Jiangsu University

Libin Duan

Xin Luo

Wei Xu

Haobin Jiang

Xing Liu

The present invention provides a material-based subdomain hybrid cellular automata algorithm for solving material optimization of thin-walled frame structures, including an outer loop and an inner loop: the outer loop is to define and update the target cost for the inner loop; the inner loop is to adjust material using a PID control strategy according to the nominal flow stress of a current cell and the nominal flow stress of candidate materials, so that a current cost of the inner loop converges to the target cost. During the execution of the inner loop, the cellular material update rule based on the PID control strategy is employed to update cellular material, to define the candidate material library and the nominal flow stress, to update the nominal flow stress of current cell, to compare the nominal flow stress with the actual flow stress of each material in the candidate material library, to select the candidate material closest to the nominal flow stress as the selected material grade for the current cell and to replace the material parameters of the current cell with the mechanical parameters of the selected material. The present invention can efficiently solve nonlinear the dynamic response optimization problems containing a large number of material variables, significantly improving the robustness of the algorithm.

Material-based subdomain hybrid cellular automata algorithm for material optimization of thin-walled frame structures

A subdomain hybrid cellular automata method for solving car body thickness optimization includes an outer loop and an inner loop: the outer loop is to conduct crash finite element simulation analysis, calculate an output response, and update a cell internal energy density, and update a target mass using a penalty function method; the inner loop is mainly to adjust a cell thickness using a PID control strategy according to internal energy densities of a current cell and neighboring cells thereof, so that a current mass of the inner loop converges to the target mass; and finally the cell internal energy density distribution approaches a step target internal energy density function as much as possible. Step target internal energy density update rules are provided in the inner loop. Cell thickness update rules based on a PID control strategy are provided in the inner loop.

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Zhenjiang, China

Libin Duan