Analysis and postprocessing of space-time compressed flow computations

时空压缩流计算分析与后处理

• 批准号: 5330316
• 负责人: Professor Dr. Martin Rumpf
• 金额: --
• 依托单位: Institut für Numerische Simulation
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2001
• 资助国家: 德国
• 起止时间: 2000-12-31 至 2006-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/5330316?language=en
• 关键词: Analysis; postprocessing; space; time; compressed

Postprocessing of nonstationary flow data requires significant effort concerning the involved mathematical methods and the algorithmical tools. Especially in case of multi-scale, complex phenomena in 3D fluid dynamics one faces various difficulties. The typically enormous amount of considered data prevents a simultaneously handling of a sufficiently large part of the data in space and time. Standard tools for visualization are unable to support an intuitive understanding of the underlying phenomena. In fact they often lead to visual clutter. Instead of a time consuming manual data exploration with simple graphic tools, automatic feature extraction methods would be helpful. We will study and implement suitable compressing methods based on stable time stepping schemes and space-time adaptive error control to reduce the amount of data considerable. Furthermore, we will apply image processing methodology to appropriately visualize complex time-dependent flows and to extract important flow patterns. Thus, multi-scale methods will be considered, which naturally support a scale of data resolutions, ranging either from fine to coarse flow visualization or from many detailed features to only a few extracted, essential features. Furthermore, especially for problems with weaker dynamics (non turbulent flows), modern numerical methods allow significantly large time steps. In the postprocessing a simple interpolation turns out to be not the appropriate solution. We will consider image matching methods as a new approach to interpolate time steps of the flow simulation for animation purposes. Here a suitable balance between interactive performance and accuracy has to be found. Finally the developed visualization and analysis tools will be incorporated in a unique frame together with the numerical flow solvers.

非平稳流数据的后处理需要在所涉及的数学方法和算法工具方面付出巨大的努力。特别是在 3D 流体动力学中的多尺度、复杂现象的情况下，人们面临着各种困难。通常所考虑的数据量巨大，阻碍了在空间和时间上同时处理足够大的数据部分。标准可视化工具无法支持对潜在现象的直观理解。事实上，它们经常导致视觉混乱。自动特征提取方法将很有帮助，而不是使用简单的图形工具进行耗时的手动数据探索。我们将研究并实现基于稳定时间步进方案和空时自适应误差控制的合适压缩方法，以显着减少数据量。此外，我们将应用图像处理方法来适当地可视化复杂的时间相关流并提取重要的流模式。因此，将考虑多尺度方法，它自然支持一定规模的数据分辨率，范围从精细到粗略的流可视化，或从许多详细特征到仅提取的几个基本特征。此外，特别是对于较弱动力学（非湍流）的问题，现代数值方法允许显着大的时间步长。在后处理中，简单的插值并不是合适的解决方案。我们将图像匹配方法视为一种新方法，用于插入动画目的的流动模拟的时间步长。这里必须在交互性能和准确性之间找到适当的平衡。最后，开发的可视化和分析工具将与数值流求解器一起整合到一个独特的框架中。