CDS&E: Multi-scale Coherent Structure Extraction and Tracking For Modern CFD Data Analysis

CDS

• 批准号: 2102761
• 负责人: Guoning Chen
• 金额: $ 52.79万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102761&HistoricalAwards=false
• 关键词: CDS& Multi; scale; Coherent; Structure

Coherent structures are persistent and recognizable patterns that can be found in fluid flows. In turbulent flows, coherent structures are closely related to a diverse range of physical phenomena, and understanding their behavior is crucial for characterizing, predicting and controlling these flows. However, reliable identification and characterization of coherent structures is challenging due to their diversity and complex inter-relations across different space- and time-scales. This project brings together experts from both the data visualization and fluid mechanics communities to investigate novel solutions to multi-scale coherent structure extraction, separation, tracking, and visualization. It aims at significantly advancing the ability to analyze large datasets of turbulent flows stemming from computational fluid dynamic (CFD) simulations in a wide range of engineering and scientific applications. This project provides opportunities for both undergraduate and graduate students with different and diverse backgrounds to participate in the proposed research. The research outcomes can be integrated into the development of a number of undergraduate and graduate courses taught at the University of Houston. The outreach activities enabled by the proposed research help motivate more students to pursue a career in STEM related fields. To achieve an efficient and reliable analysis for large-scale turbulent flow data, this project aims to investigate a new multi-scale coherent structure representation that encodes relevant flow physics, statistics, and uncertainty information, and to develop a robust computation and exploration framework based on this new representation to support data-driven research. To enable this multi-scale analysis, this project applies a number of spatial and temporal domain decomposition strategies to the computational fluid dynamic (CFD) data. Multi-field analysis and high-dimensional data projection techniques are adapted to incorporate different physical attributes to the representation. A novel graph representation is leveraged to encode this multifaceted information in a concise and dimension-independent form to enable multi-scale feature extraction and tracking. A matrix representation of this graph is employed to accelerate its processing by utilizing the recent advances in large-scale matrix calculation. A new visual analytic paradigm is devised based on the proposed graph representation to aid the exploration and comprehension of different turbulence structures individually or collectively. The developed techniques implemented as a number of software libraries can be integrated into existing software, e.g., Paraview, for domain scientists to use in their daily research. The developed techniques can also be used as pre-processing toolboxes to quantify and extract coherent structures, which can then be visualized by existing software that are not suitable for direct library integration.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

相干结构是在流体流动中可以找到的持久且可识别的模式。在湍流中，相干结构与各种物理现象密切相关，了解它们的行为对于表征、预测和控制这些流动至关重要。然而，由于相干结构在不同空间和时间尺度上的多样性和复杂的相互关系，对其进行可靠的识别和表征具有挑战性。该项目汇集了来自数据可视化和流体力学领域的专家，研究多尺度相干结构提取、分离、跟踪和可视化的新颖解决方案。它旨在显着提高分析来自广泛工程和科学应用中的计算流体动力学 (CFD) 模拟的大型湍流数据集的能力。该项目为具有不同背景的本科生和研究生提供了参与拟议研究的机会。研究成果可以融入休斯顿大学教授的许多本科生和研究生课程的开发中。拟议研究开展的外展活动有助于激励更多学生从事 STEM 相关领域的职业。 为了实现大规模湍流数据的高效可靠分析，该项目旨在研究一种新的多尺度相干结构表示，编码相关的流动物理、统计和不确定性信息，并开发基于基于这种新的表示来支持数据驱动的研究。为了实现这种多尺度分析，该项目对计算流体动力学 (CFD) 数据应用了许多空间和时间域分解策略。多场分析和高维数据投影技术适用于将不同的物理属性合并到表示中。利用新颖的图形表示以简洁且与维度无关的形式对这种多方面信息进行编码，以实现多尺度特征提取和跟踪。通过利用大规模矩阵计算的最新进展，采用该图的矩阵表示来加速其处理。基于所提出的图形表示设计了一种新的视觉分析范式，以帮助单独或集体探索和理解不同的湍流结构。作为许多软件库实现的开发技术可以集成到现有软件中，例如 Paraview，供领域科学家在日常研究中使用。所开发的技术还可以用作预处理工具箱来量化和提取相干结构，然后可以通过不适合直接图书馆集成的现有软件将其可视化。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式获得支持：使用基金会的智力价值和更广泛的影响审查标准进行评估。

项目成果

Dynamic Mode Decomposition for Large-Scale Coherent Structure Extraction in Shear Flows

剪切流中大规模相干结构提取的动态模式分解

• DOI: 10.1109/tvcg.2021.3124729
• 发表时间: 2023-02
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Nguyen, Duong B.;Wu, Panruo;Monico, Rodolfo Ostilla;Chen, Guoning
• 通讯作者: Chen, Guoning