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Ride comfort is a critical factor to customer perception of vehicle quality as it is related to vehicle experience when driving. It adds value to the product and, consequently, to vehicle brand. It has become a demand not only for passenger unibody vehicles but also to larger segments including mid-size trucks. Ride quality is usually quantified as harshness which is a measure of how the vehicle transmits the road irregularities to the customer at the tactile points such as the steering wheel and seats. Improving harshness requires tuning of different parts including tires, chassis frame/subframe and suspension mounts and bushings. This paper describes the methodology to enhance the harshness performance for a mid-size truck using a full vehicle CAE model. The influence of stiffnesses of body mounts and control arms bushings to harshness response is investigated through sensitivity analysis and the optimal configuration is found.

For purposes of reducing development time, cost and risk, the majority of new vehicles are derived strongly or at least generally from a surrogate vehicle, often of the same general size or body style. Previous test data and lessons learned can be applied as a starting point for design of the new vehicle, especially at early phases of the design before definite design decisions have been finalized and before prototype of production test hardware is available. This is true as well of vehicle NVH development where most new vehicles being developed are variants of existing vehicles for which the main noise transfer paths from sources of interest are already understood via test results and existing targets. The NVH targets for new vehicles are defined via benchmarking, market considerations, and other higher-level decisions. The objective is then to bridge the gap between test data from surrogate vehicles to direct support of the NVH development of new vehicle programs.

The development of new technologies that reduce engine size and improve performance, combined with the introduction of hybrid and electric vehicles, make tire noise critically important for the new generation of automobiles. Tire noise transmission into the passenger compartment can be classified as either air-borne or structure-borne sound. Both of these mechanisms are very complex to predict because tires are highly non-linear, subject to large static, dynamic and centrifugal loads; they suffer from impact, stick and slip forces; and the pumping of air in the tire grooves is complicated. Customers today demand more sophistication of products in terms of interior noise; thus, sound quality metrics have earned an important role during the design phase allowing human perception of noise to be predicted and improved with reduced cost in a way that addresses consumer preferences.

The early design phase of vehicle development is a challenge for NVH because test hardware is not yet available. Surrogate test data may be available and can be combined with best engineering practices to make design decisions, but accurate prediction capability for modeling NVH effects of proposed changes for new vehicles programs is valuable. SEA is a mature CAE technology for NVH development in the automotive industry worldwide, particularly for airborne noise predictions and at higher frequencies (above 200 Hz). Unlike FEA and deterministic modeling techniques, SEA accuracy relies on a good characterization of acoustic absorption, structural damping, and details of flanking paths, particularly leakage. However, an SEA model correlated to test data that isolates the contribution and mechanisms of each of the dominant paths is a resource that can be used for early design phase NVH predictions of noise transfer paths.

This work is focused on the computer aided engineering noise and vibration control area (CAE-NVH), which is one of the most important in the automobile industry. The reason for that relevancy is that the noise and vibration effects can be directly perceived by the costumer. The vibration of the seats and steering wheel, as well as audible noises are some examples of factors that can cause discomfort to the driver. During the early design of a car, the systems are designed in a way to reach a good modal management level in order to avoid resonance problems. The finite element models, used to predict these resonances, are normally generated using only deterministic values for the model parameters such as stiffnesses, thicknesses and masses. However, these properties have an uncertainty due to the manufacturing process which is, in most cases, not taken into consideration during the design.