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Generally within engineering design, the current emphasis is on biasing the development process towards increased virtual prototyping and reduced “real” prototyping. Therefore there is a requirement for more CAE based automated optimisation, Design of Experiments and Design for Six Sigma. The main requirements for these processes are that the model being analysed is parametric and that the solution time is short. Prediction of gear whine behaviour in automatic transmissions is a particularly complex problem where the conventional FEA approach precludes the rapid assessment of “what if?” scenarios due to the slow model building and solution times. This paper will present an alternative approach, which is a fully parametric functionality-based model, including the effects of and interactions between all components in the transmission. In particular the time-varying load sharing and misalignment in the planetary gears will be analysed in detail.

Generation mechanism of transmission gear whine varies significantly by gear position, frequency and path/amplifier of the total system. Although controlling the source, namely transmission error/dynamic meshing force of the gears is desirable; it is not always feasible as well as most effective. This paper describes the root cause analyses of high frequency gear whine (overdrive position) of commercial vehicle, which combined in-depth experimental and CAE analyses. The generation mechanism of the gear whine is clarified efficiently utilizing Ford Spin-Torsional AWD NVH Test Facility, state-of-the-art Powertrain NVH development test cell, combining vehicle and sub-system NVH measurement. The analyses results showed the O/D gear whine is driveshaft airborne, due to alignment of driveshaft higher bending resonance to air-borne mode (“breathing mode”).

Gear whine can be reduced through a combination of gear parameter selection and manufacturing process design directed at reducing the effective transmission error. The process of gear selection and profile modification design is greatly facilitated through the use of simulation tools to evaluate the details of the tooth contact analysis through the roll angle, including the effect of gear tooth, gear blank and shaft deflections under load. The simulation of transmission error for a range of gear designs under consideration was shown to provide a 3-5 dB range in transmission error. Use of these tools enables the designer to achieve these lower noise limits. An equally important concern is the dynamic mesh stiffness and transmissibility of force from the mesh to the bearings. Design parameters which affect these issues will determine the sensitivity of a transmission to a given level of transmission error.

Active noise control (ANC) technique with the filtered-x least mean square (FXLMS) algorithm has proven its efficiency and drawn increasingly interests in vehicle noise control applications. However, many vehicle interior and/or exterior noises are exhibiting non-Gaussian type with impulsive characteristic, such as diesel knocking noise, injector ticking, impulsive crank-train noise, gear rattle, and road bumps, etc. Therefore, the conventional FXLMS algorithm that is based on the assumption of deterministic and/or Gaussian signal may not be appropriate for tackling this type of impulsive noise. In this paper, an ANC system configured with modified FXLMS (MFXLMS) algorithm by adding thresholds on reference and error signal paths is proposed for impulsive noise control. To demonstrate the effectiveness of the proposed algorithm, an experimental study is conducted in the laboratory.

An enhanced, frequency domain filtered-x least mean square (LMS) algorithm is proposed as the basis for an active control system for treating powertrain noise. There are primarily three advantages of this approach: (i) saving of computing time especially for long controller’s filter length; (ii) more accurate estimation of the gradient due to the sample averaging of the whole data block; and (iii) capacity for rapid convergence when the adaptation parameter is correctly adjusted for each frequency bin. Unlike traditional active noise control techniques for suppressing response, the proposed frequency domain FXLMS algorithm is targeted at tuning vehicle interior response in order to achieve a desirable sound quality. The proposed control algorithm is studied numerically by applying the analysis to treat vehicle interior noise represented by either measured or predicted cavity acoustic transfer functions.

In order to effectively evaluate automatic transmission gear noise and vibration performance using a hemi-anechoic test facility, it is essential to understand the coupling mechanism between the transmission internals and the dynamometers and associated shafting. Once this coupling mechanism is well understood, each major frequency response of the resulting torsional vibration operating data can be properly categorized according to the source: transmission-internal, facility, or driveshaft. This knowledge helps noise and vibration engineers properly manage vibration peaks in transmission operating data by ensuring that the issue of concern is not inadvertently influenced by the facility system. Analytical simulations and tests were performed on a transmission operated in a hemi-anechoic facility to evaluate gear vibration using various driveshafts, followed by a program of vehicle testing.

A series of laboratory driveline clunk experiment was conducted by using an overhung torsion bar and electromagnet to create a sudden change in torque loading in the driveline system. The change of the torque loading was designed to force the driveline to go through the gear lashes inside the rear axle and result in clunk phenomenon. The study was investigated by using a simulation code developed in Matlab and ADAMS CAE. The analytical study enabled parametric investigation of component contribution to various time responses exhibited in the experiment. The results also revealed intricate interaction between the friction properties and the driveline torsional dynamics which were observed in the experiment.