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Brake groan noise and vibration occurs in a stopped vehicle by the simultaneous application of torque to the wheel and the gradual release of brake pressure. Eventually the torque load breaks the friction between pad and rotor causing slippage and energy release. If the torque load is not large enough to maintain slippage, a sustained stick-slip vibration, called groan, can occur which transmits a low frequency noise to the vehicle interior. In some cases the noise levels caused by groan can be objectionable, thus procedures for developing remedial designs are needed. To this end, a project was performed to analytically simulate groan vibration in a vehicle with a McPherson strut type suspension. The goal was to demonstrate that analytical models could be used to simulate groan behavior and to identify suspension components that affect the groan behavior. The ADAMS software was used to model a brake/suspension system.

Many analytical approaches have been proposed in the literatures to evaluate the brake squeal dynamics, the complex eigenvalue approach probably being the most popular. Although this method is generally accepted, it suffers from several drawbacks. One is that the analysis does not provide a clear indication of the squeal mechanism. Another is that the predictions are sensitive to slight changes to the system model. For this reason, a variation of the complex eigenvalue approach has been developed that is more robust and provides insight into the squeal mechanism. In this paper, the new method is used to identify the types of modal coupling mechanisms that lead to squeal. Based on this investigation, the authors present three different types of mode coupling conditions that cause squeal.

A building-block method, often used for simulating automotive systems, is described in this paper for simulating a driveline system. In the method, a driveline supplier's design responsible components are modeled with explicit FE models. Model accuracy is verified by testing and correlating the components in a free-free condition. Non-design responsible components are modeled using lumped parameters and/or modal models. These components and the validated design responsible components are integrated into a system model and connected using simple lumped parameter connections. Correlation at the system level is performed by making adjustments to the connection parameters and to the parameters of the non-design responsible components. The resulting system model has been used to accurately predict operating responses in a driveline system.

The widespread use of finite element models in assessing system dynamics for NVH evaluation has led to a recognition of the need for improved procedures for correlating models to experimental results. With the greater occurrence of finite element models preceding the first prototype hardware, it is now practical to employ pre-test analysis procedures to guide the execution of the tests in the correlation process. This aids in the efficiency of the test process, ensuring that the test article is neither under nor over instrumented. The test-analysis model (TAM) that results from the pre-test simulation provides a means to compare the test and model both during the test and during the model updating process. This paper discusses procedures for pre-test analyses and demonstrates their application to the correlation of a transmission side cover.

Analytical simulation is being used more frequently in automotive design and development to address concerns such as ride, handling, and NVH. An important consideration in these types of simulation is the accuracy of the analytical model. For this reason, a critical, but often difficult, stage in modeling is the validation of the model with measured test data. In recent years, correlation software has been developed to alleviate some of the burden of model validation. The newest software capability is based on formulations directed at correlating models to measured frequency response functions (FRFs). In this paper, the techniques needed for applying an FRF-based correlation approach are presented using several automotive case histories.