The effect of lossy data compression in computational fluid dynamics applications: Resilience and data postprocessing

Abstract

The field of computational fluid dynamics (CFD) is data intensive, particularly for high-fidelity simulations. Direct and large-eddy simulations (DNS and LES), which are framed in this high-fidelity regime, require to capture a wide range of flow scales, a fact that leads to a high number of degrees of freedom. Besides the computational bottleneck, brought by the size of the problem, a slightly overlooked issue is the manipulation of the data. High amounts of disk space and also the slow speed of I/O (input/output) impose limitations on large-scale simulations. Typically the computational requirements for proper resolution of the flow structures are far higher than those of post-processing. To mitigate such shortcomings we employ a lossy data compression procedure, and track the reduction that occurs for various levels of truncation of the data set.