Selected Publications

As the global space industry expands, rockets are being launched from a greater number of spaceports with a rapidly increasing cadence. Because of the growth in the number of spaceports, the cadence increase, and efforts at vehicle optimization to reduce weight and cost, noise has the potential to create harmful impacts – from vehicle vibroacoustic loading to expanded environmental footprint. This paper provides a brief overview of current Australian spaceport and launch vehicle development, which involves near-term plans for small-payload orbital launches. Bounds on overall sound power level from these rockets is described, as well as maximum overall sound pressure level using two different models. One of these models, RUMBLE, is used to show maximum predicted levels at the Great Barrier Reef. Eventual refinement and validation of these predictions will aid in assessing potential noise impacts on vehicles, structures, communities, and threatened and endangered species.

Far-field acoustical characterization of blast wave propagation from explosives is often carried out using relatively small shot sizes (less than 1 kg). This paper describes a series of eleven Composition C4 detonations, with shot charge mass varying from 13.6 kg to 54.4 kg (30 to 120 lbs.) that were recently measured at the Big Explosives Experimental Facility (BEEF) at the Nevada National Security Site. Pressure waveform data were recorded at up to nine different stations, ranging from 23 m to 2.7 km from the blast origin, with some angular variation. As part of examining blast overpressure decay with distance and comparing with literature, the data were analyzed from the context of human safety regulations. To provide improved guidance for BEEF personnel working distances, an empirical model equation was developed for the distance, as a function of shot size, at which the peak pressure level drops below 140 dB. A preliminary investigation into peak level variability due to wind was also conducted.

During a rocket’s liftoff, its extreme sound levels can damage launch structures, payload electronics, and even the rocket itself.

Separating crowd responses from raw acoustic signals at sporting events is challenging because recordings contain complex combinations of acoustic sources, including crowd noise, music, individual voices, and public address (PA) systems. This paper presents a data-driven decomposition of recordings of 30 collegiate sporting events. The decomposition uses machine-learning methods to find three principal spectral shapes that separate various acoustic sources. First, the distributions of recorded one-half-second equivalent continuous sound levels from men's and women's basketball and volleyball games are analyzed with regard to crowd size and venue. Using 24 one-third-octave bands between 50 Hz and 10 kHz, spectrograms from each type of game are then analyzed. Based on principal component analysis, 87.5% of the spectral variation in the signals can be represented with three principal components, regardless of sport, venue, or crowd composition. Using the resulting three-dimensional component coefficient representation, a Gaussian mixture model clustering analysis finds nine different clusters. These clusters separate audibly distinct signals and represent various combinations of acoustic sources, including crowd noise, music, individual voices, and the PA system.

To improve acoustical models of super heavy-lift launch vehicles, this Letter reports Space Launch System's (SLS's) overall sound power level (OAPWL) and compares it to NASA's past lunar rocket, the Saturn V. Measurements made 1.4–1.8 km from the launchpad indicate that SLS produced an OAPWL of 202.4 (
0.5) dB re 1 pW and acoustic efficiency of about 0.33%. Adjustment of a static-fire sound power spectrum for launch conditions implies Saturn V was at least 2 dB louder than SLS with approximately twice the acoustic efficiency.

The National Transportation Noise Map (NTNM) gives time-averaged traffic noise across the continental United States (CONUS) using annual average daily traffic. However, traffic noise varies significantly with time. This paper outlines the development and utility of a traffic volume model which is part of VROOM, the Vehicular Reduced-Order Observation-based model, which, using hourly traffic volume data from thousands of traffic monitoring stations across CONUS, predicts nationwide hourly varying traffic source noise. Fourier analysis finds daily, weekly, and yearly temporal traffic volume cycles at individual traffic monitoring stations. Then, principal component analysis uses denoised Fourier spectra to find the most widespread cyclic traffic patterns. VROOM uses nine principal components to represent hourly traffic characteristics for any location, encapsulating daily, weekly, and yearly variation. The principal component coefficients are predicted across CONUS using location-specific features. Expected traffic volume model sound level errors—obtained by comparing predicted traffic counts to measured traffic counts—and expected NTNM-like errors, are presented. VROOM errors are typically within a couple of decibels, whereas NTNM-like errors are often inaccurate, even exceeding 10 decibels. This work details the first steps towards creation of a temporally and spectrally variable national transportation noise map.
Theses, Captstones, and Dissertations





















































