Abstract

The introduction of autonomous urban air mobility vehicles is expected to revolutionize urban mobility. To ensure the safe operation of these autonomous vehicles, the flight management system needs to be validated for as many flight scenarios and operational conditions as possible. Therefore, a digital twin is invaluable as part of an efficient development process that ensures a safe product. This must include simulation models of the aircraft and the environment to develop and validate the flight management system. This study evaluated a simulation framework for its ability to support the development of automated flight management systems in an integrated manner. The framework comprises 3 coupled software packages which perform time-domain simulations. First, Siemens Simcenter Amesim® is used for modelling the flight dynamics together with propulsion systems and the navigation control loops. Second, Simcenter Prescan® models an urban environment and exteroceptive sensors that detect features in the environment (e.g. camera, lidar). Finally, Simulink® is used to connect both Amesim and Prescan. The approach is demonstrated in a case study of an all-electric, four-seater, octocopter with a maximum take of weight of 2200kg. The simulation framework was used to simulate multiple collision scenarios of the eVTOL aircraft with a tower crane in different orientations on the planned flight path to assess the obstacle detection and evasion functions. The results showed that different evasion trajectories are used depending on the orientation of the crane. Moreover, the results also show the aircraft acceleration levels that can be used for the structural assessment of the aircraft and for the assessment of the comfort level of the occupancies during the object evasion maneuvers. Hence, it is can be concluded that the simulation framework includes all required capabilities in an easy-to-use environment to support the development and validation of automated flight functions of autonomous urban air mobility vehicles. This work will be extended in the future with human body motion simulations of the passengers. Moreover, this use case will be extended with the prediction of fly-over noise.