LMS Virtual.Lab Road Noise offers a full set of capabilities to identify road noise sources, to predict road noise in operating conditions or to perform in-depth analyses in order to better understand the underlying cause of road noise problems. With the solution, a vehicle’s noise and vibration response under partially correlated loads, i.e. suspension forces to the vehicle body can be predicted. Or vice versa, the solution allows determining the operating excitations from partially correlated measurement data. Using this solution, engineers can perform path or modal contribution analysis, determine the root causes of noise problems, for example dominants paths or dominant modes and as a result, one can optimize the road noise characteristics of the vehicle in a much more fundamental and fast way.
LMS Virtual.Lab Road Noise provides a number of estimation methods to identify loads that represent realistic customer usage. The first method represents a direct method in which forces are computed by multiplying the measured vibration difference over a suspension mount by the frequency dependent mount characteristic. This mount stiffness method is best suited when the connecting stiffness is known. The solution also includes an inverse method in which the loads are estimated from the measured, operational responses they cause. This method is used for relatively stiff connections like suspension bushings that show small displacement differences over the connection. These measured operational responses can be partially correlated and are represented through PSDs. The solution takes care of extracting the Principal Components of these partially correlated sources using Singular Value Decomposition. The uncorrelated Principal Components can then be used as input for the fast Modal and FRF-based response prediction solvers. The path and modal contribution assessment tools support root-cause analyses for each of these Principal Components. Finally after having done the contribution analysis on each Principal Component, the solutions allows to recombine these uncorrelated contributions and perform path contribution analysis on the full set of partially correlated sources, so-called multi-reference Path Contribution. Responses are visualized using a wide variety of NVH-specific post-processing utilities, and can be compared with pre-defined or imported targets.
