Optimization of Rollover Crashworthiness and Vehicle Mass Based on Unreplicated Saturated Factorial Design

Abstract

In the realm of van design, researchers have been diligently working to enhance rollover crashworthiness while concurrently achieving lightweight body structures. Unlike front and side impacts, rollover crashworthiness is impacted by a greater number of structural dimensions and material parameters. As such, this paper implements an unreplicated saturated factorial design to conduct factor screening for vehicle rollover crashworthiness. This approach effectively and accurately resolves the screening challenges that arise from large numbers of factors, and eliminates dependence on traditional design experience. Consequently, it shortens the design cycle and reduces development costs. In addition, this paper establishes four Kriging approximate models that describe the specific energy absorption and total mass of the key body structure, the displacement of the roof, and the maximum angular velocity of the body’s center of mass. To address the multi-objective optimization problem of improving rollover crashworthiness while reducing mass, this paper combines the particle swarm optimization algorithm with the artificial immune algorithm. This hybrid algorithm converges rapidly, and the Pareto solution set exhibits superior uniformity and diversity. Finally, the shortest distance method is employed to identify the optimal design scheme that can enhance the rollover crashworthiness of vans and reduce the mass of body parts.

Keywords

• vans’ rollover crashworthiness
• weight reduction
• unreplicated saturated factorial design
• multi-objective optimization
• hybrid optimization algorithm