Extraction And Visualization For Complex Flow Field Features

Flow visualization is an important topic in scientific visualization, and plays an important role in scientific computation and engineering analysis. There are some typical and universal feature structures in flow field. Feature-based approaches have been developed to extract flow features to eliminate the redundant and uninterested data, which can reduce the final rendering data while maintaining the correct information. This advantage makes feature-based approaches an excellent method compared with others to visualize flow fieldHow to describe and extract feature structures is the main problem in visualizing flow fields, and there are lots of papers try to solve this problem. However, the existing methods mainly aim at a certain class of features, such as vortices, which lack universality and extensibility. Texture-based methods can represent the continuous variability very well, but it is difficult for users to discover and analyze the inner feature structures due to the occlusion problem. To solve this problem, this paper investigates the related theories about flow feature description, extraction and visualization. Based on the extracted features, this paper adopts texture-based methods and obtains a high quality flows representation. The main research achievements are detailed as follows:(1) It is incapable of depicting the scope of the flow features for the topology-based methods, whereas the existing feature analysis methods are difficult to define the feature boundary for the vector direction of the flows. To solve this problem, this paper presents a feature extraction approach based on these two kinds of methods, which can define the feature scope in 2D flow fields reasonably without the user's selection. The experiments show that this approach can define the scope reasonably and describe the trend of flow features in the time-dependent fields very well.(2) To solve the universality and extensibility deficiency of the existing feature extraction methods, this paper proposes an intelligent feature extraction approach based on the back propagation network, which can make full use of the powerful non-linear description ability of the network. The proposed approach is implemented on GPU to improve the performance. The experiment results show that the approach proposed has favorable universality and extensibility, which can extract the typical and new-come local features very well.(3) To solve the problem of describing and extracting the 3D flow features accurately, this paper proposes a universal description approach based on the fuzzy theory. Based on this description, an extracted algorithm called FRFE is presented. This algorithm is proved to be optimal in the minimum square sum rule. The experiments show that the FRFE algorithm can extract the feature structures more accurately than the traditional methods. (4) Traditional menu-based interaction and certain feature extraction methods can't describe the uncertainty of the complex flow features. To solve this problem, this paper presents a fuzzy feature define language (FFDL) based on the FRFE algorithm. Furthermore, an interactive fuzzy feature extraction algorithm is proposed with the aid of the FFDL. The experiments show that this method can extract the feature structures fuzzily and can utilize the users'knowledge flexibly as well.(5) To solve the occlusion problem in the texture-based method when visualizing 3D flows, this paper proposes an adaptive sparse texture rendering method. Furthermore, two cool/warm illumination methods are presented to represent the concrete flow direction. The experiments show that the method can relieve the occlusion phenomena very well by generating a multi-resolution linear integration convolution (LIC) textures. In addition, the disadvantage for the texture-based method, i.e. the concrete direction problem, can also be solved well by the two cool/warm illumination methods.