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

在过去的十年中，计算机的能力得到了极大的提高，使得计算流体动力学（CFD）研究人员能够更准确地模拟多种类型的复杂流动。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取得了巨大的飞跃。然而，这种新功能也产生了tb级的数据，CFD研究人员现在面临着一项非常困难的任务，即试图找到、提取和分析隐藏在这些庞大数据集中的重要流动特征（例如，时变涡流、冲击波）。与计算机能力的爆炸式增长不同，用于超大数据集的可视化工具已经适度发展，目前还不能显著地帮助完成这些任务。特别是，由于如此庞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层次上工作，将他们的数据集分成可行的部分。CFD研究人员迫切需要新技术来简化和自动化查找数据集适当部分的迭代过程。他们需要一个系统，允许用户清晰地表达感兴趣的适当类型的特征，提供这些特征的紧凑表示，并在局部有效地可视化特征信息。系统必须克服以下挑战：通过网络连接将数据集的足够部分加载到桌面机器中，将整个数据集映射为可视化表示，并以交互速率呈现结果。本项目将通过开发用于创建和使用数据集的过程抽象的技术来解决这些CFD可视化问题。主要的研究目标是：1。使用拓扑算子检测复杂流中的特征（如冲击）。使用由隐式模型和自由形式变形组成的过程表示来描述与这些特征相关的数据。使用多分辨率技术将程序表示调整到适当的细节级别。将特定领域的知识封装为元数据，以探索这些超大的数据集。直接从过程表示中可视化数据。通过跟踪逼近误差验证程序表示的准确性。将这些技术应用于斯坦福大学和密西西比州立大学目前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，通过使用程序化的数据抽象和表示，在更高、更有效的层次上进行可视化探索、特征检测、提取和分析。

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.