MaMiCo: Non-Local Means Filtering with Flexible Data-Flow for Coupling MD and CFD

Abstract

When a molecular dynamics (MD) simulation and a computational fluid dynamics (CFD) solver are coupled together to create a multiscale, molecular-continuum flow simulation, thermal noise fluctuations from the particle system can be a critical issue, so that noise filtering methods are required. Noise filters are one option to significantly reduce these fluctuations.We present a modified variant of the Non-Local Means (NLM) algorithm for MD data. Originally developed for image processing, we extend NLM to a space-time formulation and discuss its implementation details.The space-time NLM algorithm is incorporated into the Macro-Micro-Coupling tool (MaMiCo), a C++ molecular-continuum coupling framework, together with a novel flexible filtering subsystem. The latter can be used to configure and efficiently execute arbitrary data-flow chains of simulation data analytics modules or noise filters at runtime on an HPC system, even including python functions. We employ a coupling to a GPU-based Lattice Boltzmann solver running a vortex street scenario to show the benefits of our approach. Our results demonstrate that NLM has an excellent signal-to-noise ratio gain and is a superior method for extraction of macroscopic flow information from noisy fluctuating particle ensemble data.