In the research project “Acoustically and Energetically Self-Optimizing Aircraft (AkesoFlug)” funded by the Bavarian Staatsministerium für Wirtschaft, Landesentwicklung und Energie, a novel approach towards reducing the sound emission and energy consumption of unmanned aerial vehicles is being researched together with the Chair of Fluid Mechanics at the Friedrich-Alexander-Universität Erlangen-Nürnberg.
The aim of the research project is to enable unmanned aerial vehicles to capture their own acoustic radiation, to compare it with the current operating conditions and to optimize both energy consumption and sound emission independently during flight. For this purpose, a methodology based on machine learning is being researched which enables aircraft to quantify their own current operating state in terms of various parameters. In order to be able to capture the aircraft’s acoustic airborne sound radiation with onboard sensors, it is necessary to separate acoustic from hydrodynamic pressure fluctuations in a flow, for which a novel sensor system is being researched and implemented as part of the research project. As control variables or variable operating parameters, the current engine speed, as well as the speed, the acceleration, the position and the orientation of the aircraft are taken into account. In order to achieve the specified or situation-dependent target criteria such as noise reduction, energy efficiency or time efficiency, these operating parameters are dynamically adjusted and optimized during the flight.