Leveraging Mathematical Models for Efficient Design of Chassis and Powertrain Systems 2017-01-1326

Model based computer-aided processes offer an economical and accelerated alternative to traditional build-and-test "Edisonian" approaches in engineering design. Typically, a CAE based design problem is formulated in two parts, viz. (1) the inverse design problem which involves identification of the appropriate geometry with desired properties, and (2) the forward problem which is the prediction of performance from the product geometry. Solution to the forward problem requires development of an accurate model correlated to physical data. This validated model could then be used for Virtual Verification of engineering systems efficiently and for solving the inverse problem. This paper demonstrates the rigorous process of model development, calibration, validation/verification, and use of the calibrated model in the design process with practical examples from automotive chassis and powertrain systems.

The process of incremental model improvement for substituting physical tests with virtual verification techniques is showcased with a body top-mount example. Further a design problem of a lower control arm is highlighted to show how a model correlated to vehicle data is used to improve an existing design. Model-based topology optimization to reduce component weight is illustrated using a rear twist beam example. A dust shield design problem is presented to show how multiple conflicting objectives can be effectively handled within the framework of topography optimization. Further, a systems approach with multiple solution techniques is shown to solve a practical problem of protecting an engine oil pan sensor from being damaged due to collision with a shipping tray in an engine plant. In the final example analysis of a battery model is used to design limiting pins in a battery tray to effectively arrest shocks encountered by batteries during transport. Finally, the paper offers general conclusions about how selection of appropriate model complexity can further improve the efficiency of computer-aided engineering design approaches.