Radiation from Wavepackets

Atmospheric turbulence causes fluctuations in the angle-of-arrival (AOA) of sound waves. These fluctuations adversely affect the performance of sensor arrays used for source detection, ranging, and recognition. This article examines, from a theoretical perspective, the variance of the AOA fluctuations measured with two microphones. The AOA variance is expressed in terms of the propagation range, transverse distance between two microphones, acoustic frequency, and effective spectrum of quasi-homogeneous and isotropic turbulence, with parameters dependent upon the height above the ground. The effective spectrum is modeled with the von Kármán and Kolmogorov spectral models. In the latter case, the results simplify significantly, and the variance depends on the path-averaged effective structure-function parameter, which characterizes the intensity of temperature and wind velocity fluctuations in the inertial subrange of turbulence. The standard deviation of the AOA fluctuations is studied numerically for typical meteorological regimes of the daytime atmospheric boundary layer. For the cases considered, the standard deviation varies from a fraction of degree to around 1°–2°, and increases with increasing friction velocity and surface heat flux.

Time reversal (TR) is a process that can be used to generate high amplitude focusing of sound. It has been previously shown that high amplitude focused sound using TR in reverberant environments exhibits multiple nonlinear features including waveform steepening and a nonlinear increase in peak compression pressures [Patchett and Anderson, J. Acoust. Soc. Am. 151(6), 3603–3614 (2022)]. The present study investigates the removal of one possible cause for these phenomena: free-space Mach stems. By constraining the focusing in the system to one-dimensional (1-D) waves, the potential formation of Mach stems is eliminated so that remaining nonlinear effects can be observed. A system of pipes is used to restrict the focused waves to be planar in a 1-D reverberant environment. Results show that waveform steepening effects remain, as expected, but that the nonlinear increase in compression amplitudes that appears in TR focusing of three-dimensional (3-D), finite-amplitude sound in rooms disappears here because Mach stems cannot form in a 1-D system. These experiments do not prove that Mach stems cause the nonlinear increase observed for focusing in a 3-D environment, but they do support the Mach stem explanation.

This Letter presents an analysis of near-field acoustic data collected on Space Launch System's Mobile Launcher tower during the Artemis I mission. Twelve pressure sensors located two and four effective nozzle diameters ( De) from the vehicle centerline recorded maximum overall sound pressure levels ranging from ∼ 162 dB to more than 170 dB, originating ∼ 10  De downstream of the nozzle exit plane. Frequency-dependent characteristics are also discussed. The peak noise is radiated over a broader frequency range than in the far field. Low-frequency noise locations match other rockets, but high-frequency locations diverge, falling between prior measurements of undeflected and deflected plumes.

Transfer learning (TL) is used to predict source-receiver range in a laboratory tank with varying water temperature. The input data are single-hydrophone spectral levels from linear chirps over the 50–100 kHz band recorded at different ranges. Data measured in room temperature water are used to train one-dimensional convolutional neural networks. When the trained models are applied to data measured in warmer water, a bias is introduced. TL with a small dataset improves the generalization results at the new temperature, demonstrating the potential of TL to improve performance under variable environmental conditions.

Crowds at collegiate basketball games react acoustically to events on the court in many ways, including applauding, chanting, cheering, and making distracting noises. Acoustic features can be extracted from recordings of crowds at basketball games to train machine learning models to classify crowd reactions. Such models may help identify crowd mood, which could help players secure fair contracts, venues refine fan experience, and safety personnel improve emergency response services or to minimize conflict in policing. By exposing the key features in these models, feature selection highlights physical insights about crowd noise, reduces computational costs, and often improves model performance. Feature selection is performed using random forests and least absolute shrinkage and selection operator logistic regression to identify the most useful acoustic features for identifying and classifying crowd reactions. The importance of including short-term feature temporal histories in the feature vector is also evaluated. Features related to specific 1/3-octave band shapes, sound level, and tonality are highly relevant for classifying crowd reactions. Additionally, the inclusion of feature temporal histories can increase classifier accuracies by up to 12%. Interestingly, some features are better predictors of future crowd reactions than current reactions. Reduced feature sets are human-interpretable on a case-by-case basis for the crowd reactions they predict.

Particulate contamination requires dust mitigation techniques to provide low-scatter surfaces on sensitive instrumentation in space. We have shown that poly(olefin sulfone)s photodegrade under spacelike conditions: in vacuum and with UV light exposure. We now demonstrate that photodegradable polymers can reduce dust accumulation on optical surfaces for space applications. This investigation shows that the dissociative degradation of poly(olefin sulfone)s significantly decreased the number of dust particles on a dust-coated surface. These results suggest a powerful way to mitigate the collection of extraterrestrial dust on optical surfaces in space, enabling passive removal of particulate contamination without any direct human intervention.