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Vehicle NVH performance is significantly affected by the dynamics of various primary systems. In the automotive industry, different design activities or vendors are responsible for designing various different systems simultaneously. Therefore, it is highly desirable to gain a better understanding of the individual system characteristics and the interaction between the primary systems to achieve a desirable overall NVH performance. Unfortunately, it is usually quite difficult to construct a proper fixture to accurately measure and quantify the actual uncoupled system characteristics. This paper examines an alternate approach of applying the FRF-based substructuring method to back-calculate the system response characteristics from the full vehicle system measurements. The results are then used to forward-compute the dynamic response of the vehicle, which are also validated by comparison to the direct response function measurements.

This paper is intended to describe some of the advances in automotive NVH research and applications based on recent developments in the Structural Dynamics Research Laboratory (SDRL) at the University of Cincinnati. State-of-the-art vibro-acoustic research capabilities and infrastructure ranging from advanced vibration modal analysis and spectral techniques for linear and nonlinear automotive systems to computational tools for structure-borne acoustic noise generation, transmission and synthesis problems are discussed. These systems have been devised with the intent of integrating a versatile set of experimental, computational and analytical approaches in order to be able to investigate a variety of crucial automotive NVH concerns. The materials will be grouped into three separate but closely related sets of applications consisting of (i) powertrain noise and vibration control, (ii) analysis and control vehicle system dynamics, and (iii) NVH and sound quality.

A multi-coordinate FRF-based inverse substructuring approach is proposed to partition a vehicle system into two or more substructures, which are coupled at discrete interface points. The joint and free substructure dynamic characteristics are then extracted from the coupled system response spectra. Depending on the actual form of the structural coupling terms, three forms of the coupling matrix are assumed here. The most general one constitutes the non-diagonal form, and the other two simpler cases are the block-diagonal and purely diagonal representations that can be used to simplify testing process and overcome computational problems. The paper is focused on the investigation of the durability of these three formulations when the input FRFs are noise contaminated. A finite element model of a simplified vehicle system is used as the case study.

A new multi-level substructuring approach is proposed to predict the NVH response of driveline systems for the purpose of analyzing rear axle gear whine concern. The fundamental approach is rooted in the spectral-based compliance coupling theory for combining the dynamics of two adjacent subsystems. This proposed scheme employs test-based frequency response functions of individual subsystems, including gear pairs, propshaft, control arms and axle tube, in free-free state as sequential building blocks to synthesize the complete system NVH response. Using an existing driveline design, the salient features of this substructuring approach is demonstrated. Specifically, the synthesized results for the pinion-propshaft assembly and complete vehicle system are presented. The predictions are seen to be in excellent agreement with the experimental data from direct vehicle measurements.

The vehicle axle gear whine noise and vibration are key issues for the automotive industry to design a quiet, reliable driveline system. The main source of excitation for this vibration energy comes from hypoid gear transmission error (TE). The vibration transmits through the flexible axle components, then radiates off from the surface of the housing structure. Thus, the design of hypoid gear pair with minimization of TE is one way to control the dynamic behavior of the vehicle axle system. In this paper, an approach to obtain minimum TE and improved dynamic response with optimal tooth profile modification parameters is discussed. A neural network algorithm, named Back Propagation (BP) algorithm, with improved Particle Swarm Optimization (PSO) is used to predict the TE if some tooth profile modification parameters are given to train the model.