Data compression for turbulence databases using spatio-temporal sub-sampling and local re-simulation

TL;DR

This paper introduces a data compression method for turbulence databases that employs spatio-temporal sub-sampling combined with local re-simulation, optimizing data storage while maintaining accuracy for turbulent flow simulations.

Contribution

It presents a novel approach integrating sub-sampling and local re-simulation tailored for turbulence data, with parameter selection based on error analysis.

Findings

Effective data compression with controlled error levels

Parameter guidelines for accurate sub-sampling and re-simulation

Successful application to decaying isotropic turbulence

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.