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This paper describes a practical approach to extract the global static stiffness of a body in white (BIW) from dynamic measurements in free-free conditions. Based on a limited set of measured frequency response functions (FRF), the torsional and bending stiffness values are calculated using an FRF based substructuring approach in combination with inverse force identification. A second approach consists of a modal approach whereby the static car body stiffness is deduced from a full free-free modal identification including residual stiffness estimation at the clamping and load positions. As an extra important result this approach allows for evaluating the modal contribution of the flexible car body modes to the global static stiffness values. The methods have been extensively investigated using finite element modeling data and verified on a series of body in white measurements.

Despite the fact that Transfer Path Analysis (TPA) is a well known and widely used NVH tool it still has some hindrances, the most significant being the huge measurement time to build the full data model. For this reason the industry is constantly seeking for faster methods. The core concepts of a novel TPA approach have already been published in a paper at the ISMA 2008 Conference in Leuven, Belgium. The key idea of the method is the use of parametric models for the estimation of loads. These parameters are frequency independent as opposed to e.g. the classical inverse force identification method where the loads have to be calculated separately for each frequency step. This makes the method scalable, enabling the engineer to use a simpler model based on a small amount of measurement data for quick troubleshooting or simply increase accuracy by a few additional measurements and using a more complex model.

15 years of NVH applications make Transfer Path Analysis appear a commodity tool. This is however not the case. Required insight in the application constraints makes TPA remain an expert approach. This paper reviews past progress in TPA methodology and its limitations. It then introduces a number of innovative approaches addressing these, opening new application fields. This includes speed improvement (Fast TPA), structural modeling integration (Modal Contribution Analysis), CAE integration (Hybrid TPA), sound quality interpretation (TPA-sound synthesis) and supporting better exploitation of operational data (Operational Path Analysis). An outlook is given to the next challenge, the application to transient problems.

A key element to bring research advances on intelligent materials to industrial use is that the product CAE models must support such solutions. This involves modeling capabilities for intelligent material systems, sensor and actuator components, control systems as well as their integration in system-level application designs. The final result will then be a multi-attribute optimization approach integrating noise and vibration performance with reliability, durability and cost aspects. As no single integrated solution will fulfill all requirements of the various material and control approaches, the focus of the research is on the use, combination and extension of existing codes and tools.

Source identification applied to a truck engine and using inverse numerical acoustics is presented. The approach is based on acoustic transfer vectors (ATV) and truncated singular value decomposition (SVD). Acoustic transfer vectors are arrays of transfer functions between surface normal velocity and acoustic pressure at response points. They can be computed using boundary element methods (indirect, direct or multi-domain direct formulations) or finite element methods (in physical or modal coordinates). Regularization techniques such as the so-called L-curve approach are used to identify the optimum SVD truncation. To increase the reliability of the source identification, the approach can use velocity measurements on the boundary surface as well as the standard nearfield pressure measurements. It also allows for linear or spline interpolation of the acoustic transfer vectors in the frequency domain, to increase computational speed.

This paper summarizes the development of a predictive vibro-acoustic full vehicle model of a mid-size sedan and focuses on the engineering analysis procedures used to evaluate the design performance related to engine induced noise and vibration. The vehicle model is build up from a mixture of test-based and finite element component models. FRF Based Substructuring is used for their assembly. The virtual car model is loaded by engine forces resulting from indirect force identification. This force-set includes combustion, inertia, piston slap and crank bearing forces, for engine harmonics from 0.5 to 10th order. Such forced response analysis yields vibration levels at every component, at every interface between components, and interior noise predictions. The target is to provide the vehicle NVH manager with the insight required to identify major causes for peak noise levels and to set targets and develop an action plan for every component design team.