【作者】 江春冬； 贾海朋； 杜太行； 张磊； 江春波；

【Author】 Jiang Chundong 1,Jia Haipeng 1,2,Du Taihang 1,Zhang Lei 1,Chunbo Jiang 3 1.Hebei University of Technology,Tianjin 300130,P.R.China 2.China Coal Zhangjiakou Coal Mining Machinery Cooperation,Zhangjiakou 075025,P.R.China 3.Tsinghua University,Beijing 100084

【机构】 河北工业大学； 中煤张家口煤矿机械有限责任公司； 清华大学；

【摘要】 本文提出了一种用于结构拓扑优化的改进的智能计算算法,它综合了进化结构优化算法(ESO)和原来结构拓扑优化中所用的水平集方法(LSM)的优点。在LSM方法基础上,结合了ESO算法,克服了原有的LSM方法在结构优化过程中不能产生新孔及结果拓扑极大地依赖原始结构拓扑的缺点。所提算法在优化迭代过程中,能更好地选择相邻区域的节点并自由地增加新孔,从而降低变形能。该方法特别适用于那些预先无法准确确定孔的数目和位置的复杂结构的优化中,可提高原有的LSM方法在优化过程中的搜索能力。本文通过实例验证了所提方法的有效性和高效性。更多还原

【Abstract】 This paper proposes a bio-inspired evolutionary computational algorithm for structure topology optimization.It integrates the merits of evolutionary structure optimization and level set method for structure topology optimization. Traditional Level Set Method algorithm depends on the initialil topology to some extent.New holes cannot be evolved within the updated topology during the optimization iteration.The algorithm proposed in this paper combines the merits of ESO techniques with that of LSM scheme,allowing new holes to be automatically inserted in regions with low deformation energy at prescribed iterations of the optimization.The nodal neighboring region is adopted in the proposed algorithm.Numerical example shows that good result can be gotten through less optimization iteration.The approach can solve complex structures easily,in which holes cannot be properly layed out in advance.The proposed method considerably improves the ability of LSM to find the optimal topology.The validity and efficiency of the proposed algorithm are supported by some benchmark examples in this paper.更多还原