基于融合邻域规则的双尺度拓扑优化新方法  

A New Method of Dual-Scale Topology Optimization Based on Fusion of Neighborhood Rules

作　　者：王震 Wang Zhen(School of Construction Machinery,Chang’an University,Xi’an 710064,China)

机构地区：[1]长安大学工程机械学院,陕西西安710064

出　　处：《南方农机》2025年第3期147-149,159,共4页

摘　　要：【目的】开发一种创新的双尺度拓扑优化策略,应用于结构设计与优化研究。【方法】结合混合元胞自动机(HCA)与双向渐进法(BESO),推出HCA-BESO拓扑优化方法,其核心创新点在于采用HCA的邻域规则替代传统基于距离的加权处理机制,简化了灵敏度过滤规则,并引入了多样的邻域组合形式。【结果】通过引入HCA,该方法在双尺度优化方面显著提升了性能,减少了迭代次数,相比传统BESO算法,能够创建出刚度更高的结构。【结论】HCA-BESO方法代表了双尺度拓扑优化研究的一个重大进步,为未来的结构设计与优化研究开辟了新的路径。[Objective]To develop an innovative two-scale topology optimization strategy for structural design and optimization.[Method]The HCA-BESO topology optimization method was proposed by combining the bi-directional asymptotic method(BESO)and hybrid cellular automata(HCA).The core of the hca-beso topology optimization method was to replace the traditional weighted processing mechanism based on distance with the neighborhood rules of HCA,simplify the sensitivity filtering rules,and introduce a variety of neighborhood combinations.[Result]By introducing HCA,the method can significantly improve the performance in two-scale optimization and reduce the number of iterations.Compared with the traditional BESO algorithm,it can create a structure with higher stiffness.[Conclusion]The HCA-BESO method represents a significant progress in the research of two-scale topology optimization,and opens up a new path for the future research of structural design and optimization.

关 键 词：双尺度优化 双向渐进法(BESO) 混合元胞自动机(HCA) 灵敏度过滤 

分 类 号：TH122[机械工程—机械设计及理论]