摘要
流场可视化是对流场数据进行直观分析的一种新的可视化技术,而定向线积分卷积(OLIC)算法作为一种经典的纹理可视化方法,使用该算法能明显地观察出流场方向流动的演化。为了优化可视化效果,提出了一种基于信息熵的OLIC算法。首先,基于流场矢量数据生成基于信息熵的稀疏噪声;然后,采用斜坡卷积核函数对输入纹理进行卷积计算;最后,通过计算输出纹理图像中每一个像素点的灰度值,得到最终的OLIC纹理图像。所提算法可以根据熵值在临界点区域和非临界点区域自适应地生成流线。其中临界点区域含有流场的重要信息,选择密集绘制;而在非临界点区域则选择稀疏绘制。通过在不同区域绘制不同密度的流线,所提算法节省了计算成本;与普通OLIC算法相比,所提算法的绘制速度至少提升了18.6%;在可视化效果方面,所提算法优于普通的全局绘制,使用所提算法能更仔细地观察特征区域。
Flow field visualization is a new visualization technology for intuitive analysis of flow field data.And Oriented Line Integral Convolution(OLIC)algorithm,as a classic texture visualization method,can be used to clearly observe the evolution of flow in the direction of flow field.To optimize the visualization effect,an OLIC algorithm based on information entropy was proposed.Firstly,sparse noise based on information entropy was generated on the basis of the flow field vector data.Then,the slope convolution kernel function was used to convolute the input texture.Finally,the final texture image of OLIC was obtained by calculating the gray value of each pixel in the output texture image.In the proposed algorithm,streamlines were able to be generated in the critical point region and non-critical point region according to the entropy value adaptively.As the critical point region contained important information of the flow field,the dense drawing was selected,while in the non-critical point region sparse drawling was selected.By drawing streamlines with different densities in different regions,the algorithm can save computational cost,and the drawing speed of the proposed algorithm is increased by 18.6%at least compared with that of the ordinary OLIC algorithm.In terms of visualization effect,the proposed algorithm is superior to the ordinary global drawing,and can be used to observe feature regions more carefully.
作者
李梦依
方霞
郑红波
秦绪佳
LI Mengyi;FANG Xia;ZHENG Hongbo;QIN Xujia(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou Zhejiang 310023,China)
出处
《计算机应用》
CSCD
北大核心
2023年第4期1233-1239,共7页
journal of Computer Applications
基金
国家自然科学基金资助项目(61672462,61702455)
浙江省自然科学基金资助项目(LY20F020025)。
关键词
流场可视化
定向线积分卷积
信息熵
临界点
稀疏噪声
flow field visualization
Oriented Line Integral Convolution(OLIC)
information entropy
critical point
sparse noise
