Digital Image Processing Techniques for Flow Field Analysis and Interpretation

9314917 Strickland这项工作的主要目标是开发计算机分析技术，用于表示和分析由流可视化实验产生的大型、多维标量和矢量数据集。基本前提是，复杂的流动可以分解成更简单的、物理上有意义的流动成分，这样做有助于计算机辅助的流动分析。数据的大型3D区域将被分割成更小的、计算上可管理的卷。研究了临界点的空间分布与非线性模型阶数之间的相互作用。建模技术将在实验三维矢量场数据上进行测试。紊流将使用混沌动力系统的工具建模，即分形和迭代函数系统（IFS）。目标是解决经典的反问题——给定流的图像，或者速度场，推导出一个IFS，从这个IFS中可以重建输入数据。动力系统模型将用于特征（如漩涡）识别、时间序列特征跟踪、动画和可视化。从已建立的实验室获得的数据将用于测试建模范例及其在流动分析中的效用。这项工作将解决流体、空气动力学、燃烧和传热领域对处理和解释大型流体流动数据集的计算机辅助方法自动化的需求。＊＊＊

9314917 Strickland The main objective of this work is to develop computer analysis techniques for representing and analyzing large, multidimensional scalar and vector data sets generated by flow visualization experiments. The fundamental premise is that complex flows can be decomposed into simpler, physically meaningful flow components, and that doing so facilitates computer assisted flow analysis. Large 3D regions of the data will be segmented into smaller, computationally manageable volumes. The interaction between the spatial distribution of critical points and the order of the nonlinear model will be studied. Modeling techniques will be tested on experimental 3D vector field data. Turbulent flows will be modeled using the tools of chaotic dynamical systems, namely fractals and iterated function systems (IFS). The goal is to solve the classic inverse problem - given the image of a flow, or the velocity field, derive an IFS from which reconstruction of the input data is possible. The dynamical systems models will be used for feature (e.g. vortex) identification, feature tracking in time sequences, and animation and visualization. Data acquired from established laboratories will be used to test the modeling paradigms and their utility in flow analysis. This work will address the needs voiced by he fluids, aerodynamics, and combustion and heat transfer communities concerning the automation of computer assisted methods for processing and interpreting large fluid flow data sets. ***