Multi-objective optimisation of a vehicle energy absorption structure based on surrogate model

In order to optimize the crashworthy characteristic of energy-absorbing structures, the surrogate models of specific energy absorption (SEA) and ratio of SEA to initial peak force (REAF) with respect to the design parameters were respectively constructed based on surrogate model optimization methods (polynomial response surface method (PRSM) and Kriging method (KM)). Firstly, the sample data were prepared through the design of experiment (DOE). Then, the test data models were set up based on the theory of surrogate model, and the data samples were trained to obtain the response relationship between the SEA & REAF and design parameters. At last, the structure optimal parameters were obtained by visual analysis and genetic algorithm (GA). The results indicate that the KM, where the local interpolation method is used in Gauss correlation function, has the highest fitting accuracy and the structure optimal parameters are obtained as: the SEA of 29.8558 kJ/kg (corresponding to a=70 mm and t= 3.5 mm) and REAF of 0.2896 (corresponding to a=70 mm and t=1.9615 mm). The basis function of the quartic PRSM with higher order than that of the quadratic PRSM, and the mutual influence of the design variables are considered, so the fitting accuracy of the quartic PRSM is higher than that of the quadratic PRSM.