Search

Reducing the sound pressure level (SPL) reliably and effectively is paramount in the presence of uncertainty. Herein, the robust optimisation of a vibro-acoustic system is performed by combining a probabilistic analysis approach and an approximate methodology. A surrogate model containing uncertain variables is established based on the elliptical basis function (EBF) neural network. Using Monte Carlo (MC) simulations, the probability distribution of the output responses are obtained, and the result shows that the sound pressure is influenced by uncertain factors. The simulated annealing (SA) algorithm is adopted to perform the robust optimisation. It shows that the optimisation results not only reduced the SPL inside the passenger car, but also enabled the SPL to be less sensitive to the fluctuations of the uncertain variables.

Large-eddy simulation method was employed to compute the buffeting noise of a simple deep cavity model. Compared with the results of wind tunnel experiments, the numerical results coincided well with the experimental results, which further validated the correctness of Large-eddy simulation method. Large-eddy simulation method was employed to calculate automobile side-window buffeting noise when the side-window near the driver was partially opened by 25%, and the road test data validated its correctness. The flow field results were loaded to the finite element model to compute dynamic response of the car’s windows, the weak vibration of windows was excited by the air energy exchange between interior and exterior when the car at high speed. Acoustic radiation of window structure, sound pressure levels of field points in the frequency domain, panel acoustic contribution analysis of window structure were obtained by the vibration solution for the windows. The results demonstrated that the noise radiation caused by the windows’ vibration aroused the acoustic response significantly at some certain frequencies, although side-windows buffeting noise dominated in the low frequency domain. The rear window acoustic contribution accounted for the major part of the total pressure. Compared with other three side-windows, the contribution of the partially opened side-window to the total pressure was the greatest.

Flow over an open side window in a car exhibits similar characteristics as the flow over an open cavity. Computational Fluid Dynamics (CFD) simulation over a cavity was done as a benchmark. The unsteady flow simulation was carried out using Scale Adaptive Simulation (SAS) turbulence model. The benchmark results, frequency and sound pressure levels of feedback and resonance modes, all well matched with the experimental data. Then, with the right rear window, for example, the mechanism of the side window buffeting was investigated. The simulation results show that side window buffeting noise is generated by large scale vortices and in low frequency. Furthermore, buffeting noise characteristics under several patterns of side windows opening were also numerically investigated. As a result, rear window buffeting noise is more severe than that of front window when one window open, and combination pattern of side windows open can reduce buffeting noise. To decrease the interior noise and improve car ride comfort, four suppression measures through adding a side window weather deflector at the A-pillars, constructing a cavity at the B-pillars, combination of the front and rear windows and installing a row of square cylinder deflector at the B-pillars were also studied, respectively. In conclusion, certain noise reduction can be achieved through four passive control methods.