Ground noise prediction of drone delivery networks

Over the past few years, unmanned aircraft systems (UAS) have been experiencing significant growth in the United States and around the world. In a parallel fashion, online retail sales have also experienced drastic growth, which in turn has focused significant attention on drone delivery networks to increase the volume and speed of commercial packages to retail customers. One of the environmental concerns that these networks pose is noise, which will become a prominent problem that will scale with the number of drones flying simultaneously. Although field testing can provide direct measurements, it does not provide prognostic noise assessments or guide noise control strategies. In this work, we develop an effective and efficient noise evaluation tool that can be utilized to estimate various noise metrics on the ground. The outputs of this tool are represented by noise metrics contours (such as Leq or SEL) at the ground level that can be superposed on a street map.