三维流场的流线深度特征学习与特征聚类

Deep Feature Learning and Feature Clustering of Streamlines in 3D Flow Fields

流场可视化指将流体运动的数据转换为视觉形式,以便更好地理解和分析流场的流动。利用流线来实现流场可视化,是当前最为热门的方法。文中提出了一种学习、聚类三维流场流线特性的方法。首先设计了一种基于卷积的自编码器来提取流线特征。该方法中的自编码器由编码器和解码器组成,其中编码器用卷积层降维的方式来提取输入流线的特征,而解码器使用转置卷积对流线特征进行上采样,以此重建流线。通过训练不断减小输入流线与重建流线的差异,可以让编码器提取到的流线特征更加准确。其次,改进了CFSFDP算法,用于流线特征聚类。针对原CFSFDP算法需要手动选取聚类中心,以及对距离参数过于敏感的缺点,改进了其指标计算方法,实现对聚类中心的自动选取,并且引入了高斯核密度估计,实现对截断距离参数的自适应计算。实验结果表明,所提方法在流线特征的学习以及聚类上具有良好的效果。

Flow field visualization refers to converting data of fluid motion into visual forms for better understanding and analysis of flow in the field.Using streamlines for flow field visualization is currently the most popular method.This paper proposes a method for learning and clustering 3D flow field streamline features.Firstly,a convolutional autoencoder-based method is designed to extract streamline features.The autoencoder in this method consists of an encoder and a decoder.The encoder uses convolutional layers to reduce the dimensions of input streamlines to extract features,while the decoder uses transpose convolution to upsample the streamline features to restore the streamlines.By continuously reducing the difference between input and restored streamlines through training,the encoder can extract more accurate streamline features.Secondly,this paper improves the CFSFDP(clustering by fast search and find of density peaks)algorithm for clustering streamline features.To address the issue of manually selecting cluster centers and the problem of sensitivity to distance parameters in the CFSFDP algorithm,this paper improves its metric calculation method,realizes automatic selection of cluster centers,and introduces adaptive calculation of truncation distance parameters using Gaussian kernel density estimation.Experimental results show that this method has good perfor-mance in learning streamline features and clustering.