Finite element (FE) models of Body In White (BIW) structures are inherently large and computationally intensive due to a high number of degrees of freedom. Performing optimization studies of body structures in a full BIW model requires a significant amount of time, as each optimization cycle requires multiple iterations of the complete FE model. To reduce computational effort, techniques such as the Component Mode Synthesis (CMS) based reduction methods are commonly utilized. In this work, we adopt the Craig Bampton Nodal (CBN) approach by retaining the component targeted for optimization as the residual structure, while condensing the remaining BIW into a super element. This strategy significantly decreases simulation runtime.
To generate the reduced model, all the interface nodes that connect the retained residual with the super-element structure need to be defined as analysis sets (ASETs). Earlier studies primarily considered residual structures connected through simple RBE2 bolt connections, resulting in a relatively small set of ASET nodes. In the present work, we address more realistic BIW assemblies incorporating complex joining technologies such as spot welds, structural adhesives, bolts, rivets and other intricate connection schemes. This leads to a substantially larger number of ASET nodes. A high ASET count increases boundary condition complexity, memory requirements, and overall simulation runtime, thereby limiting the efficiency of the reduced model. This paper proposes a novel methodology to simplify the connection interfaces between the optimization component and the rest of the BIW while preserving essential dynamic characteristics. With this strategy the number of ASET grids is reduced by roughly 70%, thereby reducing the computation time by approximately 80% without compromising accuracy. The resulting reduced order model enables rapid execution of multiple optimization studies through an efficient use of computational resources, thereby reducing overall lead time and improving the feasibility of iterative design exploration in early vehicle development.