CAREER: Effective Analysis, Exploration and Visualization of Big Flow Data to Understand Dynamic Flows

职业：有效分析、探索和可视化大流量数据以了解动态流量

• 批准号: 1349462
• 负责人: Chaoli Wang
• 金额: $ 48.92万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1349462&HistoricalAwards=false
• 关键词: CAREER; Effective; Analysis; Exploration; Visualization

The ever-growing size and complexity of flow data produced from many scientific, engineering and medical simulations pose significant challenges which are not thoroughly addressed by existing visualization techniques. These challenges include computation, interaction, visualization and user challenges. Addressing the computation challenge is a central research focus and remains a prominent direction in the field, while the other challenges are often overlooked. The goal of this CAREER project is to address these less investigated challenges by pioneering a comprehensive framework toward effective visual understanding of flow fields. It contributes to the state of the art flow visualization by promoting an innovative database approach to shape-based field line modeling and classification, investigating new string-, sketch- and graph-based interfaces and interactions for flow field exploration, and exploring occlusion and clutter reduction through unconventional streamline repositioning and automatic tour generation. The general approach developed in this research is expected to substantially improve our ability to visually understand a wide spectrum of flow fields, ranging from the traditional application of fluid flows to new applications such as traffic flows, cash flows and message flows. This project will provide training for graduate and undergraduate students in the area of data visualization and scientific computing via capstone class projects. A pedagogical toolbox will be designed along with web-based resources to support teaching visualization classes through expressive demos, potentially benefiting universities nationwide with a similar teaching need. The PI will continue to attract underrepresented students through university and department outreach programs and engage local middle and high school students through summer youth programs. This research tackles the fundamental challenges in visualizing large, complex three-dimensional steady and unsteady flow fields. Underlying the proposed work is a novel database approach to field line shape encoding, classification and interrogation. The PI will integrate and unify a variety of concepts from geometric modeling, computer vision and data mining to create robust visual characters and words from field lines for shape analysis and organization. Novel interfaces and interactions will be introduced to enable intuitive retrieval of partial field lines via textual and visual forms, and examination of hierarchical field lines and their spatiotemporal relationships in the transformed graph space. Innovative streamline repositioning for focus+context viewing and automatic tour for examining hidden or occluded flow features will be devised to move from clutter to clarity in the visualization. The success of this research will benefit a wide variety of applications within and beyond graphics and visualization, such as shape analysis, visual perception, database organization, game development, and visualization in education.The PI will collaborate with scientists and researchers at university, industry and national labs, applying the proposed solutions to solve real-world problems. Research results will be evaluated through both domain expert reviews and formal user studies. Selected research outcomes will be integrated into user-engaging educational applications that will be run on tablet devices and delivered to the general public for wide dissemination. This CAREER project will build a solid foundation for addressing key challenges in flow visualization, and lead to multidisciplinary collaborations spanning atmospheric cloud, combustion chemistry and cardiovascular research. It will also produce fruitful deliverables, featuring the first-ever benchmark field line shape database, tutorials and workshops at premier visualization conferences, and pedagogical tools and game apps.

由许多科学、工程和医学模拟产生的不断增长的流数据的大小和复杂性带来了巨大的挑战，而现有的可视化技术并没有彻底解决这些挑战。这些挑战包括计算、交互、可视化和用户挑战。解决计算挑战是一个中心研究焦点，也是该领域的一个重要方向，而其他挑战往往被忽视。这个职业项目的目标是通过开创一个全面的框架来有效地直观地理解流场，来解决这些较少被研究的挑战。它通过促进基于形状的场线建模和分类的创新数据库方法，研究用于流场探索的基于字符串、草图和图形的新界面和交互，以及探索通过非传统流线重新定位和自动漫游生成来减少遮挡和杂乱，从而促进了流动可视化的发展。这项研究中开发的一般方法预计将显著提高我们从视觉上了解广泛的流场的能力，从传统的流体流动应用到交通流、现金流和消息流等新应用。该项目将通过顶石课程项目为研究生和本科生提供数据可视化和科学计算领域的培训。将设计一个教学工具箱和基于网络的资源，通过富有表现力的演示支持可视化课程的教学，可能使全国有类似教学需求的大学受益。国际学生联合会将继续通过大学和系外展计划吸引代表性不足的学生，并通过暑期青年计划吸引当地初中生和高中生。这项研究解决了可视化大型、复杂的三维定常和非定常流场的基本挑战。拟议工作的基础是一种新的数据库方法，用于对场线形状进行编码、分类和询问。PI将整合和统一几何建模、计算机视觉和数据挖掘的各种概念，以从现场线创建健壮的视觉字符和单词，用于形状分析和组织。将引入新的界面和交互，以实现通过文本和视觉形式直观地检索部分场线，并检查变换后的图形空间中的分层场线及其时空关系。用于焦点+上下文查看的创新流线重新定位和用于检查隐藏或被遮挡的流动特征的自动漫游将被设计为在可视化中从混乱变为清晰。这项研究的成功将使图形和可视化内外的广泛应用受益，如形状分析、视觉感知、数据库组织、游戏开发和教育中的可视化。PI将与大学、工业和国家实验室的科学家和研究人员合作，将提出的解决方案应用于解决现实世界的问题。研究结果将通过领域专家审查和正式的用户研究进行评估。选定的研究成果将被整合到用户参与的教育应用程序中，这些应用程序将在平板电脑设备上运行，并提供给普通公众广泛传播。这个职业项目将为解决流动可视化方面的关键挑战奠定坚实的基础，并导致跨越大气云、燃烧化学和心血管研究的多学科合作。它还将产生富有成果的成果，包括有史以来第一个基准场线形状数据库、主要可视化会议的教程和研讨会以及教学工具和游戏应用程序。