libMobility: A Python library for hydrodynamics at the Smoluchowski level
Abstract
Effective hydrodynamic modeling is crucial for accurately predicting fluid–particle interactions in diverse fields such as biophysics and materials science. Developing and implementing hydrodynamic algorithms is challenging due to the complexity of fluid dynamics, necessitating efficient management of large-scale computations and sophisticated boundary conditions. Furthermore, adapting these algorithms for use on massively parallel architectures such as GPUs adds an additional layer of complexity. This paper presents the libMobility software library, which offers a suite of CUDA-enabled solvers for simulating hydrodynamic interactions in particulate systems at the Rotne–Prager–Yamakawa level. The library facilitates precise simulations of particle displacements influenced by external forces and torques, including both the deterministic and stochastic components. Notable features of libMobility include its ability to handle linear and angular displacements, thermal fluctuations, and various domain geometries effectively. With an interface in Python, libMobility provides comprehensive tools for researchers in computational fluid dynamics and related fields to simulate particle mobility efficiently. This article details the technical architecture, functionality, and wide-ranging applications of libMobility. libMobility is available at https://github.com/stochasticHydroTools/libMobility.
Article Details
Journal Info
The Journal of Chemical Physics
American Institute of Physics
Authors (6)
Ryker Fish
Adam Carter
CNRS, Sorbonne Université, Physicochimie des Electrolytes et Nanosystèmes Interfaciaux 2 , F-75005 Paris,
Pablo Diez-Silva
Department of Theoretical Condensed Matter Physics, Universidad Autónoma de Madrid 3 , 28049 Madrid,
Rafael Delgado-Buscalioni
Department of Theoretical Condensed Matter Physics, Condensed Matter Physics Center, Instituto Nicolás Cabrera
Raul P. Pelaez
Department of Theoretical Condensed Matter Physics, Universidad Autónoma de Madrid 3 , 28049 Madrid,
Brennan Sprinkle
Department of Applied Math and Statistics, Colorado School of Mines