Front Underride Protection Devices (FUPDs): Multi-Objective Optimization

This work investigates a multi-objective optimization approach for minimizing design parameters for Front Underride Protection Devices (FUPDs). FUPDs are a structural element for heavy vehicles to improve crashworthiness and prevent underride in head-on collision with another vehicle. The developed dsFUPD F9 design for a Volvo VNL was subjected to modified ECE R93 testing with results utilized in the optimization process. The optimization function utilized varying member thickness to minimize deformation and system mass. Enhancements to the function were investigated by introducing variable materials and objectifying material cost. Alternative approaches for optimization was also needed to be explored. Metamodel-based and Direct simulation optimization strategies were compared to observe there performance and optimal designs. NSGA-II, SPEA-II Genetic Algorithms and Adaptive Simulated Annealing algorithms were under investigation in combination with three meta-modeling techniques. Leapfrog LFOPC algorithm hybridized forms of Genetic Algorithms and Adaptive Simulated annealing was also investigated. Crash worthiness of the optimal design was analyzed through varying collision scenarios using FEA models of a Toyota Yaris and Ford Taurus in LS-DYNA with the aid of occupant compartment intrusion evaluations and collision compatibility profiles. Direct simulation optimization with a NSGA-II approach improved the dsFUPD F9 design. This approach reduced the system mass and cost, while maintaining the modified ECE R93 requirements and crashworthiness. In final, the addition of various materials and a cost objective to the multi-objective optimization function improved the minimization of search space and progression for optimal design. Future works should investigate material selection for crashworthiness and cost effectiveness for FUPDs.