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This investigation utilizes a correlated fluid-structure interaction (FSI) model of the torque converter and clutch assembly to perform a pseudo transient clutch engagement at steady state operating conditions. The pseudo transient condition consists of a series of nine steady state simulations that transition the torque converter clutch from fully released to near full lockup at a constant input torque and output speed representative of a highway cruising speed. The flow and pressured field of the torque converter torus and clutch are solved using a CFD model and then passed along to a transient structural model to determine the torque capacity of the lockup clutch. Bulk property assumptions regarding the friction material, deformation of the clutch plate, and deflection of supporting structures were made to simplify the model setup, run time, and solution convergence.

Flexibility and backlash of vehicle drivelines typically cause unwanted oscillations and noise, known as shuffle and clunk, during tip-in and tip-out events. Computationally efficient and accurate driveline models are necessary for the design and evaluation of torque shaping strategies to mitigate this shuffle and clunk. To accomplish these goals, this paper develops a full-order physics-based model and uses this model to develop a reduced-order model (ROM), which captures the main dynamics that influence the shuffle and clunk phenomena. The full-order model (FOM) comprises several components, including the engine as a torque generator, backlash elements as discontinuities, and propeller and axle shafts as compliant elements. This model is experimentally validated using the data collected from a Ford vehicle. The validation results indicate less than 1% error between the model and measured shuffle oscillation frequencies.

An in depth discussion is presented as to why some order tracking datasets are difficult to analyze with many if not all order tracking methods. If a solid understanding of a dataset's properties is present, oftentimes it is very clear as to why different order tracking methods behave the way they do. Several analytical datasets will be presented along with comparisons of results achieved using different order tracking methods. In conjunction with these datasets, the algorithm performance of nearly all current order tracking methods will be characterized with respect to the different properties a dataset may possess. It is hoped that through the data, concepts, and examples presented in the paper that the reader will develop a very intuitive sense as to how to approach order tracking analysis on both simple and complex or difficult datasets.

As automotive torque converters decrease in both diameter and axial length, the effects of cavitation in the torque converter becomes increasingly important on noise, efficiency, and performance goals. Therefore, a cavitation prediction technique is developed in this investigation. In a previous investigation it was shown that cavitation is effected by inlet temperature, charge pressure, and K-factor. The prediction technique is devolved to encompass these variables. A dimensional analysis using the power product method is performed with all relevant variables. The nearfield acoustical cavitation detection technique, discussed in the previous investigation, is used to obtain experimental results from a torque converter test lab. The test matrix for the experimental results was constructed to include effects from inlet temperature, charge pressure, and K-factor. The data obtained experimentally is used to curve fit the results found through the power product method.