We are on the cusp of a revolution in the transportation sector, driven by the significant progress on electric powertrains, and robotics and autonomous systems. With the expectation of a substantial commercial opportunity, drone manufacturers are investing heavily in R&D, and looking at how drone technology can be used more efficiently. Drone technology is rapidly advancing, and therefore, the potential for drone use seems almost unlimited at this stage. The drone industry and several stakeholders are currently exploring the feasibility of different drone applications from monitoring, intervention to improve or support public services, delivery of goods and transport of people.
Noise has been suggested as a major barrier for the public acceptance of drone operations. This poses important challenges, as the models, methods and evidence for aircraft noise will likely not be of direct application to drone noise. Drone designs, with multiple small propellers, are significantly different to conventional aircraft and rotorcraft. As a result, drones bring unconventional noise signatures dominated by tonal noise and high frequency noise. In addition to this, drones operated in a different manner to conventional aircraft and rotorcraft, and also operate over urban communities not usually exposed to aircraft noise.
This PhD aligns with our strategic vision to develop new methods and tools to integrate human responses into drone design. This Perception-driven Engineering is at the core of recent research at the Acoustics Research Centre, for instance to address the main noise challenges of novel air vehicles to contribute shaping the future of mobility (IUK InCEPTion, EPSRC DroneNoise and EU REFMAP projects).
The aim of this PhD project is to develop auralisation tools for drone noise. Auralisation is a technique for creating an audible sound file for a given vehicle design. The developed auralisation tools will aid the design of drones as an integral element of Perception-driven design of new new air vehicles.
This PhD project can be focused on different key areas:
• Development of models for drone noise (either analytical or semi-empirical) to inform the auralisation of drone configurations
• Improvement of auralisation capabilities for complex operating conditions
• Integration of auralisation capabilities into Virtual Reality platforms.
The supervisory team is open to discussing other options within the overall topic of Perception-driven design of drones.
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