Bernaise: A flexible framework for simulating two-phase electrohydrodynamic flows in complex domains

TL;DR

Bernaise is a versatile Python-based finite element solver designed for simulating complex two-phase electrohydrodynamic flows in arbitrary geometries, integrating multi-physics models with efficient decoupled numerical schemes.

Contribution

It introduces a flexible, high-level framework that combines advanced multi-physics modeling with an efficient decoupled numerical scheme and is easily extendable for large-scale simulations.

Findings

Validated against analytical solutions and benchmarks

Operates efficiently on unstructured meshes

Suitable for large-scale high-performance computing

Abstract

Bernaise (Binary ElectRohydrodyNAmIc SolvEr) is a flexible high-level finite element solver of two-phase electrohydrodynamic flow in complex geometries. Two-phase flow with electrolytes is relevant across a broad range of systems and scales, from 'lab-on-a-chip' devices for medical diagnostics to enhanced oil recovery at the reservoir scale. For the strongly coupled multi-physics problem, we employ a recently developed thermodynamically consistent model which combines a generalized Nernst-Planck equation for ion transport, the Poisson equation for electrostatics, the Cahn-Hilliard equation for the phase field (describing the interface separating the phases), and the Navier-Stokes equations for fluid flow. As an efficient alternative to solving the coupled system of partial differential equations in a monolithic manner, we present a linear, decoupled numerical scheme which sequentially solves the three sets of equations. The scheme is validated by comparison to limiting cases where analytical solutions are available, benchmark cases, and by the method of manufactured solution. The solver operates on unstructured meshes and is therefore well suited to handle arbitrarily shaped domains and problem set-ups where, e.g., very different resolutions are required in different parts of the domain. Bernaise is implemented in Python via the FEniCS framework, which effectively utilizes MPI and domain decomposition, and should therefore be suitable for large-scale/high-performance computing. Further, new solvers and problem set-ups can be specified and added with ease to the Bernaise framework by experienced Python users.