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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.

Seatbelt retractors as important part of modern safety systems are mounted in any automotive vehicle. Their internal locking mechanism is based on mechanically sensing elements. When the vehicle is run over rough road tracks, the retractor oscillates by spatial mode shapes and its interior components are subjected to vibrations in all 6 degrees of freedoms (DOF). Functional backlash of sensing elements cause impacts with neighbouring parts and leads to weak, but persistent rattle sound, being often rated acoustically annoying in the vehicle. Current acoustic retractor bench tests use exclusively uni-directional excitations. Therefore, a silent 2 DOF test bench is developed to investigate the effect of multi-dimensional excitation on retractor acoustics, combining two slip-tables, each driven independently by a shaker. Tests on this prototype test bench show, that cross coupling between the two perpendicular directions is less than 1%, allowing to control both directions independently.