一种改进的三维流场数据可视化方法

Aiming at the problem of display confusion that easily occurs in the visualization of three-dimensional flow field data, an adaptive method for three-dimensional flow field visualization is proposed. This method introduces a fuzzy support vector machine to conduct classification preprocessing on the flow field characteristics and proposes an improved line integral convolution algorithm. This algorithm adaptively generates Sobol sparse noise using the membership degree obtained by the fuzzy support vector machine, avoiding the overlap phenomenon when the noise is too dense and missing important detailed information of the flow field when the noise is too sparse. Through the comparison and analysis of multiple sets of three-dimensional flow field visualization simulation experiments, the effectiveness of the method in this paper is verified.

针对三维流场数据可视化容易出现的显示混乱问题,提出一种三维流场可视化自适应方法。该方法引入模糊支持向量机对流场特征进行分类预处理,并提出一种改进的线积分卷积算法,该算法利用模糊支持向量机得到的隶属度自适应生成Sobol稀疏噪声,避免当噪声过于稠密时产生重叠现象,而当噪声过于稀疏时漏掉流场重要的细节信息。通过多组流场三维可视化仿真实验的比较与分析,验证了本文方法的有效性。