Selected Publications
The last two years have seen more orbital rocket launches than any period in history, exposing launch pads, natural environments, and communities to large acoustical loads. This paper is part of an ongoing effort by BYU to disseminate the results of acoustical measurements of these launch vehicles. Specifically, this paper summarizes BYU’s measurement and analysis of the Falcon-9 SARah-1 launch and landing out of Vandenberg Space Force Base in June 2022. This measurement differs from typical launch measurements due to the sonic boom created by the reentry and landing of the first-stage booster. In total, 9 measurement stations were set up at locations between 400 m and 15000 m from the launch pad, and each station successfully recorded the launch noise and reentry sonic boom. Several metrics are reported for both the launch and sonic boom at each station and compared with a previous measurement. Additionally, spectral analysis shows the sonic booms to peak at a lower frequency than the launch noise, and that they spread cylindrically rather than spherically. No evidence is found of a decrease in peak frequency at stations farther from the pad.
The distinctive geometry and structural characteristics of Balinese gamelan gongs lead to the instrument's unique sound and musical style. This work presents high-resolution directivity measurements of two types of gamelan gongs to quantify and better understand their acoustic behavior. The measured instruments' structural modes clearly impact their far-field directivity patterns, with the number of directional lobes corresponding to the associated structural mode shapes. Many of the lowest modes produce dipole-like radiation, with the dipole moment determined by the positions of the nodal and antinodal regions. Higher modes exhibit more complex patterns with multiple lobes often correlated with the location and number of antinodal regions on the gong's edge. Directivity indices correspond to dipole radiation at low frequencies and quadrupole radiation at intermediate and higher frequencies. Symmetry analysis confirms that the gong's rim significantly impacts the resultant directivity.
This paper investigates the measured far-field noise from the Space Launch System’s Artemis-I mission liftoff. Pressure waveform data were collected at seven locations 12 to 50 kilometers from Kennedy Space Center’s (KSC) Launch Complex 39B in Cape Canaveral, Florida. Reported are initial analyses of these measurements outside the perimeter of KSC, including waveform characteristics, overall sound pressure levels, and frequency spectra. Analyses build upon an initial publication [K. L. Gee et al., JASA Exp. Lett. 3, 023601 (2023)] that documented acoustical phenomena at stations 1.5 to 5.2 km from the pad and contributed to a more complete understanding of the noise produced by super heavy-lift launch vehicles. At the stations discussed in this paper, maximum overall sound pressure levels ranged from less than 65 dB to 116 dB with significant variations seen at equidistant locations. As distance increases, one-third-octave band spectra show a significant decrease in peak frequency from 18 Hz down to 3 Hz and a reduction in relative high-frequency content.
On 10 November 2022, measurements were made of the Atlas V JPSS-2 rocket launch from SLC-3E at Vandenberg Space Force Base, California. Measurements were made at 11 stations from distances of 200 m to 7 km from the launch pad. Measurement locations were arranged at various azimuthal angles relative to the rocket to investigate possible noise asymmetry. This paper discusses preliminary results from this measurement including overall levels, temporal and spectral characteristics, evidence of nonlinear propagation, and potential azimuthal asymmetry effects.
Modeling environmental sound levels over continental scales is difficult due to the variety of geospatial environments. Moreover, current continental-scale models depend upon machine learning and therefore face additional challenges due to limited acoustic training data. In previous work, an ensemble of machine learning models was used to predict environmental sound levels in the contiguous United States using a training set composed of 51 geospatial layers (downselected from 120) and acoustic data from 496 geographic sites from Pedersen, Transtrum, Gee, Lympany, James, and Salton [JASA Express Lett. 1(12), 122401 (2021)]. In this paper, the downselection process, which is based on factors such as data quality and inter-feature correlations, is described in further detail. To investigate additional dimensionality reduction, four different feature selection methods are applied to the 51 layers. Leave-one-out median absolute deviation cross-validation errors suggest that the number of geospatial features can be reduced to 15 without significant degradation of the model's predictive error. However, ensemble predictions demonstrate that feature selection results are sensitive to variations in details of the problem formulation and, therefore, should elicit some skepticism. These results suggest that more sophisticated dimensionality reduction techniques are necessary for problems with limited training data and different training and testing distributions.
Applying machine learning methods to geographic data provides insights into spatial patterns in the data as well as assists in interpreting and describing environments. This paper investigates the results of k-means clustering applied to 51 geospatial layers, selected and scaled for a model of outdoor acoustic environments, in the continental United States. Silhouette and elbow analyses were performed to identify an appropriate number of clusters (eight). Cluster maps are shown and the clusters are described, using correlations between the geospatial layers and clusters to identify distinguishing characteristics for each cluster. A subclustering analysis is presented in which each of the original eight clusters is further divided into two clusters. Because the clustering analysis used geospatial layers relevant to modeling outdoor acoustics, the geospatially distinct environments corresponding to the clusters may aid in characterizing acoustically distinct environments. Therefore, the clustering analysis can guide data collection for the problem of modeling outdoor acoustic environments by identifying poorly sampled regions of the feature space (i.e., clusters which are not well-represented in the training data).
Theses, Captstones, and Dissertations