Abstract:

An optimizational design was investigated at vehicle frontal crash cases for a multi-purpose vehicle (MPV) at 40% offset frontal crash to improve the optimization accuracy and efficiency. A modified Efficient Global Optimization (EGO) algorithm was built based on Kriging model considering the improved effect of
sequence samples of target response and constraints with a new established sequential sampling process at constraint conditions of intrusion and deformation in vehicle crashes. The results show that using the proposed modified EGO algorithm has a mininal sample number of 112 and an error of less than 8.42% with the target acceleration reducing from 28.48 g to 26.77 g in the case of crash, compared with the algorithm without considering sequential sampling, Jones classical EGO sequential sampling algorithm and Schonlau constraint EGO sequential sampling algorithm; while all the other constrained crash performances meet the requirements with reduced mass of 2.89 kg. Therefore, the accuracy and efficiency of the method are verified.

Key words: vehicle development, vehicle crash, metamodel prediction error, Kriging model, efficient global optimization (EGO) algorithm, modified EGO algorithm