Title: Massively Parallel Molecular-Continuum Flow Simulation with Error Control and Dynamic Ensemble Handling
Authors: Jafari, Vahid 
Wittmer, Niklas
Neumann, Philipp 
Language: en
Subject (DDC): DDC::000 Informatik, Informationswissenschaft, allgemeine Werke
DDC::500 Naturwissenschaften und Mathematik
Issue Date: Jan-2022
Document Type: Conference Object
Project: Simulation komplexer Mehrphasensysteme 
Resilience and Dynamic Noise Reduction at Exascale for Multiscale Simulation Coupling 
Page Start: 52
Page End: 60
Published in (Book): ACM International Conference Proceeding Series
Conference: HPCAsia2022: International Conference on High Performance Computing in Asia-Pacific Region 
Abstract: 
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.
Organization Units (connected with the publication): High Performance Computing 
DTEC.bw 
URL: https://api.elsevier.com/content/abstract/scopus_id/85122640135
ISBN: 9781450384988
DOI: 10.1145/3492805.3492812
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