Noise reduction of Urban Air Mobility Vehicles using CFD

Excess land transport emissions result from congestion, under-occupied public transport and lack of door-to-door transport networks. Internal combustion engines are to be phased out in the UK in the coming decades. Urban air mobility vehicles (UAMVs) offer an opportunity to utilise airspace to vastly increase transport capacity and provide near door-to-door service, whilst drastically cutting emissions from hydrocarbon-based fuels. Many manufacturers including Airbus and Rolls-Royce are pursuing such craft for passenger/freight transport, whilst others such as Amazon are testing smaller scale delivery drones.

Propulsion systems are mostly based on electric vertical take-off and landing (eVTOL) concepts, using multiple rotors to enable landing in densely populated areas. Noise is the dominant emission at point of use, preventing social acceptance and use of these vehicles within cities. If noise can be reduced, this sector will see rapid growth.

The proposed project aims to model flow and noise of key aircraft features including rotor sections, rotor tips and airframe interactions. High fidelity unsteady Computational Fluid Dynamics (CFD) such as Large-Eddy Simulation (LES) has been successfully used to predict jet, landing gear and aerofoil noise. This will inform noise reduction strategies such as aerofoil flow control and airframe modifications. High Performance Computing (HPC) will be used to generate and analyse the large unsteady datasets with parallel tools. The disparity in turbulence flow scales near surfaces relative to the aircraft requires multi-fidelity modelling of turbulence and geometry representation. Data from these simulations will inform lower-fidelity modelling to model entire aircraft and will be exploited with machine learning/AI. These large-scale models can then be used to study impact of (multiple) flightpaths, noise foot prints on the ground and effectiveness of noise mitigation strategies.