In the past the exterior and interior noise level of vehicles
has been largely reduced to follow stricter legislation and due to
the demand of the customers. As a consequence, the noise quality
and no longer the noise level inside the vehicle plays a crucial
role. For an economic development of new powertrains it is
important to assess noise quality already in early development
stages by the use of simulation. Recent progress in NVH simulation
methods of powertrain and vehicle in time and frequency domain
provides the basis to pre-calculated sound pressure signals at
arbitrary positions in the car interior. Advanced simulation tools
for elastic multi-body simulation and novel strategies to measure
acoustical transfer paths are combined to achieve this goal.
In order to evaluate the obtained sound impression a roughness
prediction model has been developed. The proposed roughness model
is a continuation of the model published by Hoeldrich and Pflueger.
Within the model simultaneous as well as temporal masking effects
are considered. In addition, specific model parameters have been
adjusted to predict subjective ratings of 18 experienced subjects,
including mechanical engineers and audio engineers. The adapted
roughness model has been developed by the usage of real sound
stimuli measured in the car interior for different pre-defined
engine types. Regression analysis shows that in most cases the
subjectively perceived roughness can be predicted with good
accuracy. Finally, the development model is tested with new stimuli
not used in the development of the model; also for these new
stimuli a good agreement of R₂ ≥ 88% could be achieved.
After the discussion of the roughness prediction model,
parameter variations for an automotive internal combustion engine
(ICE) are discussed and compared with the aid of the new roughness
model. From the results it is shown that the developed model is
well suited to assess design changes and their consequences on the
perceived roughness. Therefore, it can be used to develop roughness
optimized solutions already in early design stages.