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.
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Acknowledgements
Financial support from the Stiftelsen för strategisk forskning (SSF) and the Swedish e-Science Research Centre (SeRC) via the SESSI project is acknowledged. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC).
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Otero, E., Vinuesa, R., Schlatter, P., Marin, O., Siegel, A., Laure, E. (2019). The Effect of Lossy Data Compression in Computational Fluid Dynamics Applications: Resilience and Data Postprocessing. In: Salvetti, M., Armenio, V., Fröhlich, J., Geurts, B., Kuerten, H. (eds) Direct and Large-Eddy Simulation XI. ERCOFTAC Series, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-030-04915-7_24
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DOI: https://doi.org/10.1007/978-3-030-04915-7_24
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