Commercial vehicle Two Cylinder Powertrain Mount Selection Based on Robust Optimization Using MSC/ADAMS and modeFrontier

Paper #:
  • 2018-01-1286

  • 2018-04-03
Ride comfort, driving stability and drivability are vital factors in terms of vehicle performance and the customer satisfaction. The IC engine is the source for the vibration that reduces the vehicle performance and it need to be controlled to some extent such that the vehicle performance will be improved. The IC engine is made up of reciprocating and rotating parts which result in unbalanced forces during operation and produce vibrations at the vehicle supporting members. The vibration reduction is possible by minimizing unbalanced forces and/or by providing the anti-vibration mounts at the engine-vehicle interface. The power train is suspended on the vehicle frame on several flexible mounts, whose purpose is to isolate the powertrain vibration from the frame. Total six different modes of engine like roll, yaw, pitch, vertical, lateral and longitudinal need to be isolated. Engine mount stiffness and position 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, 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 positions are the chosen design variables. A baseline normal mode simulation is done using the vibration plugin in MSC ADAMS/View. A complete workflow is set up in modeFrontier wherein all the design variables, output variables, constraints and objectives are defined. Optimization is done using modeFrontier 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 robustness of all feasible solutions. This results in selection of most robust solution given as design recommendation.
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