Design optimization of a runflat structure based on multi-objective genetic algorithm

Runflat structure plays an important role in determining the sustainable mileage after the tire is shot. Lightweight, stiffness and strength are highly relevant to the overall performance of the structure. A parameterized model was built based on the full study of the structure, and a new adaptive meshing method is proposed to ensure the quality of the model. The accuracy of the new model was verified by comparing to the traditional finite element model. The parameter study was carried out to investigate the response of the performance and mass. Multi‐objective optimization model was established by applying optimal Latin square design method and response surface model approach. Non‐dominated sorting genetic algorithm‐II (NSGA‐II) was applied to obtain the optimization design. The results indicate that the combination of parameterized model and multi‐objective genetic algorithms successfully achieve the goal of multi‐objective optimization for mass and displacement while ensuring the stress. Meanwhile, the optimal topology, shape and thickness optimization for the runflat structure have been achieved at the same time.