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As the frequency of rocket launches increases, accurately predicting their noise is necessary to assess structural, environmental, and societal impacts. NASA’s Space Launch System (SLS) is a challenging vehicle to model because it has both solid-fuel rocket boosters and liquid-fueled engines that contribute to its thrust at launch. This paper discusses measured aeroacoustic properties of this super heavy-lift rocket in the context of supersonic jet theory and measurements of other rockets. Using four measured aeroacoustic properties: directivity, spectral peak frequency, maximum overall sound pressure level, and overall sound power level, an equivalent rocket based on merged plumes is created for SLS. With the constraint that the effective thrust and mass flow rates should match those of the actual vehicle, a method using weighted averages of the disparate plume parameters successfully reproduces SLS’s desired aeroacoustic properties, yielding a relatively simple model for the complex vehicle. 

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. 

Atmospheric acoustic waves from volcanoes at infrasonic frequencies (0.01–20 Hz) can be used to estimate source parameters for hazard modeling, but signals are often distorted by wavefield interactions with topography, even at local recording distances (<15 km). We present new developments toward a simple empirical approach to estimate attenuation by topographic diffraction at reduced computational cost. We investigate the applicability of a thin screen diffraction relationship developed by Maekawa [1968, doi: https://doi.org/10.1016/0003-682X(68)90020- 0]. We use a 2D axisymmetric finite-difference method to show that this relationship accurately predicts power losses for infrasound diffraction over an idealized kilometer-scale screen; thus validating the scaling to infrasonic wavelengths. However, the Maekawa relationship overestimates attenuation for realistic volcano topography (using Sakurajima Volcano as an example). The attenuating effect of diffraction may be counteracted by constructive interference of multiple reflections along concave volcano slopes. We conclude that the Maekawa relationship is insufficient as formulated for volcano infrasound, and suggest modifications that may improve the prediction capability. 

Abstract Volcanic eruption source parameters may be estimated from acoustic pressure recordings dominant at infrasonic frequencies (< 20 Hz), yet uncertainties may be high due in part to poorly understood propagation dynamics. Linear acoustic propagation of volcano infrasound is commonly assumed, but nonlinear processes such as wave steepening may distort waveforms and obscure the sourcing process in recorded waveforms. Here we use a previously developed frequency-domain nonlinearity indicator to quantify spectral changes due to nonlinear propagation primarily in 80 signals from explosions at Yasur Volcano, Vanuatu. We find evidence for$$\le$$ $\le$10⁻³ dB/m spectral energy transfer in the band 3–9 Hz for signals with amplitude on the order of several hundred Pa at 200–400 m range. The clarity of the nonlinear spectral signature increases with waveform amplitude, suggesting stronger nonlinear changes for greater source pressures. We observe similar results in application to synthetics generated through finite-difference wavefield simulations of nonlinear propagation, although limitations of the model complicate direct comparison to the observations. Our results provide quantitative evidence for nonlinear propagation that confirms previous interpretations made on the basis of qualitative observations of asymmetric waveforms. 

Outdoor ambient acoustical environments may be predicted through supervised machine learning using geospatial features as inputs. However, collecting sufficient training data is an expensive process, particularly when attempting to improve the accuracy of models based on supervised learning methods over large, geospatially diverse regions. Unsupervised machine learning methods, such as K-Means clustering analysis, enable a statistical comparison between the geospatial diversity represented in the current training dataset versus the predictor locations. In this case, the geospatial features that represent the regions of western North Carolina and Utah have been simultaneously clustered to examine the common clusters between the two locations. Initial results show that most geospatial clusters group themselves according to a relatively small number of prominent geospatial features, and that Utah requires appreciably more clusters to represent its geospace. Additionally, the training dataset has a relatively low geospatial diversity because most of the current training data sites reside in a small number of clusters. This analysis informs a choice of new site locations for data acquisition that maximize the statistical similarity of the training and input datasets. 

Machine learning refers to a collection of computational techniques for identifying or learning patterns in data. Although existing techniques are most effective on large data sets, there is growing interest in applying methods on smaller ones. We consider the application of machine learning to predicting ambient sound levels in the contiguous United States from GIS data. The challenge is limited availability of training data from which to construct a model--data collection in this case is both cost and time expensive. This leads us to consider two questions: First, how to best validate a machine learning model with limited training data and two, given additional data can we measurably improve the accuracy of the model. We create an ensemble of models that perform equally well as measured by leave-one-out cross validation on our initial training set. However, these models give wildly different predictions for areas in the central region of the country. By collecting additional data in cropland areas in Utah, we were able to improve the predictions of our machine learning model to other, geographically similar regions of the country. *National Science Foundation Grant 1557998 Brigham Young University Physics and Astronomy 

Abstract Sound waves generated by erupting volcanoes can be used to infer important source dynamics, yet acoustic source‐time functions may be distorted during propagation, even at local recording distances (15 km). The resulting uncertainty in source estimates can be reduced by improving constraints on propagation effects. We aim to quantify potential distortions caused by wave steepening during nonlinear propagation, with the aim of improving the accuracy of volcano‐acoustic source predictions. We hypothesize that wave steepening causes spectral energy transfer away from the dominant source frequency. To test this, we apply a previously developed single‐point, frequency domain, quadspectral density‐based nonlinearity indicator to 30 acoustic signals from Vulcanian explosion events at Sakurajima Volcano, Japan, in an 8‐day data set collected by five infrasound stations in 2013 with 2.3‐ to 6.2‐km range. We model these results with a 2‐D axisymmetric finite‐difference method that includes rigid topography, wind, and nonlinear propagation. Simulation results with flat ground indicate that wave steepening causes up to2 dB (1% of source level) of cumulative upward spectral energy transfer for Sakurajima amplitudes. Correction for nonlinear propagation may therefore provide a valuable second‐order improvement in accuracy for source parameter estimates. However, simulations with wind and topography introduce variations in the indicator spectra on order of a few decibels. Nonrandom phase relationships generated during propagation or at the source may be misinterpreted as nonlinear spectral energy transfer. The nonlinearity indicator is therefore best suited to small source‐receiver distances (e.g.,2 km) and volcanoes with simple sources (e.g., gas‐rich strombolian explosions) and topography.