LMS Virtual.Lab Ride Comfort and Road Noise Simulation

LMS Virtual.Lab Ride Comfort and Road Noise Simulation helps users predict operational road noise and perform in-depth analyses to understand the underlying phenomena causing road noise problems. Engineers can study a vehicle’s noise and vibration response under partially correlated loads, determine the root causes of noise problems, investigate dominants paths or dominant modes and optimize road noise characteristics in a much more fundamental and quicker way.
LMS Virtual.Lab Ride Comfort and Road Noise Simulation contains certain LMS Virtual.Lab Motion modules that helps users set up and solve multibody and flexible body simulations for ride comfort studies. LMS Virtual.Lab Motion can be translated to an equivalent linearized NVH model. Time-domain simulations can also be employed to generate PSDs used as excitation or response data in a road noise study based in the frequency domain.
By using power spectral densities or PSDs values, measured or computed excitations or responses can be uncorrelated, or only partially correlated. LMS Virtual.Lab Ride Comfort and Road Noise Simulation offers forced response solvers that provide response PSDs when given excitation signal PSDs.
Besides straightforward response computation, users can declare references in a cross-spectral density function set and create deterministic referenced spectra for response signals. This can be done with or without a pre-processing step called Principal Component Analysis (PCA). PCA extracts principal components of partially correlated sources using singular value decomposition.
Uncorrelated principal components can be used as input for the fast modal and FRFbased response prediction solvers. Included path and modal contribution assessment tools provide all the functionality to perform root-cause analyses for each principal component. Following a contribution analysis of each principal component, it is
possible to recombine the uncorrelated contributions and perform a path contribution analysis on the full set of partially correlated sources, a so-called multi-reference path contribution. Comparable to pre-defined or imported targets, the responses are visualized using a wide variety of NVH-specific post-processing utilities.