In modern day simulations researchers have a particular interest in modeling physical, chemical,
or biological processes on multiple time and length scales and combine them in a single simulation.
One particular example for such an approach are (coarse grained) molecular ensembles embedded in a
liquid or gas. This approach may be used to simulate e.g. transportation of biomolecules through a
nano pore or the filtration of dust particles. To model those phenomena different subsystems are
required, each of which is described by different sets of equations.
To tackle these problems we want to use ESPResSo, which is a very versatile and feature rich
molecular dynamics (MD) simulation tool which is developed in the SFB 716. It uses a thermalized
D3Q19 implementation of the lattice-Boltzmann method (LBM) on a regular grid to simulate a
background flow. This simple spatial discretization prevents the transition to physically more
relevant time and length scales.
To overcome this problem we want to use adaptive mesh refinement (AMR) to reduce the number of
degrees of freedom in the system. As not all regions in the simulation domain have the same
relevance to the solution, AMR allows focusing on regions of high interest while reducing
computational load in regions where that level of detail is not required.
While there are various different approaches to AMR we want to focus on two important aspects:
1) We want to focus on a minimally-invasive integration to preserve as much of the expert
knowledge as possible that is already contained in the application.
2) We want the AMR library to be as lightweight and scalable as possible to avoid introducing
additional costs.
To this end, we choose the forest-of-octrees approach and p4est, an efficient and well
scaling grid library.
Our contribution consists of two important aspects:
First, we extended p4est to be better suited in terms of a minimally-invasive integration
into an existing application.
Second, we replaced the regular linked-cell and LBM grids in ESPResSo to reduce the number of
degrees of freedom in the system.
We will show first results for an adaptive LBM simulation and our coupling between LBM and MD as
well as upscaling tests of our implementations.
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