SAE Technical Paper Series 2015
DOI: 10.4271/2015-26-0140
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A Robust Solution for a Power-Train Mounting System for Automotive NVH Refinements

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Cited by 5 publications
(2 citation statements)
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“…Much research has been carried out to improve the ill-conditioning of matrix inversion by singular value decomposition (SVD). For NVH refinement of automotives, much attention has been paid to optimization design of PMS [18][19][20][21][22][23]; however, there is little research on combining TPA for dominant path identification with optimization design of resilient mounts. e goal of this paper is to present a solution method to solve the floor vibration problem of light buses, in which the method combines matrix inversion TPA for identification of path contribution with the optimization of energy decoupling rate of PMS.…”
Section: Introductionmentioning
confidence: 99%
“…Much research has been carried out to improve the ill-conditioning of matrix inversion by singular value decomposition (SVD). For NVH refinement of automotives, much attention has been paid to optimization design of PMS [18][19][20][21][22][23]; however, there is little research on combining TPA for dominant path identification with optimization design of resilient mounts. e goal of this paper is to present a solution method to solve the floor vibration problem of light buses, in which the method combines matrix inversion TPA for identification of path contribution with the optimization of energy decoupling rate of PMS.…”
Section: Introductionmentioning
confidence: 99%
“…Due to the dilemma between the lighter vehicle and the more powerful engine, the NVH performance of an engine becomes much more significant. Powertrain mounting system (PMS) is not only designed for supporting the weight of the powertrain and controlling the displacement of the powertrain center of gravity (CG), but also of great help for improving the NVH performance of an engine, especially when the vehicle is in the condition of key on/off, 1,2 idle, 3 and so on.…”
Section: Introductionmentioning
confidence: 99%