A level set topology optimization design method considering structural size constraints

By identifying the structural skeleton through the symbolic distance function and morphological operations, and combining the velocity field level set model and MMA algorithm, the problem of not considering structural size constraints in topology optimization is solved, and the accurate calculation of structural size and the manufacturability of optimized design are realized.

CN117034511BActive Publication Date: 2026-07-24HUAZHONG UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HUAZHONG UNIV OF SCI & TECH
Filing Date
2023-06-09
Publication Date
2026-07-24

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Abstract

The present application belongs to the technical field of structural topology optimization, and discloses a level set topology optimization design method considering structural size constraints. The method comprises the following steps: S1, for a to-be-optimized structure, identifying a center axis in the to-be-optimized structure as a coarse skeleton of the to-be-optimized structure, performing morphological operation on the coarse skeleton to obtain a fine skeleton, calculating the nearest distance from each point in the to-be-optimized structure to the fine skeleton to obtain a distance field, and constructing a maximum size constraint and a minimum size constraint of the to-be-optimized structure according to the distance values in the distance field; S2, taking the minimum flexibility of the to-be-optimized structure as the target, and taking the volume constraint, the maximum size constraint and the minimum size constraint of the to-be-optimized structure as the constraint conditions, establishing a velocity field level set topology optimization design model, and solving the optimal solution of the model to realize the level set topology optimization of the to-be-optimized structure. Through the present application, the maximum and minimum sizes of the to-be-optimized structure are effectively controlled, and the manufacturability of the optimization design result is increased.
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