Dmitry Karpeyev

Miscibility Gap Closure, Interface Morphology, and Phase Microstructure of 3D LixFePO4 Nanoparticles from Surface Wetting and Coherency Strain

We study the mesoscopic effects which modify phase-segregation in LixFePO4 nanoparticles using a multiphysics phase-field model implement on a high performance cluster. We simulate 3D spherical particles of radii from 3 to 40 nm and examine the equilibrium microstructure and voltage profiles as they depend on size and overall lithiation. The model includes anisotropic, concentration-dependent elastic moduli, misfit strain, and facet dependent surface wetting within a Cahn-Hilliard formulation. We find that the miscibility gap vanishes for particles of radius ∼5 nm, and the solubility limits change with overall particle lithiation. Surface wetting stabilizes minority phases by aligning them with energetically beneficial facets. The equilibrium voltage profile is modified by these effects in magnitude, and the length and slope of the voltage plateau during two-phase coexistence.

Stable large-scale solver for Ginzburg-Landau equations for superconductors

Understanding the interaction of vortices with inclusions in type-II superconductors is a major outstanding challenge both for fundamental science and energy applications. At application-relevant scales, the long-range interactions between a dense configuration of vortices and the dependence of their behavior on external parameters, such as temperature and an applied magnetic field, are all important to the net response of the superconductor. Capturing these features, in general, precludes analytical description of vortex dynamics and has also made numerical simulation prohibitively expensive. Here we report on a highly optimized iterative implicit solver for the time-dependent Ginzburg-Landau equations suitable for investigations of type-II superconductors on massively parallel architectures. Its main purpose is to study vortex dynamics in disordered or geometrically confined mesoscopic systems. In this work, we present the discretization and time integration scheme in detail for two types of boundary conditions. We describe the necessary conditions for a stable and physically accurate integration of the equations of motion. Using an inclusion pattern generator, we can simulate complex pinning landscapes and the effect of geometric confinement. We show that our algorithm, implemented on a GPU, can provide static and dynamic solutions of the Ginzburg-Landau equations for mesoscopically large systems over thousands of time steps in a matter of hours. Using our formulation, studying scientifically-relevant problems is a computationally reasonable task.

Detecting vortices in superconductors: Extracting one-dimensional topological singularities from a discretized complex scalar field

In type II superconductors, the dynamics of superconducting vortices determine their transport properties. In the Ginzburg-Landau theory, vortices correspond to topological defects in the complex order parameter. Extracting their precise positions and motion from discretized numerical simulation data is an important, but challenging, task. In the past, vortices have mostly been detected by analyzing the magnitude of the complex scalar field representing the order parameter and visualized by corresponding contour plots and isosurfaces. However, these methods, primarily used for small-scale simulations, blur the fine details of the vortices, scale poorly to large-scale simulations, and do not easily enable isolating and tracking individual vortices. Here we present a method for exactly finding the vortex core lines from a complex order parameter field. With this method, vortices can be easily described at a resolution even finer than the mesh itself. The precise determination of the vortex cores allows the interplay of the vortices inside a model superconductor to be visualized in higher resolution than has previously been possible. By representing the field as the set of vortices, this method also massively reduces the data footprint of the simulations and provides the data structures for further analysis and feature tracking.

Extracting, Tracking, and Visualizing Magnetic Flux Vortices in 3D Complex-Valued Superconductor Simulation Data

We propose a method for the vortex extraction and tracking of superconducting magnetic flux vortices for both structured and unstructured mesh data. In the Ginzburg-Landau theory, magnetic flux vortices are well-defined features in a complex-valued order parameter field, and their dynamics determine electromagnetic properties in type-II superconductors. Our method represents each vortex line (a 1D curve embedded in 3D space) as a connected graph extracted from the discretized field in both space and time. For a time-varying discrete dataset, our vortex extraction and tracking method is as accurate as the data discretization. We then apply 3D visualization and 2D event diagrams to the extraction and tracking results to help scientists understand vortex dynamics and macroscale superconductor behavior in greater detail than previously possible.

Porting Ordinary Applications to Blue Gene/Q Supercomputers

Efficiently porting ordinary applications to Blue Gene/Q supercomputers is a significant challenge. Codes are often originally developed without considering advanced architectures and related tool chains. Science needs frequently lead users to want to run large numbers of relatively small jobs (often called many-task computing, an ensemble, or a workflow), which can conflict with supercomputer configurations. In this paper, we discuss techniques developed to execute ordinary applications over leadership class supercomputers. We use the high-performance Swift parallel scripting framework and build two workflow execution techniques -- sub-jobs and main-wrap. The sub-jobs technique, built on top of the IBM Blue Gene/Q resource manager Cobalts sub-block jobs, lets users submit multiple, independent, repeated smaller jobs within a single larger resource block. The main-wrap technique is a scheme that enables C/C++ programs to be defined as functions that are wrapped by a high-performance Swift wrapper and that are invoked as a Swift script. We discuss the needs, benefits, technicalities, and current limitations of these techniques. We further discuss the real-world science enabled by these techniques and the results obtained.

Tracking vortices in superconductors: Extracting singularities from a discretized complex scalar field evolving in time

In type-II superconductors, the dynamics of magnetic flux vortices determine their transport properties. In the Ginzburg-Landau theory, vortices correspond to topological defects in the complex order parameter field. Earlier, in Phillips et al. [Phys. Rev. E 91, 023311 (2015)], we introduced a method for extracting vortices from the discretized complex order parameter field generated by a large-scale simulation of vortex matter. With this method, at a fixed time step, each vortex [simplistically, a one-dimensional (1D) curve in 3D space] can be represented as a connected graph extracted from the discretized field. Here we extend this method as a function of time as well. A vortex now corresponds to a 2D space-time sheet embedded in 4D space time that can be represented as a connected graph extracted from the discretized field over both space and time. Vortices that interact by merging or splitting correspond to disappearance and appearance of holes in the connected graph in the time direction. This method of tracking vortices, which makes no assumptions about the scale or behavior of the vortices, can track the vortices with a resolution as good as the discretization of the temporally evolving complex scalar field. Additionally, even details of the trajectory between time steps can be reconstructed from the connected graph. With this form of vortex tracking, the details of vortex dynamics in a model of a superconducting materials can be understood in greater detail than previously possible.

Perturbative Expansions and Critical Phenomena in Random Structured Media

We present constructive solutions for the effective properties for three problems in the field of random structured media. They are all based on truncated series and on a constructive investigation of their behavior near divergence points where the physical percolation or phase transitions occur. (1) Effective conductivity of 2D conductors with arbitrary contrast parameters is reconstructed from the expansion at small concentrations and of the critical behavior at high concentrations. (2) Effective shear modulus of perfectly rigid spherical inclusions randomly embedded into an incompressible matrix is reconstructed given its expansion at small concentrations and critical behavior. In addition, the critical index S of super-elasticity is estimated. (3) We also employ a truncated Fourier expansion to study spontaneous directional ordering in models of planar fully-connected suspensions of active polar particles. The main result is the discovery of a discontinuous, abrupt transition from an ordered to a disordered state. It is a macroscopic effect caused by a mesoscopic self-quenching noise. The relaxation time remains finite at the critical point, therefore the effect of self-quenching is to strongly suppress the critical slowing down and improve the reaction time to external stimuli.