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

Abstract

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. Multiobjective 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.

Parameter Optimization of a Potted Seedling Tray Prepared from a Mixture of Rice Straw and Fermented Cow Manure Using the Response Surface Methodology

“…With the increase of the order, the ability to simulate a nonlinear model becomes stronger. Among them, the response surface model with the second-order polynomial approximation has a higher computational accuracy and problem-solving efficiency . In the current research, to fit the experimental data, the below second-order polynomial approximation was used …”

“…Among them, the response surface model with the second-order polynomial approximation has a higher computational accuracy and problem-solving efficiency. 31 In the current research, to fit the experimental data, the below second-order polynomial approximation was used. where y refers to the objective function, β ij , β ii , β i , and β 0 are the polynomial coefficients, x i and x j indicate the independent variables, and k denotes the number of independent variables.…”

Crashworthiness analysis and multi-objective optimization of a commercial vehicle frame: A mixed meta-modeling-based method

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The multi-objective optimization of a non-pneumatic wheel based on its life prediction

“…Kumar’s research also extracted the force and moment (F&M) characteristics and compared them with those of a passenger car tire (Kumar and Kumar, 2016). A multi-objective optimization model of a non-inflatable, explosion-proof wheel was established using the optimal Latin design method and a response surface model developed by Zhou G. et al Moreover, the wheel’s structure was optimized through the NSGA-II multi-objective genetic algorithm (Zhou et al , 2015). Based on the static behavior of three types of non-pneumatic tires with the same honeycomb cell wall thickness, the static bearing capacity of honeycomb tires was further studied by Jin, X. et al The results showed that the maximum stresses in a non-pneumatic tire’s spokes and tread were considerably lower than for traditional pneumatic tires with a higher loading capacity (Jin et al , 2018).…”