NASA’s X-59 aircraft is predicted to produce a significantly quieter cruise sonic boom than traditional [Formula: see text]-wave-producing aircraft. A propagation simulation study was undertaken to quantify loudness levels, exposure size, and variability of the X-59’s low-boom carpet using realistic atmospheric profiles across the contiguous United States of America (CONUS). Near-field pressure data of the X-59 in supersonic cruise from NASA’s fully unstructured Navier–Stokes three-dimensional (known as FUN3D) computational fluid dynamics code were propagated using NASA’s PCBoom code, which solves an enhanced Burgers equation along acoustic rays. Atmospheric profiles from the National Oceanic and Atmospheric Administration’s Climate Forecast System Version 2 database were used for propagation at 138 locations across the CONUS. Carpets at each location were generated for aircraft headings in the four cardinal directions. Over one million X-59 carpets were generated in total. The effects of the heading, season, geography, and climate zone on boom levels and exposure size are presented. Multiple linear regression models were developed to estimate carpet width and loudness metrics across the CONUS. These results inform regulators and mission planners on expected variations in boom levels and carpet extent from atmospheric variations. Understanding potential carpet variability is important when planning community noise surveys using the X-59.

The degree of insensitivity to atmospheric turbulence was evaluated for five metrics (A-, B-, E-weighted sound exposure level, Stevens Mark VII Perceived Level, and NASA's Indoor Sonic Boom Annoyance Predictor) that correlate to human annoyance from sonic booms. Eight N-wave shaped sonic booms from NASA's FaINT experiment and five simulated "low-boom" sonic booms were turbulized by Locey's ten atmospheric filter functions. The B-weighted sound exposure level value changed the least due to the turbulence filters for twelve of thirteen booms. This makes it the most turbulence stable metric which may be useful for quiet supersonic aircraft certification.

NASA will soon be collecting noise-annoyance community survey data as the X-59 aircraft flies supersonically over several communities in the USA. Sparse measurements of the X-59 sonic thumps will be used together with physics-based simulations to estimate noise doses at survey participant locations. These dose estimates have associated error that affects the accuracy of modeled dose-response curves, which can result in misestimation of annoyance. The precision in dose-response curves is also a consideration in selecting the number of survey participants. To enable pretest studies of dose error and precision, simulated dose-response data were generated based on NASA’s Quiet Supersonic Flights 2018 test. The data included various degrees of dose error and sample size. Frequentist multilevel logistic regression models were fit to the true and perturbed dose-response data. Simple proportional relationships were identified between the model parameters and the perturbation standard deviation. The summary dose-response curves illustrate the impact on accuracy if dose error is not accounted for in the model. The precision in the dose-response curves is also shown as the number of participants and degree of participation is varied. Finally, sampling variability is illustrated by showing the dose-response curves for several replicates with random draws of participants and errors.

NASA plans to fly the X-59 aircraft over communities to gather data on the response to quiet supersonic flight. This data campaign could revolutionize the aerospace industry by enabling commercial, overland supersonic flight. To prepare for this campaign, NASA is developing PCBoom, a software suite of sonic boom modeling tools. PCBoom-predicted Perceived Level (PL) values were previously compared with measured PL values from a recent NASA test flight campaign, Quiet Supersonic Flights 2018 (QSF18), and were found to differ by an average of 6 dB. This work investigates the PCBoom prediction performance using data from NASA’s 2020 CarpetDIEM Phase I flight test using an F-18 aircraft. PL predictions are compared using the PCBoom default F-18 F-function near-field as input versus a computational fluid dynamics near-field solution for the aircraft as input. To investigate potential sources of metric variability and differences between modeled and measured metrics, Least Absolute Shrinkage and Selection Operator (LASSO) and least-squares regression are used. Because weather has a strong influence on sonic boom variability, the regression techniques are also used to guide the necessary number of ground weather measurements to capture boom metric variability. [Work supported by NASA Langley Research Center through the National Institute of Aerospace.]

Propagation of sonic booms through turbulence reduces mean sonic boom perception metric levels and also causes considerable variability. NASA's PCBoom suite of sonic boom acoustic propagation modules includes an approximate method for accounting for the effects of turbulence on traditional N-wave sonic booms. The current implementation is ineffective for shaped sonic booms or low-booms, and it also has limited values for turbulence and ambient input parameters. NASA's future X-59 low-boom community noise surveys require an accurate estimate of the effects of turbulence in regions across the USA, so the module must be improved. This work presents the methods of selecting which ambient and turbulence parameters should be included in an improved PCBoom turbulence module. Turbulence and ambient data were collected from two atmospheric model databases, the Climate Forecast System Version 2 and European Centre for Medium-Range Weather Forecast Reanalysis Version 5 (ERA5), hourly from 7 AM to 7 PM local time for 10 years at 19 locations across the USA. A fully-factorial propagation analysis using these parameters would be exceedingly computationally expensive. Instead, a central composite design was chosen resulting in 45 combinations of ambient and turbulence parameters. These 45 cases effectively sample the space balancing computational burden.