Comparing measured sonic boom turbulence-induced variability with computational models for the Carpet Determination...

When a sonic boom propagates through the atmospheric boundary layer, atmospheric turbulence distorts the waveform, producing stochastic variations over short distances. Accurate prediction of this variability is essential because turbulence affects both waveform characteristics and human-perception metrics used to evaluate supersonic overflights. During NASA's 2019 Carpet Determination in Entirety Measurements I (CarpetDIEM I) flight test campaign, Brigham Young University deployed a 120 m (400 ft) linear array with seven microphones to quantify turbulence-induced variability of sonic boom waveforms, spectra, and metrics. In this study, state-of-the-art models PCBoom and KZKFourier are combined with two weather models, CFSv2 and ERA5, to predict metric variability and compare results with CarpetDIEM I measurements. Depending on the metric and confidence interval considered, prediction success ranges between about 30% and 80%. For the perceived level metric, the prediction successes for the mean and standard deviation were 30%-48% and 52%-65% respectively, depending on the weather model. This compares favorably with the Sonic Booms in Atmospheric Turbulence prediction success results of 45% and 71% for the perceived level mean and standard deviation respectively, using data reported by Stout, Sparrow, and Blanc-Benon [(2021). J. Acoust. Soc. Am. 149, 3250-3260].