Digital Image Processing Techniques for Flow Field Analysis and Interpretation

将开发一种模式建模技术，用于表示、分析和分类流体动力学和燃烧诊断研究产生的数据集中的特征，尤其是湍流。研究的主要目的是建立一个两组分的流场模型：一种是沿各向异性的主方向，描述大尺度流动；另一种是代表独立的小尺度流动结构的残留型。分离成全局流和局部流便于随后识别和跟踪诸如涡流之类的特征。由于流模式的去相关性，将可以实现显著的数据压缩。将开发低级别图像处理技术，以应对流体图像的非常规特性带来的挑战。研究了一种基于地形分类器的低层图像分割方法。此外，还将研究用于进一步压缩双组分流模型的正交变换。从现有实验室获得的数据将用于测试我们的建模策略。空间和时间分辨率的2D数据集将被用于扩展我们之前在特征检测和跟踪实验中的工作。我们预计拟议的工作将满足燃烧和传热界提出的关于大型流体流动数据集的计算机分析技术自动化的一些需求。

A pattern modeling techniques for representing, analyzing, and classifying features in data sets resulting from fluid dynamics and combustion diagnostics research, with particular emphasis on turbulent flows will be developed. The primary thrust of the research is directed toward a two component model for flow fields; one being oriented along the major direction of anisotropy, describing the large scale flow; the other being a residual pattern representing the independent small scale flow structures. The separation into global and localized flows facilitates subsequent identification and tracking of features such as eddies. Significant data compression will be achievable due to the decorrelation of the flow patterns. Low level image processing techniques will be developed to meet the challenges posed by the unconventional properties of fluid imagery. A method for low level image segmentation based on a topographic classifier will be investigated. In addition, orthogonal transforms for further compression of the two component flow model will be researched. Data acquired from established laboratories will be used to test our modeling strategy. Spatially and temporally resolved 2D data sets will be used to extend our previous work in feature detection and tracking experiments. We expect the proposed work will meet some of the needs voiced by the combustion and heat transfer community concerning the automation of computer analysis techniques for large fluid flow data sets.