Jafari, Vahid

The macro–micro-coupling tool (MaMiCo) is an open source C++ research software framework, designed to create molecular-continuum flow simulations in a modular way, i.e. with exchangeable solvers. It can be used to couple discrete particle dynamics codes with computational fluid dynamics solvers while retaining performance, especially for parallel execution on supercomputers. We present a new version of MaMiCo that extends its functionality by a multitude of new features, notably with dynamic handling of molecular dynamics simulation instances, automated error estimation, coupling interfaces to the community codes ls1 mardyn and OpenFOAM, a Python interface, support for machine learning modules and enhanced two-way coupling. These features of the new MaMiCo version impact several fields of computational science and can be employed to tackle open research questions in the future, such as efficient multiscale numerical simulation of multi-phase flows, or fault tolerance of coupled simulations on large-scale cluster systems.

In coupled molecular-continuum flow simulations, molecular dynamics (MD) simulations exhibit thermal fluctuations. Finding a way to minimize the impact of these fluctuations on the CFD solver, e.g. in terms of stability, and to control the corresponding statistical error plays a key role in order to obtain reliable results. In this paper, statistical error analysis is employed for MD simulations to determine the statistical error in flow velocities and the number of MD data samples to bound this error. The corresponding error estimator is augmented by a dynamic ensemble handling approach, which allows to couple a variable number of MD simulation instances to a single CFD solver. The ensemble members can be simulated independently from each other over separate coupling time intervals, enabling a high level of (MPI-based) parallelism. Adding or removing MD simulations to/from the ensemble allows to regulate the error and keep it under a prescribed threshold. All functionality is implemented in the massively parallel macro-micro-coupling tool (MaMiCo). We validate our approach by coupled molecular-continuum Couette flow simulation for liquid argon and provide scalability tests on up to 131.072 cores. The computational overhead for handling the dynamic MD ensemble is found to be rather negligible.