715 research outputs found
Parallel simulation of reinforced concrete sructures using peridynamics
The failure of concrete structures involves many complex mechanisms. Traditional theoretical models are limited to specific problems and are not applicable to many real-life problems. Consequently, design specifications mostly rely on empirical equations derived from laboratory tests at the component level. It is desirable to develop new analysis methods, capable of harnessing material-level test parameters. To overcome limitations and shortcomings of models based on continuum mechanics and fracture mechanics, Stewart Silling introduced the concept of peridynamics in 1998. Similar to molecular dynamics, peridynamic modeling of a physical structure involves simulating interacting particles subjected to an empirical force field. The evolution of interacting particles determines the deformation of the structure at a given time due to the applied boundary condition. As a particle-based model, peridynamics requires the repeated evaluation of many particle interactions which is computationally demanding. However, with todays inexpensive computing hardware, parallel algorithms can be utilized to run such problems on multi-node supercomputers with fast interconnects. However, existing codes tend to be domain-specific with too many built-in physical assumptions. In this work, a novel method for parallelization of any particle-based simulation is presented which is quite general and suitable for simulating diverse physical structures. A scalable parallel code for molecular dynamics and peridynamics simulation, PDQ, is described which implements a novel wall method parallelization algorithm, developed as part of this thesis. PDQ partitions the geometric domain of a problem across multi-nodes while the physics is left open to the user to decide whether to simulate a solvated protein or alloy grain boundary at the atomic scale or to simulate cracking phenomena in concrete via peridynamics. A further extension of PDQ brings more flexibility by allowing the user to define any desired number of degrees of freedom for each particle in a peridynamics simulation. At the end of this thesis, plain, reinforced and prestressed concrete benchmark problems are simulated using PDQ and the results are compared to available design code equations or analytical solutions. This research is a step toward next level of computational modeling of reinforced concrete structures and the revolutionizing of how concrete is analyzed and also how concrete structures are designed.\u2
A spectral scheme for Kohn-Sham density functional theory of clusters
Starting from the observation that one of the most successful methods for
solving the Kohn-Sham equations for periodic systems -- the plane-wave method
-- is a spectral method based on eigenfunction expansion, we formulate a
spectral method designed towards solving the Kohn-Sham equations for clusters.
This allows for efficient calculation of the electronic structure of clusters
(and molecules) with high accuracy and systematic convergence properties
without the need for any artificial periodicity. The basis functions in this
method form a complete orthonormal set and are expressible in terms of
spherical harmonics and spherical Bessel functions. Computation of the occupied
eigenstates of the discretized Kohn-Sham Hamiltonian is carried out using a
combination of preconditioned block eigensolvers and Chebyshev polynomial
filter accelerated subspace iterations. Several algorithmic and computational
aspects of the method, including computation of the electrostatics terms and
parallelization are discussed. We have implemented these methods and algorithms
into an efficient and reliable package called ClusterES (Cluster Electronic
Structure). A variety of benchmark calculations employing local and non-local
pseudopotentials are carried out using our package and the results are compared
to the literature. Convergence properties of the basis set are discussed
through numerical examples. Computations involving large systems that contain
thousands of electrons are demonstrated to highlight the efficacy of our
methodology. The use of our method to study clusters with arbitrary point group
symmetries is briefly discussed.Comment: Manuscript submitted (with revisions) to Journal of Computational
Physic
Chebyshev polynomial filtered subspace iteration in the Discontinuous Galerkin method for large-scale electronic structure calculations
The Discontinuous Galerkin (DG) electronic structure method employs an
adaptive local basis (ALB) set to solve the Kohn-Sham equations of density
functional theory (DFT) in a discontinuous Galerkin framework. The adaptive
local basis is generated on-the-fly to capture the local material physics, and
can systematically attain chemical accuracy with only a few tens of degrees of
freedom per atom. A central issue for large-scale calculations, however, is the
computation of the electron density (and subsequently, ground state properties)
from the discretized Hamiltonian in an efficient and scalable manner. We show
in this work how Chebyshev polynomial filtered subspace iteration (CheFSI) can
be used to address this issue and push the envelope in large-scale materials
simulations in a discontinuous Galerkin framework. We describe how the subspace
filtering steps can be performed in an efficient and scalable manner using a
two-dimensional parallelization scheme, thanks to the orthogonality of the DG
basis set and block-sparse structure of the DG Hamiltonian matrix. The
on-the-fly nature of the ALBs requires additional care in carrying out the
subspace iterations. We demonstrate the parallel scalability of the DG-CheFSI
approach in calculations of large-scale two-dimensional graphene sheets and
bulk three-dimensional lithium-ion electrolyte systems. Employing 55,296
computational cores, the time per self-consistent field iteration for a sample
of the bulk 3D electrolyte containing 8,586 atoms is 90 seconds, and the time
for a graphene sheet containing 11,520 atoms is 75 seconds.Comment: Submitted to The Journal of Chemical Physic
Parallelization solutions for the YNANO Discontinua Simulations.
PhDIn the context of constant and fast progresses in nano technology, discontinua based
computation simulations are becoming increasingly important, especially in the context
of virtual experimentations. The efficiency of discontinua based nanoscale simulations
are still limited by CPU capacity (the number of simulation particles in the
system).
It is accepted that parallelization will play an important role in solving this problem.
In this thesis, two parallelization approaches have been undertaken to parallelize the
YNANO discontinua simulations. The scope of the work includes parallelization of
the YNANO using the shared-memory approach OpenMP and the distributed-memory
approach MPI, and also includes a novel MR_PB linear contact detection algorithm
which can be used under periodic boundary conditions.
The developed MPI parallelization solutions are compatible with the MR linear
contact detection algorithm used in the sequential YNANO, the developed solutions
preserves the linearity of both MR_Sort and MR_Search algorithm.
The overall performance and scalability of the parallelization has been studied using
nanoscale simulations in fluid dynamics and aerodynamics
Multi-Architecture Monte-Carlo (MC) Simulation of Soft Coarse-Grained Polymeric Materials: SOft coarse grained Monte-carlo Acceleration (SOMA)
Multi-component polymer systems are important for the development of new
materials because of their ability to phase-separate or self-assemble into
nano-structures. The Single-Chain-in-Mean-Field (SCMF) algorithm in conjunction
with a soft, coarse-grained polymer model is an established technique to
investigate these soft-matter systems. Here we present an im- plementation of
this method: SOft coarse grained Monte-carlo Accelera- tion (SOMA). It is
suitable to simulate large system sizes with up to billions of particles, yet
versatile enough to study properties of different kinds of molecular
architectures and interactions. We achieve efficiency of the simulations
commissioning accelerators like GPUs on both workstations as well as
supercomputers. The implementa- tion remains flexible and maintainable because
of the implementation of the scientific programming language enhanced by
OpenACC pragmas for the accelerators. We present implementation details and
features of the program package, investigate the scalability of our
implementation SOMA, and discuss two applications, which cover system sizes
that are difficult to reach with other, common particle-based simulation
methods
Two-level Chebyshev filter based complementary subspace method: pushing the envelope of large-scale electronic structure calculations
We describe a novel iterative strategy for Kohn-Sham density functional
theory calculations aimed at large systems (> 1000 electrons), applicable to
metals and insulators alike. In lieu of explicit diagonalization of the
Kohn-Sham Hamiltonian on every self-consistent field (SCF) iteration, we employ
a two-level Chebyshev polynomial filter based complementary subspace strategy
to: 1) compute a set of vectors that span the occupied subspace of the
Hamiltonian; 2) reduce subspace diagonalization to just partially occupied
states; and 3) obtain those states in an efficient, scalable manner via an
inner Chebyshev-filter iteration. By reducing the necessary computation to just
partially occupied states, and obtaining these through an inner Chebyshev
iteration, our approach reduces the cost of large metallic calculations
significantly, while eliminating subspace diagonalization for insulating
systems altogether. We describe the implementation of the method within the
framework of the Discontinuous Galerkin (DG) electronic structure method and
show that this results in a computational scheme that can effectively tackle
bulk and nano systems containing tens of thousands of electrons, with chemical
accuracy, within a few minutes or less of wall clock time per SCF iteration on
large-scale computing platforms. We anticipate that our method will be
instrumental in pushing the envelope of large-scale ab initio molecular
dynamics. As a demonstration of this, we simulate a bulk silicon system
containing 8,000 atoms at finite temperature, and obtain an average SCF step
wall time of 51 seconds on 34,560 processors; thus allowing us to carry out 1.0
ps of ab initio molecular dynamics in approximately 28 hours (of wall time).Comment: Resubmitted version (version 2
Parallel simulation of particle dynamics with application to micropolar peridynamic lattice modeling of reinforced concrete Structures
As the first goal of this thesis, we will explain a general purpose parallel particle dynamics code (pdQ2). We describe the re-architecting of pdQ (the MD/PD code that was developed in [Sakhavand 2011]) as pdQ2. pdQ2 is completely non-domain-specific in that user files are clearly separated from non-user files and no #ifdefs exist in the code. Thus, it operates as a particle simulation engine that is capable of executing any parallel particle dynamics model. As in the original pdQ, users can customize their own physical models without having to deal with complexities such as parallelization, but the ease of extensibility has been significantly improved. It is shown that pdQ2 is about four times as fast as pdQ using parallel supercomputers. In the second part of the thesis, we will model reinforced concrete structures based on peridynamic theory [Silling 1998]. We discard the continuum mechanics paradigm completely, and model reinforced concrete by introducing the micropolar peridynamic lattice model (MPLM)\u27. The MPLM models a structure as a close-packed particle lattice. In the MPLM, rather than viewing the structure as collection of truss or beam elements (as with traditional lattice models), the model is viewed as collection of particle masses (as with peridynamic models). The MPLM uses a finite number of equally-spaced interacting particles of finite mass. Thus, it does not need any ad hoc discretization and it is more straightforward to implement computationally. Also, the MPLM is conceptually simpler than both the lattice and peridynamic models [Gerstle et al. 2012]. After defining the MPLM, its application to reinforced concrete structures is investigated through several examples using pdQ2.\u2
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