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Transfer path analysis (TPA) has become a widely used diagnostic technique in the automotive and other sectors. In classic TPA, a two-stage measurement is conducted including operational and frequency response function (FRF) phases from which the contribution of various excitations to a target quantity, typically cabin sound pressure, are determined. Blocked force TPA (also called in situ Source Path Contribution Analysis, in-situ TPA and component TPA) is a development of the classic TPA approach and has been attracting considerable recent attention. Blocked force TPA is based on very similar two stage measurements to classic TPA but has two major advantages: there is no need to dismantle the vehicle and the blocked forces obtained are an independent property of the source component and are therefore transferrable to different assemblies.

A major challenge in automotive NVH engineering is to approach complex structure-borne sound and vibration problems with sufficient accuracy but reasonable experimental effort. Typical issues encountered are poor correlation between objective component performance criteria tested for during bench validation and corresponding subjective targets evaluated during system validation in the actual vehicle. Additional challenges arise from the need to impose assumptions on sophisticated physical vibration problems to reduce the complexity to a level feasible for conventional experimental test methods. This paper addresses all mentioned issues by elaborating on a system NVH engineering approach employing Virtual Acoustic Prototyping (VAP) (related to what is now often called component Transfer Path Analysis) to synthesize time domain sound and vibration responses of vibrating machinery operated in a virtual vehicle environment.

Axle forces from tire-road interaction can excite different structural resonances of the vehicle hence a high number of sensors is required for observing and separating all the vibrations dynamics that are coherent with the cabin noise. Feed-forward road noise control strategies adopted so far rely mainly on capturing these dynamics and thus the number of sensors constitutes one major limitation of this approach. Therefore there is a necessity for reducing the number of sensors without degrading the performance of an ANC system. In the past coherence function analysis has been found to be a useful tool for optimizing the sensor location. In this case coherence function mapping was performed between an array of vibration sensors and the headrest microphones in order to identify the locations on the structure that are highly correlated with road noise bands in the compartment.