Selected Publications
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).
Classical jet noise theory indicates that radiated sound power is proportional to the jet velocity raised to the eighth and third powers for subsonic and supersonic jets, respectively. To connect full-scale measurements with classical jet noise theory, this letter presents sound power and acoustic efficiency values for an installed GE-F404 engine. When subsonic, the change in sound power follows the eighth-power law, and the sound power change approximately follows the third-power law at supersonic conditions, with an acoustic efficiency of ∼0.5-0.6%. However, the OAPWL increase from subsonic to supersonic jet velocities is greater than would be predicted.
In a recent study of noise from a T-7A-installed GE F404 engine, microphones along a 76 m (250 ft) arc were mounted 1.8 m (5 ft) above the ground to quantify human impacts. While helpful for this purpose, the resulting multipath effects pose challenges for other acoustical analyses. For jet noise runup measurements, these effects are complicated by the fact that the noise source is extended and partially correlated, and its spatial properties are frequency dependent. Furthermore, a finite-impedance ground surface and atmospheric turbulence affect interference nulls. This study applies a ground-reflection method developed previously [Gee et al., Proc. Mtgs. Acoust. 22, 040001 (2014)] for rocket noise measurements. The model accounts for finite ground impedance, atmospheric turbulence, and extended source models that are treated as coherent and incoherent arrays of monopoles. Application to the ground runup data to correct the 76 m spectra at a range of angles suggests the incoherent line source model is more appropriate at upstream and sideline angles whereas the coherent source model is more appropriate for downstream propagation. Comparisons with near-field data and similarity spectra show that, while imperfect, this method represents an advancement in correcting jet noise spectra for ground reflection effects.