摘要
介绍了一种基于蒙特卡罗表述的空间缩减策略和局部边界线搜索的序列采样算法,该算法利用已有样本点的信息缩减原有设计空间,使得在缩减设计空间上生成的新样本点能够同时具有良好的空间填充特性和投影特性.与已有的序列采样算法的比较结果表明,该算法具有较高的采样效率和采样质量.采用此序列采样算法结合Kriging模型和遗传算法进行轮盘减质优化,优化结果减质10%.该序列采样算法为工程结构的优化提供了一条灵活有效的途径.
A sequential sampling algorithm based on Monte Carlo-based space reduction and local boundary search was introduced. This algorithm utilized the information of the current samples to reduce the design space in order to generate new samples with better space- filling and projective properties. The comparative results with existing sequential sampling algorithm demonstrate that this algorithm can efficiently generate better samples. This sequential sampling algorithm combined with Kriging model and genetic algorithm was used in the mass optimization of turbine disk, obtaining mass reduction of 10%. The results show that this sequential sampling algorithm provides a flexible and efficient approach for the engineering structure optimization.
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2014年第9期2097-2103,共7页
Journal of Aerospace Power
基金
辽宁省博士启动基金(20131019)
国家重点基础研究发展计划(2009CB724303)
中央高校基本科研业务费专项资金(DUT14QY36)
关键词
空间缩减
序列采样
代理模型
轮盘
减质优化
space reduction
sequential sampling
surrogate models
turbine disk
mass optimization
