Data compression for turbulence databases using spatio-temporal sub-sampling and local re-simulation
Fluid Dynamics
2020-07-01 v1 Computational Physics
Abstract
Motivated by specific data and accuracy requirements for building numerical databases of turbulent flows, data compression using spatio-temporal sub-sampling and local re-simulation is proposed. Numerical re-simulation experiments for decaying isotropic turbulence based on sub-sampled data are undertaken. The results and error analyses are used to establish parameter choices for sufficiently accurate sub-sampling and sub-domain re-simulation.
Cite
@article{arxiv.1910.11994,
title = {Data compression for turbulence databases using spatio-temporal sub-sampling and local re-simulation},
author = {Zhao Wu and Tamer A. Zaki and Charles Meneveau},
journal= {arXiv preprint arXiv:1910.11994},
year = {2020}
}