Magnitude-based streamlines seed point selection for unsteady flow visualization

Flow visualization is a method utilized to obtain information from flow data sets. Proper blood flow visualization can assist surgeons in treating the patients. However, the main problem in visualizing the blood flow inside the aorta is the unsteady blood flow rate. Thus, an unsteady flow visualization method is required to show the blood flow clearly. Unfortunately, streamlines cannot be used by time-dependent flow visualization. This research aims to propose an improvement for the current streamline visualization technique and appearance by implementing an improved streamline generation method based on structured grid vector data to visualize the unsteady flow. The research methodology follows a comparative study method with the Evenly-Spaced Seed Point placement (ESSP) method as the benchmark. Magnitude-Based Seed Point placement (MBSP) and selective streamlines enhancement are introduced to produce longer, uniform, and clutter-free streamlines output. A total of 20 visualization results are produced with different streamlines separation distance. Results are then evaluated by comparing streamlines count and uniformity score. Subsequently, survey and expert reviews are carried out to strengthen the analysis. Survey questions are distributed to respondents that have data visualization knowledge background in order to get feedback related to streamlines uniformity and enhancement. In addition, experts review is conducted to get feedback based on current researches and techniques utilized in the related fields. Results indicate that streamlines count for MBSP are higher, but the differences are neglectable. Uniformity analysis shows good performance; with 80% of the MBSP results have better uniformity. Survey responses show 65% of respondents agreed MBSP results have better uniformity compared to ESSP. Majority of the respondents (92%) agreed that selective streamlines is a better approach. Experts review highlights that MBSP can distribute streamlines better in 3-dimension space compared to ESSP. Two significant findings are identified in this research: magnitude is proven to be an important input to locate seed points; and selective streamlines enhancement is a more effective approach as compared to global streamlines enhancement

Multivariate Pointwise Information-Driven Data Sampling and Visualization

With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.Comment: 25 page

Coherent view-dependent streamline selection for importance-driven flow visualization

Streamline visualization can be formulated as the problem of streamline placement or streamline selection. In this paper, we present an importance-driven approach to view-dependent streamline selection that guarantees coherent streamline update when the view changes gradually. Given a large number of randomly or uniformly seeded and traced streamlines and sample viewpoints, our approach evaluates, for each streamline, the view-dependent importance by considering the amount of information shared by the 3D streamline and its 2D projection as well as how stereoscopic the streamline\u27s shape is reflected under each viewpoint. We achieve coherent view-dependent streamline selection following a two-pass solution that considers i) the relationships between local viewpoints and the global streamline set selected in a view-independent manner and ii) the continuity between adjacent viewpoints. We demonstrate the effectiveness of our approach with several synthesized and simulated flow fields and compare our view-dependent streamline selection algorithm with a naïve algorithm that selects streamlines solely based on the information at the current viewpoint. © 2013 SPIE-IS&T