705 research outputs found

    An adaptive hierarchical domain decomposition method for parallel contact dynamics simulations of granular materials

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    A fully parallel version of the contact dynamics (CD) method is presented in this paper. For large enough systems, 100% efficiency has been demonstrated for up to 256 processors using a hierarchical domain decomposition with dynamic load balancing. The iterative scheme to calculate the contact forces is left domain-wise sequential, with data exchange after each iteration step, which ensures its stability. The number of additional iterations required for convergence by the partially parallel updates at the domain boundaries becomes negligible with increasing number of particles, which allows for an effective parallelization. Compared to the sequential implementation, we found no influence of the parallelization on simulation results.Comment: 19 pages, 15 figures, published in Journal of Computational Physics (2011

    Self Assembly Problems of Anisotropic Particles in Soft Matter.

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    Anisotropic building blocks assembled from colloidal particles are attractive building blocks for self-assembled materials because their complex interactions can be exploited to drive self-assembly. In this dissertation we address the self-assembly of anisotropic particles from multiple novel computational and mathematical angles. First, we accelerate algorithms for modeling systems of anisotropic particles via massively parallel GPUs. We provide a scheme for generating statistically robust pseudo-random numbers that enables GPU acceleration of Brownian and dissipative particle dynamics. We also show how rigid body integration can be accelerated on a GPU. Integrating these two algorithms into a GPU-accelerated molecular dynamics code (HOOMD-blue), make a single GPU the ideal computing environment for modeling the self-assembly of anisotropic nanoparticles. Second, we introduce a new mathematical optimization problem, filling, a hybrid of the familiar shape packing and covering problem, which can be used to model shaped particles. We study the rich mathematical structures of the solution space and provide computational methods for finding optimal solutions for polygons and convex polyhedra. We present a sequence of isosymmetric optimal filling solutions for the Platonic solids. We then consider the filling of a hyper-cone in dimensions two to eight and show the solution remains scale-invariant but dependent on dimension. Third, we study the impact of size variation, polydispersity, on the self-assembly of an anisotropic particle, the polymer-tethered nanosphere, into ordered phases. We show that the local nanoparticle packing motif, icosahedral or crystalline, determines the impact of polydispersity on energy of the system and phase transitions. We show how extensions of the Voronoi tessellation can be calculated and applied to characterize such micro-segregated phases. By applying a Voronoi tessellation, we show that properties of the individual domains can be studied as a function of system properties such as temperature and concentration. Last, we consider the thermodynamically driven self-assembly of terminal clusters of particles. We predict that clusters related to spherical codes, a mathematical sequence of points, can be synthesized via self-assembly. These anisotropic clusters can be tuned to different anisotropies via the ratio of sphere diameters and temperature. The method suggests a rich new way for assembling anisotropic building blocks.Ph.D.Applied Physics and Scientific ComputingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91576/1/phillicl_1.pd

    Numerical simulation of destabilizing heterogeneous suspensions at vanishing Reynolds numbers

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    This work deals with the numerical investigation of destabilizing suspensions, which are governed by two basic processes: Clustering and sedimentation. After laying the foundation to an efficient numerical simulation based on the Stokesian Dynamics method, hydrodynamic clustering and clustering due to non-hydrodynamic interactions are investigated. It is shown that multi-particle simulations need parallelization and an efficient post-processing to yield reliable results within a reasonable time

    Basic Understanding of Condensed Phases of Matter via Packing Models

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    Packing problems have been a source of fascination for millenia and their study has produced a rich literature that spans numerous disciplines. Investigations of hard-particle packing models have provided basic insights into the structure and bulk properties of condensed phases of matter, including low-temperature states (e.g., molecular and colloidal liquids, crystals and glasses), multiphase heterogeneous media, granular media, and biological systems. The densest packings are of great interest in pure mathematics, including discrete geometry and number theory. This perspective reviews pertinent theoretical and computational literature concerning the equilibrium, metastable and nonequilibrium packings of hard-particle packings in various Euclidean space dimensions. In the case of jammed packings, emphasis will be placed on the "geometric-structure" approach, which provides a powerful and unified means to quantitatively characterize individual packings via jamming categories and "order" maps. It incorporates extremal jammed states, including the densest packings, maximally random jammed states, and lowest-density jammed structures. Packings of identical spheres, spheres with a size distribution, and nonspherical particles are also surveyed. We close this review by identifying challenges and open questions for future research.Comment: 33 pages, 20 figures, Invited "Perspective" submitted to the Journal of Chemical Physics. arXiv admin note: text overlap with arXiv:1008.298

    Modelling dry powder inhaler operation with the discrete element method

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    Dry powder inhalers (DPI) are a common asthma treatment. Despite the number of commercial devices available, little is known about their internal operation: the process of fluidising a powder dose into an inhalation airflow. This PhD aims to investigate this process, and demonstrate that it can be modelled computationally. . Experimental work is described to record high speed video of the dose fluidisation from simplified DPls. Typical DPI powders such as lactose are tested, along with cohesionless glass spheres and aluminium flakes. Two distinct dose fluidisation mechanisms are identified, labelled 'fracture' and 'erosion'. Lactose exhibits a fracture mechanism -- large agglomerates are produced as the powder bed cracks along lines of weakness. Glass or aluminium particles exhibit an erosion mechanism: powder is entrained into the flow as individual particles from the bed surface. The recorded video is quantitatively analysed to determine fluidisation timescales and pressures. Shear cell test results show that predicting the mechanism of fluidisation is not possible using averaged bulk powder properties. This suggests any DPI model must include the fundamental particle interactions. The discrete element method (OEM) is introduced as a computational technique capable of predicting DPI behaviour from individual particle properties. The numerical accuracy of the method is assessed, showing that time integration is limited to a maximum of 2nd order accuracy due to discontinuities in particle contact forces. A sensitivity analysis shows inter-particle cohesion is the dominant factor affecting OEM predictions. OEM is used to create a simple model of the dose fluidisation that occurs within a DPI. The results are compared with real powder behaviour. OEM is shown to capture the realistic fluidisation of both lactose and glass powder doses. It is concluded that OEM is a promising technique to predict DPI behaviour, although further work is required to quantify inter--particle cohesive parametersImperial Users onl

    Dynamic Load Balancing Techniques for Particulate Flow Simulations

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    Parallel multiphysics simulations often suffer from load imbalances originating from the applied coupling of algorithms with spatially and temporally varying workloads. It is thus desirable to minimize these imbalances to reduce the time to solution and to better utilize the available hardware resources. Taking particulate flows as an illustrating example application, we present and evaluate load balancing techniques that tackle this challenging task. This involves a load estimation step in which the currently generated workload is predicted. We describe in detail how such a workload estimator can be developed. In a second step, load distribution strategies like space-filling curves or graph partitioning are applied to dynamically distribute the load among the available processes. To compare and analyze their performance, we employ these techniques to a benchmark scenario and observe a reduction of the load imbalances by almost a factor of four. This results in a decrease of the overall runtime by 14% for space-filling curves

    Dynamic Compression of in situ Grown Living Polymer Brush: Simulation and Experiment

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    A comparative dynamic Monte Carlo simulation study of polydisperse living polymer brushes, created by surface initiated living polymerization, and conventional polymer monodisperse brush, comprising linear polymer chains, grafted to a planar substrate under good solvent conditions, is presented. The living brush is created by end-monomer (de)polymerization reaction after placing an array of initiators on a grafting plane in contact with a solution of initially non-bonded segments (monomers). At equilibrium, the monomer density profile \phi(z) of the LPB is found to decline as \phi(z) ~ z^{-\alpha} with the distance from the grafting plane z, while the distribution of chain lengths in the brush scales as c(N) ~ N^{-\tau}. The measured values \alpha = 0.64 and \tau = 1.70 are very close to those, predicted within the framework of the Diffusion-Limited Aggregation theory, \alpha = 2/3 and \tau = 7/4. At varying mean degree of polymerization (from L = 28 to L = 170) and effective grafting density (from \sigma_g = 0.0625 to \sigma_g = 1.0), we observe a nearly perfect agreement in the force-distance behavior of the simulated LPB with own experimental data obtained from colloidal probe AFM analysis on PNIPAAm brush and with data obtained by Plunkett et. al., [Langmuir 2006, 22, 4259] from SFA measurements on same polymer
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