Sound Source Identification for Tire-Pavement Interaction

Environmental noise due to vehicle traffic is highly topical as it causes millions of people in Europe to be exposed to high long-term noise levels. Tire-pavement interactions are a dominant sub-source of vehicle traffic noise. This thesis demonstrates the application of different frequency-based sound source localization algorithms using microphone array measurements to identify the most dominant sound sources during the tire-pavement interaction. A vehicle trailer equipped with a test tire was used for the microphone array measurements. The application of an inverse method for identifying sound sources in amplitude and phase is presented. This method uses microphone array measurements and Finite Element (FE) simulations to reconstruct the sound sources on the tire from sound pressure measurements. Due to the use of numerical simulations, the actual boundary conditions of the given measurement setup are fully considered. This work illustrates the process of finding the measurement trailer’s correct boundary conditions. Acoustic absorbers, which are mounted for sound insulation on the insides of the measurement trailer, are modeled as equivalent fluid in the FE model. Their material properties are obtained using the Johnson-Champoux-Allard-Lafarge model, whose parameters are fitted from impedance tube measurements. The fitted material properties were validated by comparing the acoustic pressure calculated with the FE method to microphone measurements. For the measurements, a loudspeaker inside the stationary trailer was excited with a sinusoidal signal, and its membrane deflection was measured using a laser scanning vibrometer. The thereby measured surface velocity was imposed as a Neumann boundary condition to the FE problem. In the first step, the sound source localization algorithms are compared using virtual sound pressure measurements and the stationary microphone measurements from the validation setup. Subsequently, the sound sources on the running tire are identified using the inverse method and established beamforming-based methods. For this purpose, measurement runs were performed on Austrian highways with different speeds and on various pavements. With the primary sound sources identified, the sound pressure field within the trailer is reconstructed by forward FE simulations. It could be shown that the inverse method is capable of matching the simulated sound pressure at the microphone positions very well to the microphone measurements and that it outperforms commonly known advanced beamforming-based algorithms, such as CLEAN-SC. Thereby it is possible to reconstruct the sound pressure field at arbitrary positions within the measurement trailer via the Inverse method.