Publications

Plimpton, Steven J.

Abstract not provided.

Computer Physics Communications

Nguyen, Trung D.; Plimpton, Steven J.

In traditional molecular dynamics (MD) simulations, atoms and coarse-grained particles are modeled as point masses interacting via isotropic potentials. For studies where particle shape plays a vital role, more complex models are required. In this paper we describe a spectrum of approaches for modeling aspherical particles, all of which are now available (some recently) as options within the LAMMPS MD package. Broadly these include two classes of models. In the first, individual particles are aspherical, either via a pairwise anisotropic potential which implicitly assigns a simple geometric shape to each particle, or in a more general way where particles store internal state which can explicitly define a complex geometric shape. In the second class of models, individual particles are simple points or spheres, but rigid body constraints are used to create composite aspherical particles in a variety of complex shapes. We discuss parallel algorithms and associated data structures for both kinds of models, which enable dynamics simulations of aspherical particle systems across a wide range of length and time scales. We also highlight parallel performance and scalability and give a few illustrative examples of aspherical models in different contexts.

Plimpton, Steven J.

Abstract not provided.

Plimpton, Steven J.

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AIP Conference Proceedings

Gallis, Michail A.; Torczynski, J.R.; Bitter, Neal B.; Koehler, Timothy P.; Moore, Stan G.; Plimpton, Steven J.; Papadakis, G.

The Direct Simulation Monte Carlo (DSMC) method has been used for more than 50 years to simulate rarefied gases. The advent of modern supercomputers has brought higher-density near-continuum flows within range. This in turn has revived the debate as to whether the Boltzmann equation, which assumes molecular chaos, can be used to simulate continuum flows when they become turbulent. In an effort to settle this debate, two canonical turbulent flows are examined, and the results are compared to available continuum theoretical and numerical results for the Navier-Stokes equations.

Plimpton, Steven J.

Abstract not provided.

Physics of Fluids

Plimpton, Steven J.; Moore, Stan G.; Borner, A.; Stagg, Alan K.; Koehler, T.P.; Torczynski, J.R.; Gallis, Michail A.

The gold-standard definition of the Direct Simulation Monte Carlo (DSMC) method is given in the 1994 book by Bird [Molecular Gas Dynamics and the Direct Simulation of Gas Flows (Clarendon Press, Oxford, UK, 1994)], which refined his pioneering earlier papers in which he first formulated the method. In the intervening 25 years, DSMC has become the method of choice for modeling rarefied gas dynamics in a variety of scenarios. The chief barrier to applying DSMC to more dense or even continuum flows is its computational expense compared to continuum computational fluid dynamics methods. The dramatic (nearly billion-fold) increase in speed of the largest supercomputers over the last 30 years has thus been a key enabling factor in using DSMC to model a richer variety of flows, due to the method's inherent parallelism. We have developed the open-source SPARTA DSMC code with the goal of running DSMC efficiently on the largest machines, both current and future. It is largely an implementation of Bird's 1994 formulation. Here, we describe algorithms used in SPARTA to enable DSMC to operate in parallel at the scale of many billions of particles or grid cells, or with billions of surface elements. We give a few examples of the kinds of fundamental physics questions and engineering applications that DSMC can address at these scales.

Marinella, Matthew J.; Agarwal, Sapan A.; Jacobs-Gedrim, Robin B.; Hughart, David R.; Richter, Isaac R.; Hsia, Alexander W.; Fuller, Elliot J.; Talin, A.A.; Goeke, Ronald S.; Plimpton, Steven J.; James, Conrad D.

Abstract not provided.

Marinella, Matthew J.; Agarwal, Sapan A.; Jacobs-Gedrim, Robin B.; Hughart, David R.; Richter, Isaac R.; Hsia, Alexander W.; Fuller, Elliot J.; Talin, A.A.; Goeke, Ronald S.; Plimpton, Steven J.; James, Conrad D.

Abstract not provided.

Marinella, Matthew J.; Agarwal, Sapan A.; Jacobs-Gedrim, Robin B.; Hsia, Alexander W.; Hughart, David R.; Richter, Isaac R.; Fuller, Elliot J.; Plimpton, Steven J.; Talin, A.A.; James, Conrad D.

Abstract not provided.

Marinella, Matthew J.; Agarwal, Sapan A.; Jacobs-Gedrim, Robin B.; Hsia, Alexander W.; Hughart, David R.; Richter, Isaac R.; Fuller, Elliot J.; Plimpton, Steven J.; Talin, A.A.; James, Conrad D.

Abstract not provided.

Plimpton, Steven J.

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Plimpton, Steven J.

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Berry, Jonathan W.; Phillips, Cynthia A.; Plimpton, Steven J.; Porter, Alexandra P.; Shead, Timothy M.

Abstract not provided.

Plimpton, Steven J.

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Tranchida, Julien G.; Thompson, Aidan P.; Plimpton, Steven J.; Thibaudeau, Pascal T.

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Torczynski, J.R.; Gallis, Michail A.; Bitter, Neal B.; Koehler, Timothy P.; Plimpton, Steven J.; Papadakis, George P.

Abstract not provided.

Molecular Physics

Mattox, Timothy I.; Larentzos, James P.; Moore, Stan G.; Stone, Christopher P.; Ibanez, Daniel A.; Thompson, Aidan P.; Lísal, Martin; Brennan, John K.; Plimpton, Steven J.

Simulating energetic materials with complex microstructure is a grand challenge, where until recently, an inherent gap in computational capabilities had existed in modelling grain-scale effects at the microscale. We have enabled a critical capability in modelling the multiscale nature of the energy release and propagation mechanisms in advanced energetic materials by implementing, in the widely used LAMMPS molecular dynamics (MD) package, several novel coarse-graining techniques that also treat chemical reactivity. Our innovative algorithmic developments rooted within the dissipative particle dynamics framework, along with performance optimisations and application of acceleration technologies, have enabled extensions in both the length and time scales far beyond those ever realised by atomistic reactive MD simulations. In this paper, we demonstrate these advances by modelling a shockwave propagating through a microstructured material and comparing performance with the state-of-the-art in atomistic reactive MD techniques. As a result of this work, unparalleled explorations in energetic materials research are now possible.

Marinella, Matthew J.; Agarwal, Sapan A.; Jacobs-Gedrim, Robin B.; Hughart, David R.; Richter, Isaac R.; Hsia, Alexander W.; Fuller, Elliot J.; Talin, A.A.; Goeke, Ronald S.; Plimpton, Steven J.; James, Conrad D.

Abstract not provided.

Tranchida, Julien G.; Wood, Mitchell A.; Moore, Stan G.; Plimpton, Steven J.; Thompson, Aidan P.; Thibaudeau, Pascal T.

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Plimpton, Steven J.

Abstract not provided.

Physical Review Fluids

Gallis, Michail A.; Torczynski, J.R.; Bitter, Neal B.; Koehler, Timothy P.; Plimpton, Steven J.; Papadakis, G.

We provide a demonstration that gas-kinetic methods incorporating molecular chaos can simulate the sustained turbulence that occurs in wall-bounded turbulent shear flows. The direct simulation Monte Carlo method, a gas-kinetic molecular method that enforces molecular chaos for gas-molecule collisions, is used to simulate the minimal Couette flow at Re=500. The resulting law of the wall, the average wall shear stress, the average kinetic energy, and the continually regenerating coherent structures all agree closely with corresponding results from direct numerical simulation of the Navier-Stokes equations. These results indicate that molecular chaos for collisions in gas-kinetic methods does not prevent development of molecular-scale long-range correlations required to form hydrodynamic-scale turbulent coherent structures.

Lacy, Susan L.; Plimpton, Steven J.

The computational power of HPC is beyond our comprehension when we hear that 5 quadrillion computations can happen in a matter of seconds, or that machine learning is changing the way everything works. But none of that happens in a vacuum, and the teams behind the scenes—the developers of the hardware, the operating systems, the data transfer protocols, and the applications themselves—are the unsung heroes of a world where faster is better and you'd better hope there's no bug in the software or the hardware to slow you down. HPC is most successful when all these aspects work together seamlessly. The stories that follow are a tribute to the hardworking teams behind the scenes.

Plimpton, Steven J.

Abstract not provided.

Plimpton, Steven J.

Abstract not provided.