Automotive Crashworthiness Design Optimization Based on Efficient Global Optimization Method 2018-01-1029
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. Based on few samples, the initial Kriging models are constructed. Then the new sample found by the expected improvement criterion (EI) is employed to update the Kriging models in each subsequent loop iteration. Since the expected improvement criterion can balance the global search and local search, a global optimal design will be found after several iterations. Thus, EGO will reduce the number of computationally expensive evaluations while achieving the desired optimal design. The application of the EGO on vehicle crashworthiness design is demonstrated through a case of vehicle low speed crash design. And a comparison study between the EGO and traditional surrogate model based crashworthiness design optimization method indicates that the EGO method is more effective in crashworthiness design.