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Surrogate model based design optimization has been widely adopted in automotive industry. Hybrid surrogate model with multiple component surrogates is considered to be a better choice when simulating highly non-linear responses in vehicle crashworthiness analysis. Currently, the number of component surrogates has to be decided before-hand when constructing of a hybrid surrogate model. This paper conducts a comparative study on the performances of three popular hybrid modeling methods including heuristic computation strategy, and two kinds of optimal weighted surrogates. The effects of samples size on the number of individual surrogates that should be included into the final hybrid surrogate models for crashworthiness responses are investigated. Different hybrid modeling techniques and multiple validation criteria are evaluated. Some observations and conclusions on the selection of component surrogates in hybrid surrogate modeling are given in the end.

Finite element (FE) models are commonly used for automotive crashworthiness design. However, even with increasing speed of computers, the FE-based simulation is still too time-consuming when simulating the complex dynamic process such as vehicle crashworthiness. To improve the computational efficiency, the response surface model, as the surrogate of FE model, has been widely used for crashworthiness optimization design. Before introducing the surrogate model into the design optimization, the surrogate should satisfy the accuracy requirements. However, the bias of surrogate model is introduced inevitably. Meanwhile, it is also very difficult to decide how many samples are needed when building the high fidelity surrogate model for the system with strong nonlinearity. In order to solve the aforementioned problems, the application of a kind of surrogate optimization method called Efficient Global Optimization (EGO) is proposed to conduct the crashworthiness design optimization.