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.

相干结构是可以在流体流动中发现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，可靠的识别和表征相干结构是具有挑战性的，由于其多样性和复杂的相互关系，在不同的空间和时间尺度。该项目汇集了来自数据可视化和流体力学社区的专家，研究多尺度相干结构提取，分离，跟踪和可视化的新解决方案。它的目的是显着提高分析大型湍流数据集的能力，这些数据集来自广泛的工程和科学应用中的计算流体动力学（CFD）模拟。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。研究成果可以整合到休斯顿大学教授的一些本科和研究生课程的开发中。拟议研究所开展的外展活动有助于激励更多学生在STEM相关领域从事职业。 为了实现对大规模湍流数据的有效和可靠的分析，该项目旨在研究一种新的多尺度相干结构表示，该表示编码相关的流动物理，统计和不确定性信息，并基于这种新表示开发一个强大的计算和探索框架，以支持数据驱动的研究。为了实现这种多尺度分析，该项目将一些空间和时间域分解策略应用于计算流体动力学（CFD）数据。多场分析和高维数据投影技术适用于将不同的物理属性的表示。一种新的图形表示被利用来以简洁和维度无关的形式对这种多方面的信息进行编码，以实现多尺度特征提取和跟踪。利用大规模矩阵计算的最新进展，该图的矩阵表示，以加速其处理。一个新的视觉分析范式的基础上提出的图形表示，以帮助探索和理解不同的湍流结构单独或集体。实现为多个软件库的所开发的技术可以被集成到现有软件中，例如，Paraview，供领域科学家在日常研究中使用。所开发的技术也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库集成的现有软件进行可视化。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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

• DOI: 10.1109/tvcg.2021.3124729
• 发表时间: 2021-11
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Duong B. Nguyen;Panruo Wu;R. O. Monico;Guoning Chen