ITR/AP+IM: Procedural Representation and Visualization Enabling Personalized Computational Fluid Dynamics

ITR/AP IM：程序表示和可视化实现个性化计算流体动力学

• 批准号: 0121288
• 负责人: David Ebert
• 金额: --
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0121288&HistoricalAwards=false
• 关键词: ITR; AP+IM; Procedural; Representation; Visualization

Computer power has increased dramatically over the past decade and has allowed computational fluid dynamics (CFD) researchers to more accurately simulate many types of complex flow. These simulations have enabled great leaps forward in the design and safety of ships, airplanes, automobiles, and other vehicles. However, this new power has also yielded terabytes of data, and CFD researchers now face a very difficult task in trying to find, extract, and analyze important flow features (e.g., time varying vortices, shock waves) buried within these monstrous datasets. Unlike the explosive growth in computer power, visualization tools for very large datasets have evolved modestly and cannot yet help with these tasks significantly. In particular, since detailed visualization of such large datasets is impractical, CFD researchers must work at a very cumbersome, low level to dice their datasets into workable pieces.CFD researchers desperately need new techniques that simplify and automate the iterative process of finding the appropriate portion of their data set. They need a system that will allow the user to articulate appropriate types of features of interest, provide a compact representation of those features, and effectively visualize the feature information locally. The system will have to overcome the challenges of loading a sufficient portion of the data set over a network connection into a desktop machine, mapping the entire data set to a visual representation, and rendering the results at interactive rates.This project will attack these CFD visualization problems by developing techniques for creating and using a procedural abstraction for a dataset. The major research objectives are to:1. Detect features (e.g. shocks) in complex flows using topological operators.2. Characterize the data relative to these features using a procedural representation consisting of implicit models and free-form deformations.3. Adapt the procedural representation to the appropriate level of detail using multi-resolution techniques.4. Encapsulate domain-specific knowledge as metadata to explore these extremely large datasets.5. Visualize the data directly from the procedural representation.6. Verify the accuracy of the procedural representation by tracking approximation error.7. Apply these techniques to the large-scale computational flow simulation problems currently studied at Stanford and Mississippi State University. The resulting system will allow CFD researchers to work more effectively by interactively exploring their data to pinpoint the features of interest. Moreover, the results of this project will provide solutions not only for CFD researchers, but also for a wide variety of other visualization challenges and applications. The project's main goal is to develop techniques that allow visualization exploration, feature detection, extraction, and analysis at a higher, more effective level through the use of procedural data abstraction and representation.

在过去的十年中，计算机能力急剧增加，并允许计算流体动力学（CFD）研究人员更准确地模拟许多类型的复杂流动。这些模拟使船舶、飞机、汽车和其他交通工具的设计和安全性实现了巨大的飞跃。然而，这种新能力也产生了TB级的数据，CFD研究人员现在面临着一项非常艰巨的任务，即试图寻找、提取和分析重要的流动特征（例如，时变漩涡，冲击波）埋在这些可怕的数据集。与计算机能力的爆炸性增长不同，用于超大型数据集的可视化工具发展缓慢，尚不能显著帮助完成这些任务。特别是，由于如此大的数据集的详细可视化是不切实际的，CFD研究人员必须在一个非常繁琐的，低水平的工作，以骰子他们的数据集成可行的pieces.CFD研究人员迫切需要新的技术，简化和自动化的迭代过程中找到他们的数据集的适当部分。他们需要一个系统，将允许用户阐明适当类型的感兴趣的功能，提供这些功能的紧凑表示，并有效地可视化本地的功能信息。该系统将不得不克服的挑战，加载足够的数据集的一部分，通过网络连接到桌面机，整个数据集映射到一个可视化的表示，并呈现结果在交互rate.This项目将攻击这些CFD可视化问题，通过开发技术，创建和使用一个数据集的程序抽象。主要研究目标是：1.使用拓扑算子检测复杂流中的特征（例如冲击）。2.使用由隐式模型和自由形式变形组成的过程表示来表征与这些特征相关的数据。3.使用多分辨率技术使程序表示适应适当的细节级别。将特定领域的知识封装为元数据，以探索这些超大型数据集。5.直接从程序表示中可视化数据。6.通过跟踪近似误差来验证程序表示的准确性.将这些技术应用于目前在斯坦福大学和密西西比州立大学研究的大规模计算流模拟问题。由此产生的系统将允许CFD研究人员通过交互式地探索他们的数据来更有效地工作，以确定感兴趣的特征。此外，该项目的成果不仅将为CFD研究人员提供解决方案，还将为各种其他可视化挑战和应用提供解决方案。该项目的主要目标是开发技术，通过使用过程数据抽象和表示，允许可视化探索，特征检测，提取和分析在更高，更有效的水平。