Mango-Selm | Fluctuating Hydrodynamics
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Mango-Selm is a package for performing fluctuating hydrodynamics simulations in LAMMPS. The package can be used for performing implicit-solvent coarse-grained simulations in molecular dynamics and for more general problems involving fluid-structure interactions subject to thermal fluctuations. The package includes implementations of Stochastic Eulerian Lagrangian Method (SELM) and Stochastic Immersed Boundary Method (SIB) approaches. In these hybrid methods solvent/fluid mediated effects are taken into account through continuum stochastic hydrodynamic equations that are coupled to discrete microstructure degrees of freedom. The SELM methods provide momentum conserving thermostats for these systems and approaches for resolving relaxation of hydrodynamic modes and other dissipative interactions. For more details, see the papers below.

Related Talks:

Fluctuating Hydrodynamics Approaches for Mesoscopic Modeling and Simulation (Part 1 & 2)
Stanford University: Summer School on Multiscale Modeling of Materials Workshop.

[PDF of slides 1] [PDF of slides 2]
[link to video 1] [link to video 2]

Package Details:

The Mango-Selm package has been implemented using a combination of programming languages including Python, Java, and C++. The core SELM package is implemented in C++ and uses data files represented using standardized formats, such as XML and VTK. The SELM package is integrated with LAMMPS. LAMMPS is a dynamics package for modeling mechanical systems and provides common force laws, different types of degrees of freedom, and statistical analysis methods. Mango-Selm (USER-SELM) allows for using these capabilities when performing fluctuating hydrodynamics simulations.

Mango is a user interface for the specification of simulation models and numerical parameters. MANGO can be used to setup models and simulations for the SELM package.

MANGO provides a platform independent graphical user interface using the Java programming language. MANGO allows for the graphical specification of the model geometry, solution domain, force interactions, and numerical integration parameters. For basic models, MANGO graphical interface can be used. For more complicated models, the python interface can be used.

Python / Jython To allow for a programmatic specification of models and simulations, Python-based scripts have been developed. This allows for model specifications leveraging standard packages. This has also been incorporated into the MANGO interface using Jython. Jython scripts also allow for extending the capabilities of the interface with customized control panels for new features.


When reporting results using this package, please cite

Fluctuating Hydrodynamics Methods for Dynamic Coarse-Grained Implicit-Solvent Simulations in LAMMPS, Y. Wang, J. K. Sigurdsson, and P.J. Atzberger, SIAM J. Sci. Comp., 38(5), S62S77, (2016) [preprint] [paper link]

title = {Fluctuating Hydrodynamics Methods for Dynamic Coarse-Grained Implicit-Solvent Simulations in LAMMPS},
author = {Wang, Y. and Sigurdsson, J. K. and Atzberger, P. J.},
journal = {SIAM Journal on Scientific Computing},
volume = {38},
number = {5},
pages = {S62-S77},
year = {2016},
doi = {10.1137/15M1026390},
URL = {},

Related papers:

  1. Fluctuating Hydrodynamics Methods for Dynamic Coarse-Grained Implicit-Solvent Simulations in LAMMPS, Y. Wang, J. K. Sigurdsson, and P.J. Atzberger, SIAM J. Sci. Comp., 38(5), S62S77, (2016) [preprint] [full paper]
  2. Stochastic Reductions for Inertial Fluid-Structure Interactions Subject to Thermal Fluctuations, G. Tabak and P.J. Atzberger, SIAM J. Appl. Math., 75(4), 18841914, (2015) [full paper]
  3. Fluctuating Hydrodynamic Methods for Fluid-Structure Interactions in Confined Channel Geometries, Y. Wang, H. Lei, P. Atzberger, Applied Mathematics and Mechanics (Springer), January, Volume 39, Issue 1, pp 125152, (2018) [full paper].
  4. Spatially Adaptive Stochastic Methods for Fluid-Structure Interactions Subject to Thermal Fluctuations in Domains with Complex Geometries, P. Plunkett, J. Hu, C. Siefert, P.J. Atzberger, Journal of Computational Physics, Vol. 277, 15 Nov. 2014, pg. 121--137, (2014) [full paper]
  5. Dynamic Implicit-Solvent Coarse-Grained Models of Lipid Bilayer Membranes : Fluctuating Hydrodynamics Thermostat, Y. Wang, J. K. Sigurdsson, E. Brandt, and P.J. Atzberger, Phys. Rev. E 88, 023301, (2013) [full paper]
  6. Incorporating Shear into Stochastic Eulerian Lagrangian Methods for Rheological Studies of Complex Fluids and Soft Materials, P.J. Atzberger, Physica D, Vol. 265, pg. 5770, (2013). [full paper]

  7. Stochastic Eulerian Lagrangian Methods for Fluid Structure Interactions with Thermal Fluctuations, P.J. Atzberger, J. of Comp. Phys., 230, pp. 2821--2837, (2011). [full paper]

  8. A Stochastic Immersed Boundary Method for Fluid-Structure Dynamics at Microscopic Length Scales, P.J. Atzberger, P.R. Kramer, and C.S. Peskin, J. Comp. Phys., Vol. 224, Iss. 2, (2007). [full paper]

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