels. The vehicle has four support arms each with a pair of contra-rotating propellers. Noise measurements show higher noise levels from the lower propellers, likely due to inflow distortion from the arms and top propellers. Possible motor noise was identified by calculating harmonics of the line frequency and comparing to the tones in the narrowband acoustic spectra and phased microphone array data. The acoustic far field was found to be about 100 feet away from the vehicle, but additional microphones are needed to provide a better assessment. Results show the presence of modulation for some test conditions. The work reported here is only for ground tests.

Acoustics↗

NASA NTRSDate not supplied

An Initial Electric Motor Rotor Vibration Model

Ongoing integration of outrunner brushless electric motors into drones and Advanced Air Mobility aircraft presents a need for accurate acoustic predictions derived from prediction of the motor rotor vibrations. In outrunner motors, the external rotor vibrates and drives the acoustic field. At resonant frequencies of the rotor, the rotor displacements will be the largest. The vibrations can lead to acoustic tones that are relevant to the overall acoustic design of these types of vehicles. This study performs a finite element analysis to assess the modes in two electric motor rotors. This model is then refined into a simple parameterized geometry that can make the same predictions for motors of the same class. A parametric sweep and least-squares-curve fit to the finite element analysis data results in a series of simple curves that can predict the mode shapes and frequencies that are likely to appear in this class of motors without the need for any simulation. The simulations are validated by comparison to acoustic and experimental modal-analysis data.

Electric Motor Noise↗