MaMiCo: Parallel Noise Reduction for Multi-instance Molecular-Continuum Flow Simulation

摘要

Transient molecular-continuum coupled flow simulations often suffer from high thermal noise, created by fluctuating hydrodynamics within the molecular dynamics (MD) simulation. Multi-instance MD computations are an approach to extract smooth flow field quantities on rather short time scales, but they require a huge amount of computational resources. Filtering particle data using signal processing methods to reduce numerical noise can significantly reduce the number of instances necessary. This leads to improved stability and reduced computational cost in the molecular-continuum setting. We extend the Macro-Micro-Coupling tool (MaMiCo) - a software to couple arbitrary continuum and MD solvers - by a new parallel interface for universal MD data analytics and post-processing, especially for noise reduction. It is designed modularly and compatible with multi-instance sampling. We present a Proper Orthogonal Decomposition (POD) implementation of the interface, capable of massively parallel noise filtering. The resulting coupled simulation is validated using a three-dimensional Couette flow scenario. We quantify the denoising, conduct performance benchmarks and scaling tests on a supercomputing platform. We thus demonstrate that the new interface enables massively parallel data analytics and post-processing in conjunction with any MD solver coupled to MaMiCo.