Browse Publications Technical Papers 2018-01-1286

Commercial Vehicle Two Cylinder Powertrain Mount Selection Based on Robust Optimization Using MSC/ADAMS and Mode Frontier 2018-01-1286

Ride comfort, drivability and driving stability are important factors defining vehicle performance and customer satisfaction. The IC powertrain is the source for the vibration that adversely affects the vehicle performance. The IC powertrain is composed of reciprocating and rotating components which result in unbalanced forces, moments during operation and produce vibrations at the vehicle supporting members. The vibration reduction is possible by minimizing unbalanced forces and/or by providing anti-vibration mounts at the powertrain-vehicle interface.
The power train is suspended on the vehicle frame via several flexible mounts, whose function is to isolate powertrain vibrations from the frame. Total six different modes of powertrain vibration namely - roll, yaw, pitch, vertical, lateral and longitudinal need to be isolated. Powertrain mount stiffness and location is critical in this regard. The corresponding six modal frequencies must meet certain acceptance criteria which are calculated based on factors like number of cylinders, idling rpm and wheel hop frequency. The modal kinetic energy distribution must ensure proper decoupling between the six modes.
In this study, a commercial vehicle two-cylinder powertrain model mounted on three mounts is built in MSC ADAMS/View. Powertrain mass and inertia properties are considered as constants. The rubber mounts stiffness and locations are the chosen design variables. A baseline normal mode simulation is done in MSC ADAMS/View. A complete workflow is set up in Mode Frontier wherein all the design variables, output variables, constraints and objectives are defined. Optimization is done using Mode Frontier yielding all the feasible solutions.
Next step is to subject all the input data sets of feasible solutions to manufacturing and assembly tolerances and to check their robustness. This results in selection and recommendation of most robust solution.


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