Semi-empiric noise modeling of a Cargo eVTOL UAV by means of system identification from flight noise measurement data

This publication shows the semi-empiric noise modeling of an electric-powered vertical takeoff and landing (eVTOL) unmanned aerial vehicle (UAV) by means of system identification from flight noise measurement data. This work aims to provide noise models with a compact analytical ansatz for horizontal and vertical flight which are suited for integration into a geographical information system. Therefore, flight noise measurement campaigns were conducted and evaluated. An existing noise model ansatz is adapted to the eVTOL UAV under consideration and noise models are computed from the measurement data using the output error method. The resulting models are checked for plausibility by comparing them to technical literature. The horizontal flight noise model is subjected to a correlation analysis and the influence of meteorological effects are examined. To achieve a higher level of accuracy in future noise modelings, an optimization of the microphone positions as well as the flight trajectory is carried out.

Traffic noise models and noise guidelines: A review

Traffic noise is a major cause of noise pollution, and impacts of noise pollution on humans have been studied extensively. Previous studies have helped in the development of traffic noise prediction analysis using robust analytical and computational models, parameters connected with traffic, road characteristics, and environment. This study reviewed 11 traffic noise modeling strategies and parameters used by various agencies in different parts of the world. Seven out of the 11 models reviewed in this paper were based on outdoor measurements while the remaining four were based on outdoor and indoor measurements. Considering the cost and time involved in developing these models, there is need to understand existing traffic noise models, differences, and assumptions before adopting or recalibrating them for local demands. This study contributes to the larger body of knowledge and intends to serve as a reference material for future researchers in the area of traffic noise modeling and techniques.