Active Noise Control of Automotive Tyres

Automotive vehicles are becoming heavier with wider tyres to support this weight. Above a speed of around 30 miles per hour, the noise produced by the tyres is the dominant source. Environmental noise pollution is only one design attribute for vehicles and yet is one of the most important attributes relating to people who don't purchase the vehicle but are impacted by it. Society is relying on building homes closer to roads, yet the mitigation methods currently predicted will be limited by the higher vehicle weights. Traditional barriers are also insufficient and take up valuable road space. Instead in this PhD, it is the source of the sound which will be reduced.

This PhD will aim to reduce the environmental noise pollution from automotive tyres using active noise control techniques. This will advance the possible methods used to reduce the impact of vehicles on society. As the tyre rolls on the rough road surface, it vibrates, causing noise to be radiated in all directions. This noise pollution reflects from the road surface, and underside of the vehicle, scattering in a 3D sound field. Due to the shape of the tyre meeting the road surface, the sound can be amplified significantly, which complicates the mitigation.

Reducing the noise has traditionally relied on passive damping methods to wheel arches, however, in this project, you will apply active noise control techniques to reduce the carcass vibration frequencies in a 3D environment. Typically, the cancellation sound field is generated by a loudspeaker, however, this is impractical as it would then cause more sound radiation and pollution in some directions. Instead, a highly novel directional noise generation source will be employed to direct the cancellation sound to exactly the location where the noise is produced, whilst being located away from the tyre surface. This will involve making 3D maps of the sound field using an acoustic camera and creating directional cancellation signals. You will have access to experimental tyre test rigs and vehicles.

Active noise control of the vehicle interior has been successfully implemented to reduce the interior tyre noise, but this PhD research is focused on the impact on society and the potential reductions which can be achieved. The directional sound source will be used to generate sound waves which are focused in a tight beam, which cannot be achieved with traditional speaker designs which radiate unwanted sound in all directions. The project will augment the experimental tyre sound radiation measurements with simplified, real time models of the tyre surface, to aid the controller in mitigating the surrounding 3D sound field which includes scattering.

A key question is whether it is feasible to reduce any of the tyre external noise at frequencies important for pedestrians or urban dwellings. This external modification of the sound field from a vehicle is a vision for reducing the noise on the ecosystem. The student will be expected to set up a real-time Simulink model of the tyre vibration to tune a Simulink control model which provides the active noise cancellation signals. It is possible that reinforced machine learning will be used to tune the controller gains for the spatial 3D noise optimisation.

The focus will be on a joint theoretical and experimental PhD with skills developed of use to the automotive industry. Research questions to answer include the nonlinear demodulation which occurs in the presence of scattering surfaces, the interaction of microphones in the presence of both the original sound and a cancellation field and what the practical implementation of technology looks like. 

Loughborough has access to a full size anechoic chamber for experimental measurements, and a full series of data acquisition equipment. It also has access to a full sized tyre test trailer for prototype or baseline experimental measurements. 

You will be working with Dr Dan O'Boy and others from the Department of Aeronautical and Automotive Engineering who have a proven track record working with industrial manufacturers such as Ford and Jaguar Land Rover. Your supervision team will include experts in machine learning applied to acoustic signals and control. You will have access to other noise experts, control, experimental and machine learning specialists as needed.