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This paper is on the application of reliability-based design optimization of automotive structures. Several foundmental approaches aimed at simulations, optimization and robustness analysis are discussed. They include: Sampling techniques and nonlinear response surface methodologies; Optimization and robustness assessment. Basic approaches of reliability-based design optimization such as Hasofer-Lind method, single loop single vector method and mean value first order reliability method; Robust design formulation. The major focus is on the implementation of reliability-based design optimization methodologies to vehicle crash safety design. Some recent applications are presented to demostrate how these approaches can be used for vehicle structure design.

The optimization method and CAE analysis have been widely used in structure design for crash safety. Combining the CAE analysis and optimization approach, vehicle structure design for crash can be implemented more efficiently. One of the recent safety desirables in structure design is to reduce vehicle pitch and drop. At frontal impact tests with unbelted occupants, the interaction between occupant's head and interior header/sun visor, which is caused by excessive vehicle pitch and drop, is not desired in vehicle crash development. In order to comply with the federal frontal crash requirements for unbelted occupant, it is necessary to manage the vehicle pitch and drop by improving structure design. In this paper, a systematic process of CAE analysis with optimization approach is applied for discovering the major structural components affecting vehicle pitch and drop.

This paper describes the development of a Bayesian estimate of vehicle safety performance. The vehicle crash testing is conducted often using a very small sample size. With these limited tests, one often has to face the following question, “what is the confidence to meet the design target or government compliance in a subsequent test?” The prediction methods will be discussed to determine the confidence in meeting overall the design requirements based on successful test results with multiple responses and design targets.

In vehicle safety engineering, it is important to determine the severity of occupant injury during a crash. Computer simulations are widely used to study how occupants move in a crash, what they collide during the crash and thus how they are injured. The vehicle motion is typically defined for the occupant simulation by specifying a crash pulse. Many computer models used to analyze occupant kinematics do not calculate both vehicle motion and occupant motion at the same time. This paper presents a framework of response surface methodology for the crash pulse prediction and vehicle structure design optimization. The process is composed of running simulation at DOE sampling data points, generating surrogate models (response surface models), performing sensitivity analysis and structure design optimization for time history data (e.g., crash pulse).