Selected Publications
Nonlinear propagation of noise from military jet aircraft has been fairly well documented, but only within a few hundred meters from the aircraft. This paper describes analysis of nonlinear propagation for morning static runups of F-35 aircraft at greater distances, out to 1220 m near the direction of maximum radiation and at heights ranging from 0 m up to 30.5 m. A comparison of overall levels with distance and height reveals evidence of significant atmospheric refraction effects, and a general trend of decreasing level with height. Examination of nonlinearity metrics reveals opposite behavior, however. At these distances, nonlinear propagation effects are often strongest in waveforms with lower sound levels, which is counterintuitive. One important finding, however, is that acoustic shock strength can vary greatly from runup to runup, even for seemingly small changes in atmospheric conditions. This analysis demonstrates the need for further research into long-range nonlinear propagation of jet noise through realistic atmospheric conditions.
Wind-induced microphone self-noise is a non-acoustic signal that may contaminate outdoor acoustical measurements, particularly at low frequencies, even when using a windscreen. A recently developed method [Cook et al., JASA Express Lett. 1, 063602 (2021)] uses the characteristic spectral slope of wind noise in the inertial subrange for screened microphones to automatically classify and reduce wind noise in acoustical measurements in the lower to middling frequency range of human hearing. To explore its uses and limitations, this method is applied to acoustical measurements which include both natural and anthropogenic noise sources. The method can be applied to one-third octave band spectral data with different frequency ranges and sampling intervals. By removing the shorter timescale data at frequencies where wind noise dominates the signal, the longer timescale acoustical environment can be more accurately represented. While considerations should be made about the specific applicability of the method to particular datasets, the wind reduction method allows for simple classification and reduction of wind-noise-contaminated data in large, diverse datasets.
This editorial's goals are (1) to highlight a few key developments in supersonic jet and launch vehicle noise research over the past several decades while describing some of the critical modern requirements facing government and industry organizations and (2) to summarize the contributions of the articles in this Supersonic Jet Noise special issue in the context of these developments and requirements.
In 1971, the U.S. National Aeronautics and Space Administration (NASA) published a seminal report—NASA SP-8072—which compiled the results of the early supersonic jet noise studies and provided methods to calculate the noise produced from launch vehicles. Fifty years later and despite known limitations, SP-8072 remains the foundation for much of the launch vehicle noise modeling today. This article reviews what has been learned about the physics of noise generation and radiation from free and impinging rocket plumes since the completion of SP-8072. State-of-the-art methods for the mitigation of launch vehicle noise are also reviewed. A discussion of launch vehicle noise modeling, from empirical to numerical and including reduced-order models of supersonic jets, points to promising approaches that can describe rocket noise characteristics not captured by SP-8072.
This paper describes the contributions of David T. Blackstock to the understanding of nonlinear propagation of jet noise. Although he investigated this problem with students in the 1970s, their findings directly formed the foundation for my own doctoral studies begun in 2002, partly caused by a relative lack of high-amplitude jet noise propagation research in the intervening years. Moreover, exchanges with David during my doctoral program and after led to improved physical insights, additional research directions, and meaningful interactions.
Modeling outdoor environmental sound levels is a challenging problem. This paper reports on a validation study of two continental-scale machine learning models using geospatial layers as inputs and the summer daytime A-weighted L-50 as a validation metric. The first model was developed by the National Park Service while the second was developed by the present authors. Validation errors greater than 20 dBA are observed. Large errors are attributed to limited acoustic training data. Validation environments are geospatially dissimilar to training sites, requiring models to extrapolate beyond their training sets. Results motivate further work in optimal data collection and uncertainty quantification.