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Growing demand for fuel-efficient or light weight vehicle has become a challenge for vehicle development. Upfront engineering process provides more opportunities for engineers to improve body weight efficiency. To accelerate the upfront body development process, the parametric concept modeling technology is commonly employed to generate parametric three-dimensional geometry, joints, modular components, concept welding, and finite element meshes. The topology optimization which determines the best structural layout without weight penalty has also been used during the conceptual design stage. The objective of this research is to explore the feasibility of integrating the advanced parametric concept modeling and both topology optimization and structural optimization technologies into upfront body architecture development process.

A passenger airbag is an important part of a vehicle restraint system which provides supplemental protection to an occupant in a crash event. New Federal Motor Vehicle Safety Standards No. 208 requires considering multiple crash scenarios at different speeds with various sizes of occupants both belted and unbelted. The increased complexity of the new requirements makes the selection of an optimal airbag shape a new challenge. The aim of this research is to present an automated optimization framework to facilitate the airbag shape design process by integrating advanced tools and technologies, including system integration, numerical optimization, robust assessment, and occupant simulation. A real-world frontal impact application is used to demonstrate the methodology.

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).

This paper focuses on the development of a framework of nonlinear finite element model validation for vehicle crash simulation. Integrated computational and test-based methods were discussed for validating computational models under physical, informational and model uncertaintes. Several methods were investigated to quantify transient time-domain data (functional data). The concept of correlation index was proposed to determine the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model. The methodologies developed in this paper can also be used for CAE model updating, parameter tuning, and model calibration.

This paper focuses on the methodology development and application of reliability-based design optimization to a vehicle exhaust system under noise, vibration and harshness constraints with uncertainties. Reliability-based design optimization provides a systematic way for considering uncertainties in product development process. As traditional reliability analysis itself is a design optimization problem that requires many function evaluations, it often requires tremendous computational resources and efficient optimization methodologies. Multiple functional response constraints and large number of design variables add further complexity to the problem. This paper investigates an integrated approach by taking advantages of variable screening, design of experiments, response surface model, and reliability-based design optimization for problems with functional responses. A typical vehicle exhaust system is used as an example to demonstrate the methodology.

To reduce the cost of prototype and physical test, CAE analysis has been widely used to evaluate the vehicle performance during product development process. Combining CAE analysis and optimization approach, vehicle design process can be implemented more efficiently with affordable cost. Reliability based design optimization (RBDO) formulation considers variations of input variables, such as component gauges and material properties. As a result, the design obtained by using RBDO is more reliable and robust compared to those by deterministic optimization. The RBDO process starts from running simulation at DOE sampling data points, generating surrogate models (response surface) and performing robust and reliability based design optimization on the surrogate models by using Monte Carlo simulation. This paper presents a RBDO framework in Excel enviroment.

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.