Buckling of cylindrical shells under the action of nonuniform external pressure

Over the last few decades, storage tanks have become bigger and thinner. Because of this, the buckling capacity of these cylindrical shells may well be the determining factor of shell thickness. In this paper, the critical buckling load of isotropic and orthotropic cylinders subjected to different types of wind load distributions is investigated. The prebuckling displacements are obtained by using the membrane theory of shell analysis. The principle of minimum potential energy in conjunction with Ritz's approach is used to obtain the stability matrix. The size of the stability matrix in this analysis is (81 × 81). By solving the stability matrix as an eigenvalue problem, the critical pressures are obtained as eigenvalues and the deflection shapes as eigenvectors. In the present study cylindrical shells of various dimensions, which are fixed at the base and free at the top, are investigated. The buckling load curves for isotropic and orthotropic cylinders of various dimensions are given for practical use.     

Rigid body constraints realized in massively-parallel molecular dynamics on graphics processing units

Molecular dynamics (MD) methods compute the trajectory of a system of point particles in response to a potential function by numerically integrating Newton?s equations of motion. Extending these basic methods with rigid body constraints enables composite particles with complex shapes such as anisotropic nanoparticles, grains, molecules, and rigid proteins to be modeled. Rigid body constraints are added to the GPU-accelerated MD package, HOOMD-blue, version 0.10.0. The software can now simulate systems of particles, rigid bodies, or mixed systems in microcanonical (NVE), canonical (NVT), and isothermal-isobaric (NPT) ensembles. It can also apply the FIRE energy minimization technique to these systems. In this paper, we detail the massively parallel scheme that implements these algorithms and discuss how our design is tuned for the maximum possible performance. Two different case studies are included to demonstrate the performance attained, patchy spheres and tethered nanorods. In typical cases, HOOMD-blue on a single GTX 480 executes 2.5–3.6 times faster than LAMMPS executing the same simulation on any number of CPU cores in parallel. Simulations with rigid bodies may now be run with larger systems and for longer time scales on a single workstation than was previously even possible on large clusters.     

A strategy for detecting extreme eigenvalues bounding gaps in the discrete spectrum of self-adjoint operators

For a self-adjoint linear operator with a discrete spectrum or a Hermitian matrix, the “extreme” eigenvalues define the boundaries of clusters in the spectrum of real eigenvalues. The outer extreme ones are the largest and the smallest eigenvalues. If there are extended intervals in the spectrum in which no eigenvalues are present, the eigenvalues bounding these gaps are the inner extreme eigenvalues.We will describe a procedure for detecting the extreme eigenvalues that relies on the relationship between the acceleration rate of polynomial acceleration iteration and the norm of the matrix via the spectral theorem, applicable to normal matrices. The strategy makes use of the fast growth rate of Chebyshev polynomials to distinguish ranges in the spectrum of the matrix which are devoid of eigenvalues.The method is numerically stable with regard to the dimension of the matrix problem and is thus capable of handling matrices of large dimension. The overall computational cost is quadratic in the size of a dense matrix; linear in the size of a sparse matrix. We verify computationally that the algorithm is accurate and efficient, even on large matrices.     

网络拓扑的超能整循环图构造

循环图是一类重要的网络拓扑结构图,在并行计算和分布计算中发挥重要作用。图[G]的能量[E(G)]定义为图的特征值的绝对值之和。具有[n]个顶点的图[G]称为超能图如果图[G]的能量[E(G)>2n-2]。一个图称为循环图,若它是循环群上的Cayley图,即它的邻接矩阵是一个循环矩阵;整循环图是指循环图的特征值全为整数。借助Ramanujans和,利用Euler函数和Mobius函数,讨论了整循环图的超能性。利用Cartesian积图给出了一个构造超能整循环图的方法。     

大规模3D并行分层可扩展矩阵乘法的递阶优化方法

为进一步提高大规模平台上可扩展矩阵乘法的并行计算效率,提出一种并行分层可扩展矩阵乘法的递阶优化方法。首先,在可扩展矩阵乘法算法(SMM)算法枢轴行和枢轴列通信研究基础上,利用分层方式在更高等级上对网格进行矩形群划分,实现矩阵乘法的二维计算向三维计算转变,并设计对应的集群内通信和集群间通信过程,实现SMM乘法的递阶并行优化(HSMM);其次,对所提HSMM算法进行理论分析,分情况对其通信成本进行分析和预测,推导出最佳计算成本的集群数选取方式;最后,通过在Grid5000和BlueGene/P测试平台实验,验证了所提算法有效性和理论分析的正确性。     

On the implementation of rate-independent standard dissipative solids at finite strain – Variational constitutive updates

This paper is concerned with an efficient, variationally consistent, implementation for rate-independent dissipative solids at finite strain. More precisely, focus is on finite strain plasticity theory based on a multiplicative decomposition of the deformation gradient. Adopting the formalism of standard dissipative solids which allows to describe constitutive models by means of only two potentials being the Helmholtz energy and the yield function (or equivalently, a dissipation functional), finite strain plasticity is recast into an equivalent minimization problem. In contrast to previous models, the presented framework covers isotropic and kinematic hardening as well as isotropic and anisotropic elasticity and yield functions. Based on this approach a novel numerical implementation representing the main contribution of the paper is given. In sharp contrast to by now classical approaches such as the return-mapping algorithm and analogously to the theoretical part, the numerical formulation is variationally consistent, i.e., all unknown variables follow naturally from minimizing the energy of the considered system. Consequently, several different numerically efficient and robust optimization schemes can be directly employed for solving the resulting minimization problem. Extending previously published works on variational constitutive updates, the advocated model does not rely on any material symmetry and therefore, it can be applied to a broad range of different plasticity theories. As two examples, an anisotropic Hill-type and a Barlat-type model are implemented. Numerical examples demonstrate the applicability and the performance of the proposed implementation.     

A new front-tracking method to model anisotropic grain and phase boundary motion in rocks

Microstructures of rocks play an important role in determining rheological properties and help to reveal the processes that lead to their formation. Some of these processes change the microstructure significantly and may thus have the opposite effect in obliterating any fabrics indicative of the previous history of the rocks. One of these processes is grain boundary migration (GBM). During static recrystallisation, GBM may produce a foam texture that completely overprints a pre-existing grain boundary network and GBM actively influences the rheology of a rock, via its influence on grain size and lattice defect concentration.In this paper we present a new front-tracking method to simulate GBM. Generally, any movement of boundaries is driven by the minimisation of the internal free energy of a system. The new method moves boundaries along the energy gradient towards a lower total energy state of the system. The calculation of the energy gradient is not necessarily limited to (an)isotropic boundary energies but may also include metamorphism, melting, reaction energies, surface energies and elastic stresses, etc. Two examples are included in this paper where we simulate grain growth with isotropic boundary energy functions and grain growth with isotropic and anisotropic boundary energy functions in a system with a melt present.     

Improving the energy efficiency of data-intensive applications running on clusters

Abstract

As an alternative to traditional computing architecture, cloud computing now is rapidly growing. However, it is based on models like cluster computing in general. Now supercomputers are getting more and more powerful, helping scientists have more indepth understanding of the world. At the same time, clusters of commodity servers have been mainstream in the IT industry, powering not only large Internet services but also a growing number of data-intensive scientific applications, such as MPI based deep learning applications. In order to reduce the energy cost, more and more efforts are made to improve the energy consumption of HPC systems. Because I/O accesses account for a large portion of the execution time for data intensive applications, it is critical to design energy-aware parallel I/O functions for addressing challenges related to HPC energy efficiency. As the de facto standard for designing parallel applications in cluster environment, the Message Passing Interface has been widely used in high performance computing, therefore, getting the energy consumption information of MPI applications is critical for improving the energy efficiency of HPC systems. In this work we first present our energy measurement tool, a software framework that eases the energy collection in cluster environment. And then we present an approach which can optimise the parallel I/O operation’s energy efficiency. The energy scheduling algorithm is evaluated in a cluster.     

Energy approach to stability analysis of the linear stationary dynamic systems

A new approach was proposed to analyze the stability of the linear continuous stationary dynamic systems. It is based on the decomposition of a square H₂ norm of the transfer function of the dynamic system into parts corresponding either to particular eigenvalues of the system matrix, or to pairwise combinations of these eigenvalues. The spectral decompositions of a square H₂ norm of the transfer function with multiple poles were obtained using the residues of the transfer function and their derivatives. Exact analytical expressions for calculation of the quadratic forms of the corresponding expansions were derived for an arbitrary location of the eigenvalues in the left half-plane. The obtained decompositions allow one to characterize the contribution of individual eigen-components or their pairwise combinations into the asymptotic variation of the system energy. We propose the energy criterion for estimation of the system stability margins that uses an evaluation of energy accumulated in a group of weakly stable system modes. This approach is illustrated by calculating the energy of a band-pass filter.     

Implementation of the lanczos method for structural vibration analysis on a parallel computer

The eigenvalue problem associated with structural vibration analysis is a major, computationally-intensive activity in large-scale finite element calculations. Advances in parallel computers together with appropriate solution methods have the potential for providing high-speed computational power to aid eigenvalue solutions for these large problems. The key to exploiting this potential is the development of appropriate methods tailored for such parallel computers. This paper reports on experiences from a study involving the implementation of the Lanczos method on a parallel computer. The results of this study show that introducing shifts, assigning each processor a different region in the eigenvalue spectrum, and implementing the Lanczos calculation steps in parallel is an effective strategy for speeding up calculations. This approach provides good parallel performance and easy balance of processor workload. Two example vibration problems were solved to assess the behavior of the Lanczos implementation. The test-problem results include examples of the Lanczos phenomenon where lack of orthogonality in the vectors can result in spurious eigenvalues. Tests were incorporated in the parallel calculations which detected these spurious eigenvalues. The parallel eigenvalue algorithm demonstrates that significant speedups in calculation time can be realized over traditional sequential methods.     

A projection method for the numerical solution of linear systems in separable stiff differential equations

This paper deals with the efficient implementation of implicit methods for solving stiff ODEs, in the case of Jacobians with separable sets of eigenvalues. For solving the linear systems required we propose a method which is particularly suitable when the large eigenvalues of the Jacobian matrix are few and well separated from the small ones. It is based on a combination of an initial iterative procedure, which reduces the components of the vector error along to the nondominant directions of J and a projection Krylov method which reduces the components of the vector error along to the directions corresponding to the large eigenvalues. The technique solves accurately and cheaply the linear systems in the Newton's method, and computes the number of stiff eigenvalues of J when this information is not explicitly available. Numerical results are given as well as comparisons with the LSODE code.     

Spectral properties of Toeplitz-plus-Hankel matrices

We study asymptotic and uniform properties of eigenvalues of a large class of real symmetric matrices that can be decomposed into the sum of a Toeplitz matrix and a Hankel matrix. In particular, we show that their properties are essentially driven by those of the Toeplitz part. A special subclass of structured matrices arising in an approximation problem is analyzed in detail.     

A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion

Many differential methods for the recovery of the optic flow field from an image sequence can be expressed in terms of a variational problem where the optic flow minimizes some energy. Typically, these energy functionals consist of two terms: a data term, which requires e.g. that a brightness constancy assumption holds, and a regularizer that encourages global or piecewise smoothness of the flow field. In this paper we present a systematic classification of rotation invariant convex regularizers by exploring their connection to diffusion filters for multichannel images. This taxonomy provides a unifying framework for data-driven and flow-driven, isotropic and anisotropic, as well as spatial and spatio-temporal regularizers. While some of these techniques are classic methods from the literature, others are derived here for the first time. We prove that all these methods are well-posed: they posses a unique solution that depends in a continuous way on the initial data. An interesting structural relation between isotropic and anisotropic flow-driven regularizers is identified, and a design criterion is proposed for constructing anisotropic flow-driven regularizers in a simple and direct way from isotropic ones. Its use is illustrated by several examples.     

New results on the separation of matrix eigenvalues and theclustering of matrix elements

The problem of separation of matrix eigenvalues is considered in this paper. Necessary and sufficient conditions are given for a matrix to have all eigenvalues contained in an open set defined on the complex plane by a separable polynomial γ(z₁, z₂). The problem of clustering of matrix elements is also considered. In this case, a set of matrices is identified, having all eigenvalues in a given subset of the complex plane. The presented results can be useful in both robust stability analysis and the design of control systems     

Parallel analysis of rotationally periodic structures

A parallel finite element solution algorithm for analysing large rotationally periodic structures on MIMD parallel computer systems is described. For a rotationally periodic structure, the global stiffness matrix under the corresponding symmetric coordinate system is periodic, i.e. possesses isomorphic properties, so that the global equation system can be transformed into a number of smaller equation systems which are fully decoupled. These decoupled equation systems then can be solved simultaneously on a multiprocessor parallel computer. The algorithm also generates the decoupled equation systems in parallel, without explicitly assembling the global stiffness matrix of the structure. A prototype implementation of the algorithm on an array of transputers is presented, and the efficiency of the program is also studied in this paper. Finally, a numerical example is given to demonstrate the speedup of the program.     

Application of Mathematical Modeling to Determine the Thermoelastic Characteristics of Nano-Reinforced Composites

A two-level mathematical model is constructed to describe the thermomechanical interaction between structural elements of a composite (nanoclusters formed by randomly distributed anisotropic single-walled carbon nanotubes and matrix particles) and an isotropic medium possessing the desired thermoelastic characteristics. This model was first employed to obtain the thermoelastic properties of a nanocluster by the self-consistency method and then the same technique was used to describe the thermomechanical interaction of nanoclusters with an isotropic matrix of the composite. A comparative analysis of the calculated dependences for the elastic moduli of the composite and its thermal coefficient of linear expansion was carried out with two-sided estimates of these characteristics based on the dual variational formulation of the thermoelasticity problem. For comparison, the results of a numerical experiment are also used. The presented relationships make it possible to predict the thermoelastic properties of promising composites reinforced by nanoclusters.     

Random eigenvalue problems for large systems

The inherent uncertainties in geometry, material properties, etc. of engineering structures can be represented by stochastic models, where the parameters are described by probabilistic laws. Results from any analysis based on stochastic models inherit probabilistic information as well, which can be used e.g. for reliability analysis. Particularly in linear dynamics of structures the calculation and analysis of random eigenvalues and eigenvectors is crucial. A quite versatile, however computationally intensive way to analyze such systems is direct Monte Carlo simulation. In this paper procedures are shown, which allow a significant reduction of computational efforts of the simulation using a subspace iteration scheme with “optimally” selected start-vectors. As the subspace iteration procedure, although quite accurate, requires a factorization of the stiffness matrix, as an alternative, a procedure based on component mode synthesis is suggested.