Heteroclinic cycles in lattice dynamical systems

A criterion of spatial chaos occurring in lattice dynamical systems-heteroclinic cycle-is discussed. It is proved that if the system has asymptotically stable heteroclinic cycle, then it has asymptotically stable homoclinic point which implies spatial chaos.

Modified Generalized Beam Lattice Model Associated With Fracture Of Reinforced Fiber/Particle Composites

Lattice-type model can simulate in a straightforward manner heterogeneous brittle media. Mohr-Coulomb failure criterion has recently been involved into the generalized beam (GB) lattice model, and as a result, numerical experiments on concrete under various loading conditions can be conducted. The GB lattice model is further used to investigate the reinforced fiber/particle composites instead of only particle composites as the model did before. Numerical examples are given to show the effectiveness of the modeling procedure, and influences of inclusions (particle, fiber and rebar) on the fracture processes are also discussed. (c) 2008 Elsevier Ltd. All rights reserved.

Rapidly Infrared-Assisted Cooperatively Self-Assembled Highly Ordered Multiscale Porous Materials

In this paper, cooperative self-assembly (CSA) of colloidal spheres with different sizes was studied. It was found that a complicated jamming effect makes it difficult to achieve an optimal self-assembling condition for construction of a well-ordered stacking of colloidal spheres in a relatively short growth time by CSA. Through the use of a characteristic infrared (IR) technique to significantly accelerate local evaporation on the growing interface without changing the bulk growing environment, a concise three-parameter (temperature, pressure, and IR intensity) CSA method to effectively overcome the jamming effect has been developed. Mono- and multiscale inverse opals in a large range of lattice scales can be prepared within a growth time (15-30 min) that is remarkably shorter than the growth times of several hours for previous methods. Scanning electron microscopy images and transmittance spectra demonstrated the superior crystalline and optical qualities of the resulting materials. More importantly, the new method enables optimal conditions for CSA without limitations on sizes and materials of multiple colloids. This strategy not only makes a meaningful advance in the applicability and universality of colloidal crystals and ordered porous materials but also can be an inspiration to the self-assembly systems widely used in many other fields, such as nanotechnology and molecular bioengineering.

The vector fields admitting one-parameter spatial symmetry group and their reduction

For a n-dimensional vector fields preserving some n-form, the following conclusion is reached by the method of Lie group. That is, if it admits an one-parameter, n-form preserving symmetry group, a transformation independent of the vector field is constructed explicitly, which can reduce not only dimesion of the vector field by one, but also make the reduced vector field preserve the corresponding ( n - 1)-form. In partic ular, while n = 3, an important result can be directly got which is given by Me,ie and Wiggins in 1994.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

Effects of physical parameter range on dimensionless variable sensitivity in water flooding reservoirs

The similarity criterion for water flooding reservoir flows is concerned with in the present paper. When finding out all the dimensionless variables governing this kind of flow, their physical meanings are subsequently elucidated. Then, a numerical approach of sensitivity analysis is adopted to quantify their corresponding dominance degree among the similarity parameters. In this way, we may finally identify major scaling law in different parameter range and demonstrate the respective effects of viscosity, permeability and injection rate.

Effects of physical parameter range on dimensionless variable sensitivity in water flooding reservoirs

The similarity criterion for water flooding reservoir flows is concerned with in the present paper. When finding out all the dimensionless variables governing this kind of flow, their physical meanings are subsequently elucidated. Then, a numerical approach of sensitivity analysis is adopted to quantify their corresponding dominance degree among the similarity parameters. In this way, we may finally identify major scaling law in different parameter range and demonstrate the respective effects of viscosity, permeability and injection rate.

Lattice type of fracture model for concrete

Concrete is usually described as a three-phase material, where matrix, aggregate and interface zones are distinguished. The beam lattice model has been applied widely by many investigators to simulate fracture processes in concrete. Due to the extremely large computational effort, however, the beam lattice model faces practical difficulties. In our investigation, a new lattice called generalized beam (GB) lattice is developed to reduce computational effort. Numerical experiments conducted on a panel subjected to uniaxial tension show that the GB lattice model can reproduce the load-displacement curves and crack patterns in agreement to what are observed in tests. Moreover, the effects of the particle overlay on the fracture process are discussed in detail. (C) 2007 Elsevier Ltd. All rights reserved.

Sequential Monte Carlo computation of the score and observed information matrix in state-space models with application to parameter estimation

Sequential Monte Carlo (SMC) methods are popular computational tools for Bayesian inference in non-linear non-Gaussian state-space models. For this class of models, we propose SMC algorithms to compute the score vector and observed information matrix recursively in time. We propose two different SMC implementations, one with computational complexity $\mathcal{O}(N)$ and the other with complexity $\mathcal{O}(N^{2})$ where $N$ is the number of importance sampling draws. Although cheaper, the performance of the $\mathcal{O}(N)$ method degrades quickly in time as it inherently relies on the SMC approximation of a sequence of probability distributions whose dimension is increasing linearly with time. In particular, even under strong \textit{mixing} assumptions, the variance of the estimates computed with the $\mathcal{O}(N)$ method increases at least quadratically in time. The $\mathcal{O}(N^{2})$ is a non-standard SMC implementation that does not suffer from this rapid degrade. We then show how both methods can be used to perform batch and recursive parameter estimation.