The computation of Lagrangian coherent structures (LCS) has become a standard tool for the analysis of advective transport in unsteady flow applications. LCS identification is primarily accomplished by evaluating measures based on the finite-time Cauchy Green (CG) strain tensor over the fluid domain. Sampling the CG tensor requires the advection of large numbers of fluid tracers, which can be computationally intensive, but presents a large degree of data parallelism. Processing can be specialized to parallel computing architectures, but on the other hand, there is compelling need for robust and flexible implementations for end users. Specifically, code that can accommodate analysis of wide-ranging fluid mechanics applications, while using a modular structure that is easily extended or modified, and facilitates visualization is desirable. We discuss the use of Visualization Toolkit (VTK) libraries as a foundation for object-oriented LCS computation, and how this framework can facilitate integration of LCS computation into flow visualization software such as ParaView. We also discuss the development of CUDA GPU kernels for efficient parallel spatial sampling of the flow map, including optimizing these kernels for better utilization.
翻译:Lagrangian连贯结构(LCS)的计算已成为分析不稳定流动应用中的活性运输的标准工具。 LCS的识别主要通过在流体域的有限时间Cauch绿色(CG)菌株强力的基础上评价措施来实现。CG气压的取样要求对大量液体追踪器进行吸附,这些液体追踪器可以进行密集的计算,但具有很大的数据平行性。处理可以专门用于平行的计算结构,但另一方面,迫切需要对终端用户进行强有力和灵活的实施。具体地说,可以容纳对广泛流体机械应用的分析的代码,同时使用易于扩展或修改的模块结构,并促进可视化。我们讨论了利用可视化工具包图书馆作为以物体为导向的LCS计算基础的问题,以及这一框架如何促进将LCS的计算纳入ParaView等流动可视化软件的问题。我们还讨论了CUDA GPU内核子对流动地图进行高效平行空间取样的问题,包括优化这些内核器以便更好地利用。