Comparison and regression analysis of lateral sonic boom measurements and PCBoom predictions

Abstract

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.]