On operator split technique for the time integration within finite strain viscoplasticity in explicit FEM

In this contribution, the operator split technique is applied to the time integration within viscoplasticity for explicit FEM. As an example, the finite strain viscoplastic material model of Shutov and Kreißig is analyzed. In the new solution scheme, some evolution equations are solved using an explicit update formula for implicit time stepping. The solution procedure is split into three steps: an elastic predictor and two viscoplastic corrector steps. Aspects of accuracy and stability of the algorithm are discussed. As shown, the proposed method is superior compared to a fully explicit integration of evolution equations. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Non‐Gaussian invariant measures for the Majda model of decaying turbulent transport

The problem of turbulent transport of a scalar field by a random velocity field is considered. The scalar field amplitude exhibits rare but very large fluctuations whose typical signature are fatter than Gaussian tails for the probability distribution of the scalar. The existence of such large fluctuations is related to clustering phenomena of the Lagrangian paths within the flow. This suggests an approach to turn the large deviation problem for the scalar field into a small deviation, or small ball, problem for some appropriately defined process measuring the spreading with time of the Lagrangian paths. Here, such a methodology is applied to a model proposed by Majda consisting of a white‐in‐time linear shear flow and some generalizations of it where the velocity field has finite, or even infinite, correlation time. The non‐Gaussian invariant measure for the (reduced) scalar field is derived and, in particular, it is shown that the one‐point distribution of the scalar has stretched exponential tails, with a stretching exponent depending of the parameters in the model. Different universality classes for the scalar behavior are identified which, all other parameters being kept fixed, display a one‐to‐one correspondence with a exponent measuring time persistence effects in the velocity field. © 2001 John Wiley & Sons, Inc.     

波动方程外区域问题基于自然边界归化的区域分解算法(英文)

本文以二维波动方程为例 ,研究基于自然边界归化的一种区域分解算法 .首先将控制方程对时间进行离散化 ,得到关于时间步长离散化格式 ,对每一时间步长求解一椭圆型外问题 ;然后引入两条人工边界 ,提出了 Schwarz交替算法 ,给出了算法的收敛性 ,并对圆外区域研究了压缩因子     

Exact Algorithm for the Surrogate Dual of an Integer Programming Problem: Subgradient Method Approach

One of the critical issues in the effective use of surrogate relaxation for an integer programming problem is how to solve the surrogate dual within a reasonable amount of computational time. In this paper, we present an exact and efficient algorithm for solving the surrogate dual of an integer programming problem. Our algorithm follows the approach which Sarin et al. (Ref. 8) introduced in their surrogate dual multiplier search algorithms. The algorithms of Sarin et al. adopt an ad-hoc stopping rule in solving subproblems and cannot guarantee the optimality of the solutions obtained. Our work shows that this heuristic nature can actually be eliminated. Convergence proof for our algorithm is provided. Computational results show the practical applicability of our algorithm.     

A locally adaptive time stepping algorithm for the solution to reaction diffusion equations on branched structures

In this paper, we present a numerical method for solving reaction-diffusion equations on one dimensional branched structures. Through the use of a simple domain decomposition scheme, the many branches are decoupled so that the equations can be solved as a system of smaller problems that are tri-diagonal. This technique allows for locally adaptive time stepping, in which the time step used in each branch is determined by local activity. Though the method is presented in the specific context of electrical activity in neural systems, it is sufficiently general that it can be applied to other classes of reaction-diffusion problems and higher dimensions. Information in neurons, which can be effectively modeled as one-dimensional branched structures, is carried in the form of electrical impulses called action potentials. The model equations, based on the Hodgkin-Huxley cable equations, are a set of reaction equations coupled to a single diffusion process. Locally adaptive time stepping schemes are well suited to neural simulations due to the spatial localization of activity. The algorithm significantly reduces the computational cost compared to existing methods, especially for large scale simulations.     

Deformation Argument under PSP Condition and Applications

In this paper we introduce a new deformation argument, in which C~0-group action and a new type of Palais-Smale condition PSP play important roles. This type of deformation results are studied in [17, 21] and has many different applications [10,11, 17, 21] et al. Typically it can be applied to nonlinear scalar field equations. We give a survey in an abstract functional setting. We also present another application to nonlinear elliptic problems in strip-like domains. Under conditions related to [5,6], we show the existence of infinitely many solutions. This extends the results in [8].     

Numerical calculation of singular integrals appearing in three-dimensional potential flow problems

Two approximate methods for calculating singular integrals appearing in the numerical solution of three-dimensional potential flow problems are presented. The first method is a self-adaptive, fully numerical method based on special copy formulas of Gaussian quadrature rules. The singularity is treated by refining the partitions of the copy formula in the vicinity of the singular point. The second method is a semianalytic method based on asymptotic considerations. Under the small curvature hypothesis, asymptotic expansions are derived for the integrals that are involved in the calculation of the scalar potential, the velocity as well as the deformation field induced from curved quadrilateral surface elements. Compared to other methods, the proposed integration schemes, when applied to practical flow field calculations, require less computational effort.     

An analysis of convergence for a learning version of the subspace method

The learning subspace method of pattern recognition has been earlier introduced by Kohonen et al. in a speech recognition application, where the phonemes to be classified are given as spectral representations. In that method, the class subspaces are updated recursively using special rotation matrices, which depend on the training vectors entering one at a time. Here the learning algorithm based on these operators is represented in a general mathematical form, and almost sure convergence is shown to a given criterion that is a function of the statistics of the training set as well as of a set of nonrandom but free parameters. The proof employs current techniques in stochastic approximation theory. For illustration, the resulting classification criterion is then applied to a concrete pattern recognition situation with suitably chosen parameter values.     

Worst case complexity of multivariate Feynman–Kac path integration

We study the multivariate Feynman–Kac path integration problem. This problem was studied in Plaskota et al. (J. Comp. Phys. 164 (2000) 335) for the univariate case. We describe an algorithm based on uniform approximation, instead of the L₂-approximation used in Plaskota et al. (2000). Similarly to Plaskota et al. (2000), our algorithm requires extensive precomputing. We also present bounds on the complexity of our problem. The lower bound is provided by the complexity of a certain integration problem, and the upper bound by the complexity of the uniform approximation problem. The algorithm presented in this paper is almost optimal for the classes of functions for which uniform approximation and integration have roughly the same complexities.     

A micro-sphere-based remodelling formulation for arterial tissue including residual stresses

An essential property of soft biological tissues is the ability to adapt according to respective loading conditions, e.g. by means of fibre reorientation (remodelling). In particular with regard to arterial tissue, an externally unloaded state of the material is generally associated with residual stresses. In this contribution a three-dimensional micro-sphere-based constitutive model for anisotropic soft biological tissue is presented, which includes fibre-reorientation-related remodelling as well as residual stress-effects. As a key aspect of this contribution, time-dependent remodelling effects are incorporated by introducing evolution equations for the integration directions of the micro-sphere scheme, which thereby characterize the material's anisotropic properties. An appropriate remodelling approach for the orthotropic case is discussed, whereas the effect of residual stresses is additionally included in the model by means of a multiplicative decomposition of the deformation gradient tensor. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

On strong consistency of the fuzzy generalized nearest neighbor rule

The k nearest neighbor rule (k-NNR) is a well-known nonparametric decision rule in pattern classification. Fuzzy set theory has been widely used to represent the uncertainty of class membership. Several researchers extended conventional k-NNR to fuzzy k-NNR, such as Bezdek et al. [Fuzzy Sets and Systems 18 (1986) 237–256], Keller et al. [IEEE Trans. Syst. Man, and Cybern. 15(4) (1985) 580–585], Béreau and Dubuisson [Fuzzy Sets and Systems 44 (1991) 17–32]. In this paper, we describe a fuzzy generalized k-NN algorithm. This algorithm is a unified approach to a variety of fuzzy k-NNR's. Then we create the strong consistency of posterior risk of the fuzzy generalized NNR.     

An optimal solution to a three echelon supply chain network with multi-product and multi-period

Recently, Kadadevaramath et al. (2012) [1] presented a mathematical model for optimizing a three echelon supply chain network. Their model is an integer linear programming (ILP) model. In order to solve it, they developed five algorithms; four of them are based on a particle swarm optimization (PSO) method and the other is a genetic algorithm (GA). In this paper, we develop a more general mathematical model that contains the model developed by Kadadevaramath et al. (2012) [1]. Furthermore, we show that all instances proved in Kadadevaramath et al. (2012) [1] can easily be solved optimally by any integer linear programming solver.     

Damage and fracture behavior in dynamic tension tests: Modelling and numerical simulations

The continuum damage model is based on a general thermodynamic framework for the modeling of rate and temperature dependent behavior of anisotropically damaged elastic-plastic materials subjected to fast deformation. The introduction of damaged and fictitious undamaged configurations allows the definition of damage tensors and the corresponding free energy functions lead to material laws affected by damage and temperature. The damage condition and the corresponding damage rule strongly depend on stress triaxiality. Furthermore, the rate and temperature dependence is reflected in a multiplicative decomposition of the plastic hardening and damage softening functions. The macro crack behavior is characterized by a triaxiality dependent fracture criterion. The continuum damage model is implemented into LS-DYNA as user defined material model. Corresponding numerical simulations of unnotched and notched tension tests with high strain rates demonstrate the plastic and damage processes during the deformation leading to final fracture numerically predicted by an element erosion technique. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A continuum model for free growth in living materials

We focus in this work on isotropically growing materials. An adaptive algorithm is used in order to maintain a stress-free state during growth if no external loads are applied, but keeping the volume growth defined by a former kinetic. The proposed model is based on a modified multiplicative split of the deformation gradient into a growth part and an elastic part. The growth part will be isotropic if the elastic deformations are favourable, otherwise the growth will find a more comfortable direction. Three-dimensional examples based on different kinetics are presented and discussed using the numerical model. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Numerical modeling of pollution transport in flexible vegetation

In this study, we present a large eddy simulation model of the flow and scalar transport in an open channel with flexible vegetation. We propose a model for recognizing flexible vegetation to simulate the deflected height of vegetation with flexibility. A mixed scale model is modified for canopy turbulence modeling. A random walk model is used to calculate the mechanical dispersion during the scalar transport process within the canopy. A two-stage second-order nonlinear strong stability-preserving Runge–Kutta scheme is combined with the operator splitting algorithm to solve the governing equations. We verified the numerical model based on previously reported experimental data and the comparisons between the simulations and measurements were favorable. The model was then applied to simulate scalar transport within flexible vegetation, where the simulations showed that the recognition of flexible vegetation could enhance the vertical mixing and diffusion of the scalar concentration.     

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Reference分析最早由Bernardo(1979)提出的, Berger和Bernardo(1992a)做了进一步的发展. 而Berger 等(2001)提出了一个获得精确reference先验的方法, 它已经成为获取无信息先验的最成功的方法之一. 本文基于Berger等(2001)所提出的的算法, 研究了具有一般协方差结构的增长曲线模型的reference先验. 同时, 给出了相应结果的一些应用.     

On unconditionally positivity preserving DG schemes for shallow water flows with shock capturing by adaptive filtering procedures

In this work, we present an unconditionally positivity preserving implicit time integration scheme for the DG method applied to shallow water flows. For locally refined grids with very small elements, the ODE resulting from space discretization is stiff and requires implicit or partially implicit time stepping. However, for simulations including wetting and drying or regions with small water height, severe time step restrictions have to be imposed due to positivity preservation. Nevertheless, as implicit time stepping demands a significant amount of computational time in order to solve a large system of nonlinear equations for each time step we need large time steps to obtain an efficient scheme. In this context, we propose a novel approach to the strategy of positivity preservation. This new technique is based on the so-called Patankar trick and guarantees non-negativity of the water height for any time step size while still preserving conservativity. Due to this modification, the implicit scheme can take full advantage of larger time steps and is therefore able to beat explicit time stepping in terms of CPU time. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A unified discontinuous Galerkin framework for time integration

We introduce a new discontinuous Galerkin approach for time integration. On the basis of the method of weighted residual, numerical quadratures are employed in the finite element time discretization to account for general nonlinear ordinary differential equations. Many different conditions, including explicit, implicit, and symplectic conditions, are enforced for the test functions in the variational analysis to obtain desirable features of the resulting time‐stepping scheme. The proposed discontinuous Galerkin approach provides a unified framework to derive various time‐stepping schemes, such as low‐order one‐step methods, Runge–Kutta methods, and multistep methods. On the basis of the proposed framework, several explicit Runge–Kutta methods of different orders are constructed. The derivation of symplectic Runge–Kutta methods has also been realized. The proposed framework allows the optimization of new schemes in terms of several characteristics, such as accuracy, sparseness, and stability. The accuracy optimization is performed on the basis of an analytical form of the error estimation function for a linear test initial value problem. Schemes with higher formal order of accuracy are found to provide more accurate solutions. We have also explored the optimization potential of sparseness, which is related to the general compressive sensing in signal/imaging processing. Two critical dimensions of the stability region, that is, maximal intervals along the imaginary and negative real axes, are employed as the criteria for stability optimization. This gives the largest Courant–Friedrichs–Lewy time steps in solving hyperbolic and parabolic partial differential equations, respectively. Numerical experiments are conducted to validate the optimized time‐stepping schemes. Copyright © 2013 John Wiley & Sons, Ltd.