VEHICLE-TO-VEHICLE FULL FRONTAL CRASH OPTIMIZATION USING A CAE-BASED METHODOLOGY

This paper describes a CAE-based methodology used to identity major factors influencing vehicle structural performance and crash energy management in full-frontal vehicle-to-vehicle collisions. Finite element models of an “average” SUV and an “average” full-size passenger vehicle were used in this study. The determining factors of vehicle compatibility in multi-vehicle collisions are relative mass, relative stiffness and relative geometry. Four parameters of the average SUV, mass, fore rail length, fore rail thickness, and fore rail height were selected as design variables. A uniformly spaced Optimal Latin Hypercube sampling technique was employed to probe the design space of these variables using thirteen simulation runs.

Dash intrusions in the passenger vehicle and the absorbed collision energy in both vehicles were selected as response variables. Polynomial response surfaces were constructed, based on the simulation results, and found to fit the results well (R²= 0.98 for dash intrusion and R²= 0.85 for absorbed energy). As a result, prediction equations for maximum dash intrusion and absorbed collision energy as a function of the vehicle design variables were obtained. Results indicated that aligning front-end structures (specifically fore rail heights between impacted vehicles) in vehicle-to-vehicle full-frontal collisions has greater effect on reducing dash intrusions and managing crash energy than mass and variables associated with stiffness. An optimal design solution could also be determined with the appropriate introduction of constraint conditions.