Efficient Surrogate-based NVH Optimization of a Full Vehicle Using FRF Based Substructuring 2020-01-0629
The computer simulation with the Finite Element (FE) code for the structural dynamics becomes more attractive in the industry since it enables quickly evaluating the dynamic performances of the mechanical products like automobile in development with improved accuracy owing to modern technological advancements. However, it normally takes a prohibitive amount of computation time when design optimization is performed with conducting a dynamic analysis using a large-scale FE model many times. Exploiting Dynamic Structuring (DS) leads to alleviating the computational complexity since DS necessities iterative reanalysis of only the substructure(s) to be optimally designed. In this research, FRF Based Substructuring (FBS) is implemented to realize the benefits of DS for fast single- and multi-objective evolutionary design optimization. Also, Differential Evolution (DE) is first combined with two sorting approaches of NSGA-II and Infeasibility Driven Evolutionary Algorithm (IDEA) for effective constrained single- and multi-objective evolutionary optimization. The effectiveness of the proposed algorithm (NSGA-II/DE-IDEA) is verified using several test functions for constrained single- and multi-objective optimization. To circumvent the need for frequent time-consuming simulation runs, Kriging surrogate models are established by interpolating the responses simulated at the sample points, which are generated by executing an Optimal LHS algorithm. Besides, the Morris method is implemented to leave out unimportant design variables. A constrained single-objective and a constrained multi-objective NVH design optimization of a truck are carried out to demonstrate the surrogate-based design optimization process involving FBS and the proposed algorithm. The stiffness of the bushings placed in between the frame and the cabin and the thickness of the frame parts are optimally designed with the goal of minimizing the road-induced noise level in the single-objective optimization problem and the mass of the frame as well as the noise level in the multi-objective optimization problem subject to multiple constraints.
Inseok Park, Dimitrios Papadimitriou
Beta CAE Systems USA Inc., Oakland University