A Shape Parameterization Method Using Principal Component Analysis in Applications to Parametric Shape Optimization
A Shape Parameterization Method Using Principal Component Analysis in Applications to Parametric Shape Optimization
复制标题
主成分分析的形状参数化方法在参数形状优化中的应用
DOI：
10.1115/1.4028273
复制
发表时间：
2014
影响因子：
3.3
通讯作者：
O. Watanabe
中科院分区：
文献类型：
--
作者：
Kazuo Yonekura;O. Watanabe
提出了一种基于主成分分析的形状参数化方法。所提出的方法被用作参数优化算法，如遗传算法（GAs）或响应面方法（RSM）的预处理工具。当使用这些参数优化算法时，参数的数量应该很小，而由参数表示的设计空间应该能够表示各种形状。为了定义参数，PCA应用于形状。在许多工业领域中，积累了大量的形状及其性能数据。通过对数据库中包括的这些形状应用PCA，提取形状的重要特征。设计空间由从所提取的特征生成的基向量定义。设计空间的维数减少而不省略重要特征。本文将每个形状离散为一组点集，并应用主元分析法，提出了一种形状离散化方法，并给出了数值算例。
This paper proposes a shape parameterization method using a principal component analysis (PCA) for shape optimization. The proposed method is used as a preprocessing tool of parametric optimization algorithms, such as genetic algorithms (GAs) or response surface methods (RSMs). When these parametric optimization algorithms are used, the number of parameters should be small while the design space represented by the parameters should be able to represent a variety of shapes. In order to define the parameters, PCA is applied to shapes. In many industrial fields, a large amount of data of shapes and their performance is accumulated. By applying PCA to these shapes included in a database, important features of the shapes are extracted. A design space is defined by basis vectors which are generated from the extracted features. The number of dimensions of the design space is decreased without omitting important features. In this paper, each shape is discretized by a set of points and PCA is applied to it. A shape discretization method is also proposed and numerical examples are provided.