Bernaise：一个用于模拟复杂区域中两相电流体动力学流动的灵活框架
Bernaise: A flexible framework for simulating two-phase electrohydrodynamic flows in complex domains
摘要：
Bernaise（Binary ElectRohydrodyNAmIc SolvEr）是一款灵活的高级有限元求解器，用于求解复杂几何形状下的两相电流体动力学流动。具有电解质的两相流与广泛的系统和规模相关，从用于医疗诊断的“芯片实验室”设备到油藏规模的提高石油采收率。对于强耦合的多物理问题，我们采用了最近开发的物理一致性模型，该模型结合了广义能斯特-普朗克方程的离子输运，泊松方程的静电，Cahn-Hilliard方程的相场（描述界面分离相），和纳维-斯托克斯方程的流体流动。作为一个有效的替代解决耦合系统的偏微分方程在一个整体的方式，我们提出了一个线性的，解耦的数值方案，依次解决了三组方程。该计划是通过比较分析解决方案的极限情况下，基准情况下，和制造解决方案的方法进行验证。求解器在非结构化网格上运行，因此非常适合处理任意形状的域和问题设置，例如，在域的不同部分中需要非常不同的分辨率。Bernaise是通过FeNiCS框架在Python中实现的，该框架有效地利用了MPI和域分解，因此应该适合大规模/高性能计算。此外，经验丰富的Python用户可以轻松地指定新的求解器和问题设置并添加到Bernaise框架中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
