摘要
多学科设计优化过程中需要多次调用高精度学科分析模型,从而造成计算复杂性问题。为解决上述计算复杂性问题,首先提出基于最优LHD的逆序贯试验设计方法,在此基础上结合模型验证,建立了基于Kriging函数的序贯近似建模方法,提出了序贯近似建模收敛准则,通过权衡训练样本数据数量和近似精度,得到精度与效率折衷的近似模型。数学算例证明了该方法的有效性。
In the process of multidisciplinary design optimization, there exits the problem of calculation complexity due to frequently calling high fideity disciplinary analysis model. For solving this problem, the sequential approximation-modeling method using Kriging function is established. This paper first puts forward anti-sequential experiment design method based on optimal Latin Hypercube Design, then develops Kriging function-based sequential approximation modeling method by taking into account model validation, thus the sequential approximation modeling convergence metric is set up, and the high-fidelity and efficient approximate model can be gained by tradeoff between sampling number and approximation precision. Mathematical example demonstrates the validity of this method.
出处
《机械设计与研究》
CSCD
北大核心
2007年第3期6-10,13,共6页
Machine Design And Research
基金
国家"863"计划资助项目(2005AA765030)
关键词
计算复杂性
Kriging函灵符
序贯近似建模
近似模型
模型验证
试验设计方法
computational complexity
kriging function
sequential approximation modeling
approximate mod-el
model validation
experiment design method
