Selected Publications
Connor R. Duke, Scott D. Sommerfeldt, Kent L. Gee, and Cole V. Duke
Cole V. Duke, Scott D. Sommerfeldt, Kent L. Gee, and Connor R. Duke
Brian B. Monson, Scott D. Sommerfeldt, and Kent L. Gee
An active noise control (ANC) system was previously developed by Gee and Sommerfeldt for the reduction of tonal noise radiated by small axial cooling fans, such as those found in desktop computers. That system had a 125×125-mm2 footprint, composed of four small loudspeakers surrounding an 80×80-mm2 axial cooling fan in a mock computer casing. A smaller system is described in this paper that has a footprint of 80×80 mm2, which is the space allotted for the 80×80-mm2 standard sized fan. Compared with the previous system, the current system employs a smaller fan running at a higher speed and smaller control speakers. It is demonstrated that the higher output noise levels and higher frequency tones produced by the smaller fan can be reduced by the current ANC system, such that the global control achieved by the smaller system is comparable or better than that achieved by the previous system for the targeted frequencies. It is also shown that control at the second and third harmonics of the blade passage frequency approach theoretical limits.
Kent L. Gee and Micah R. Shepherd (et al.)
In the collection and analysis of high-amplitude jet noise data for nonlinear acoustic
propagation, both model-scale and full-scale measurements have limitations. Model-scale
measurements performed in anechoic facilities are usually limited by transducer and data
acquisition system bandwidths and maximum propagation distance. The accuracy of fullscale
measurements performed outdoors is reduced by ground reflections and atmospheric
effects. This paper describes the use of two nonlinearity indicators as complementary to
ordinary spectral analysis of jet noise propagation data. The first indicator is based on an
ensemble-averaged version of the generalized Burgers equation. The second indicator is the
bicoherence, which is a normalized version of the bispectral density. These indicators are
applied to Mach-0.85 and Mach-2.0 unheated jet noise data collected at the National Center
for Physical Acoustics. Specifically, the indicators are used to separate geometric near-field
effects from nonlinear propagation effects for the Mach-2.0 data, which cannot be done conclusively using comparisons of power spectral densities alone.
propagation, both model-scale and full-scale measurements have limitations. Model-scale
measurements performed in anechoic facilities are usually limited by transducer and data
acquisition system bandwidths and maximum propagation distance. The accuracy of fullscale
measurements performed outdoors is reduced by ground reflections and atmospheric
effects. This paper describes the use of two nonlinearity indicators as complementary to
ordinary spectral analysis of jet noise propagation data. The first indicator is based on an
ensemble-averaged version of the generalized Burgers equation. The second indicator is the
bicoherence, which is a normalized version of the bispectral density. These indicators are
applied to Mach-0.85 and Mach-2.0 unheated jet noise data collected at the National Center
for Physical Acoustics. Specifically, the indicators are used to separate geometric near-field
effects from nonlinear propagation effects for the Mach-2.0 data, which cannot be done conclusively using comparisons of power spectral densities alone.
Kent L. Gee (et al.)
Numerous analyses techniques have been proposed as means of characterizing acoustical
nonlinearities in high-thrust engine noise. These include probability distributions for the pressure
and the time derivative of the pressure (i.e., the gradient), the skewness and kurtosis coefficients of
the pressure and its time derivative, and Howell-Morfey nonlinear indicators. In this paper, a
number of these analyses techniques are applied to acoustic data recorded during a series of
military jet flyovers. The analysis examines these different measures as a function of microphone
height above the ground. This analysis provides strong indications that microphone should be
mounted well above the ground to properly measure nonlinearities in high-thrust engine noise.
nonlinearities in high-thrust engine noise. These include probability distributions for the pressure
and the time derivative of the pressure (i.e., the gradient), the skewness and kurtosis coefficients of
the pressure and its time derivative, and Howell-Morfey nonlinear indicators. In this paper, a
number of these analyses techniques are applied to acoustic data recorded during a series of
military jet flyovers. The analysis examines these different measures as a function of microphone
height above the ground. This analysis provides strong indications that microphone should be
mounted well above the ground to properly measure nonlinearities in high-thrust engine noise.
Kent L. Gee (et al.)
Crackle is a phenomenon sometimes found in supersonic jet noise and can comprise an annoying and dominant part of the overall perceived noise. In the past, crackle has been commonly quantified by the skewness of the time waveform. In this investigation, a simulated waveform with a virtually identical probability density function and power spectrum as an actual F/A-18E afterburner recording has been created by nonlinearly transforming a statistically Gaussian waveform. Although the afterburner waveform crackles noticeably, playback of the non-Gaussian simulated waveform yields no perception of crackle at all, despite its relatively high skewness. Closer examination of the two waveforms reveals that although they have virtually identical statistics, there are considerable differences in their time rates of change in the intense compressive portions of the waveforms. The afterburner waveform is much more shocklike with its more rapid variations in pressure than the non-Gaussian simulated waveform. This results in a significant difference in the probability distributions of the time derivatives of the actual and simulated data and suggests that the perception of crackle in jet noise waveforms may be better quantified with statistics of the time derivative of the waveform, rather than by the skewness of the time waveform itself.