Acceleration of complex high-performance computing ensemble simulations with super-resolution-based subfilter models

Abstract

Direct numerical simulation (DNS) of fluid flow problems has been one of the most important applications of high-performance computing (HPC) in the last decades. For example, turbulent flows require the simultaneous resolution of multiple spatial and temporal scales as all scales are coupled, resulting in very large simulations with enormous degrees of freedom. Another example is reactive flows, which typically result in a large system of coupled differential equations and multiple transport equations that must be solved simultaneously. In addition, many flows exhibit chaotic behavior, meaning that only statistical ensembles of results can be compared, further increasing the computational time. In this work, a combined HPC/deep learning (DL) workflow is presented that drastically reduces the overall computational time required while still providing acceptable accuracy.Traditionally, all the simulations required to compute ensemble statistics are performed using expensive DNS. The idea behind the combined HPC/DL workflow is to reduce the number of expensive DNSs by developing a DL-assisted large-eddy simulation (LES) approach that uses a sophisticated DL network, called PIESRGAN, as a subfilter model for all unclosed terms and is accurate enough to substitute DNSs. The remaining DNSs are thus used in two ways: first, as data contributing to the ensemble statistics, and second, as data used to train the DL network. It was found that in many cases two remaining DNSs are sufficient for training the LES approach. The cost of the DL-supported LES is usually more than one order of magnitude cheaper than the DNS, which drastically speeds up the workflow, even considering the overhead for training the DL network.