Development of an Efficient and Flexible Pipeline for Lagrangian Coherent Structure Computation

TL;DR

This paper presents a modular, GPU-accelerated pipeline for computing Lagrangian coherent structures that integrates with visualization tools, improving efficiency and flexibility in analyzing unsteady flows.

Contribution

It introduces a VTK-based object-oriented framework and CUDA GPU kernels for efficient, adaptable LCS computation suitable for diverse fluid mechanics applications.

Findings

GPU kernels significantly speed up flow map sampling

Framework easily integrates with flow visualization software

Modular design allows easy extension and customization

Abstract

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.