基于自适应随机抽样敏度分析的双向渐进结构优化方法

为了解决周期循环结构拓扑优化难题,在传统的双向渐进结构优化方法(BESO)的基础上,引入了一种自适应参数策略和随机抽样的方法,提出了基于自适应随机抽样敏度分析的双向渐进结构优化方法,该方法同样适用于非周期结构.以该方法为基础,对Michell桁架结构进行了拓扑优化设计,得到了与理论解一致的结构,并且相对初始结构质量减少了71.5%,说明了所提方法的正确性.基于此方法对多辐板风扇盘进行了结构拓扑优化设计,得到了三辐板风扇盘结构,相比同等设计条件下的参考风扇盘质量减少了17.12%,进一步说明了此方法具有处理复杂周期循环结构拓扑优化设计问题的能力,另外此方法克服了传统双向渐进结构优化方法中容易产生的振荡问题.

Bi-directional evolutionary structural optimization method based on self-adaption and random sampling sensitivity analysis

In order to solve the issue of periodic structural topology optimization with design-dependence,the self-adaption strategy and random sampling method were developed,and a topology optimization approach called bi-directional evolutionary structural optimization(BESO) using self-adaption and random sampling sensitivity analysis was proposed based on traditional BESO method.It is also suitable for the non-periodic structures.Based on the approach,the Michell truss structure topological optimization design was carried out,and the optimum structure was obtained in consistence with the theoretical solution.The mass was reduced by 71.5%,compared with the initial structure,showing the proposed method is reasonable;moreover,the multi-web fan disk was designed by the above method,and a new three-web fan disk was obtained.The mass declined by 17.21% in relation to the reference fan disk.The results further illustrates that the method has the ability to cope with the complex issue of periodic structural topology optimization.Additionally,the prone oscillation problems of classical BESO method has been overcome by the algorithm.