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Efficient Surrogate-Based NVH Optimization of a Full Vehicle Using FRF Based Substructuring
ISSN: 2641-9637, e-ISSN: 2641-9645
Published April 14, 2020 by SAE International in United States
Citation: Park, I. and Papadimitriou, D., "Efficient Surrogate-Based NVH Optimization of a Full Vehicle Using FRF Based Substructuring," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(3):1429-1442, 2020, https://doi.org/10.4271/2020-01-0629.
The computer simulation with the Finite Element (FE) code for the structural dynamics becomes more attractive in the industry. However, it normally takes a prohibitive amount of computation time when design optimization is performed with running a large-scale FE simulation 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, Frequency Response Function (FRF) based substructuring 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 Non-dominated Sorting Genetic Algorithm II (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 FRF based substructuring and the proposed algorithm.