Nonlinear electroelasticity is not a new problem, its theory involving nonlinear deformation and nonlinear material behavior has been well established. However, the numerical simulation of nonlinear electroelasticity is until now still far from satisfactory, especially when the interaction between electric fields and matter cannot be considered as confined in the finite space occupied by the matter. It is understood that under the application of an electric field, the deformation of an elastic body is governed not always by what happens inside the material body but in many cases also by the environment surrounding it. This is notably true in the case of electronic electroactive polymers, the materials that emerge today as a leading candidate in developing artificial muscles. In this work, we present a numerical analysis of nonlinear electroelasticity by assuming large deformation, nonlinear polarization and by paying attention to the contribution of the free space surrounding the bodies of interest.


Keywords: nonlinear electricity, nonlinear elasticity, nonlinear coupling, coupled BEM-FEM analysis.

In this paper the two-dimensional finite element with an embedded edge crack proposed by Potirniche et al. (2008) is improved further for crack depth ratios ranging up to 0.9 and for predicting the natural frequency of a cracked beam more accurately. The element is implemented in the commercial finite element code ABAQUS as user element subroutine. The accuracy of the proposed improved cracked element is verified by comparing the predicted, first natural frequency with available experimental data. Subsequently, a methodology to detect the crack's location and size in conjunction with the proposed improved cracked element is also presented.


Keywords: cracked finite element, user element, ABAQUS, natural frequency, crack fault diagnosis.

Particle swarm optimization is one of the evolutionary computations which is inspired by social behavior of bird flocking or fish schooling. This research focuses on the application of the particle swarm optimization to two-dimensional packing problem. Packing problem is a class of optimization problems which involve attempting to pack the items together inside a container, as densely as possible. In this study, when the arbitrary polygon-shaped packing region is given, the total number of items in the region is maximized. The optimization problem is defined not as the discrete-value optimization problem but as the continuous- value optimization problem. The problem is solved by two algorithms, original and improved PSOs. In the original PSO, the particle position vector is updated by the best particle position in all particles (global best particle position) and the best position in previous positions of each particle (personal best position). The improved PSO utilizes, in addition to them, the second best particle position in all particles (global second best particle position) in the stochastic way. In the numerical example, the algorithms are applied to three problems. The results show that the improved PSO can pack more items than the original PSO and therefore, number of the successful simulations is also improved.


Keywords: packing problem, particle swarm optimization, global best position, global second best position, personal best position.

A brief overview of causality analysis (CA) methods applied to MD simulations data for model biomolec ular systems is presented. A CausalMD application for postprocessing of MD data was designed and implemented. MD simulations of two model systems, porphycene (ab initio MD) and HIV-1 protease (coarse-grained MD) were carried out and analyzed. Granger's causality methodology based on a Multivariate Autoregressive (MVAR) formalism, followed by the Directed Transfer Function (DTF) analysis was applied. A novel approach based on the descriptors of local structure was also presented and preliminary results were reported. Casuality analyses are required for a better understanding of biomolecular functioning mechanisms. In particular, such analyses can link physics-based structural dynamics with functions inferred from molecular evolution processes. Current limitations and future developments of the presented methodologies are indicated.


Keywords: causality analysis, signal analysis, local descriptors, alignment, MVAR, Directed Transfer Function, molecular dynamics, porphycene, HIV-1 protease, molecular function, molecular evolution.

In this work, we discuss the role of probability in providing the most appropriate multiscale based uncertainty quantification for the inelastic nonlinear response of heterogeneous materials undergoing localized failure. Two alternative approaches are discussed: i) the uncertainty quantification in terms of constructing the localized failure models with random field as parameters of failure criterion, ii) the uncertainty quantification in terms of the corresponding Bayesian updates of the corresponding evolution equation. The detailed developments are presented for the model problem of cement-based composites, with a two- phase meso-scale representation of material microstructure, where the uncertainty stems from the random geometric arrangement of each phase. Several main ingredients of the proposed approaches are discussed in detail, including microstructure approximation with a structured mesh, random field KLE representation, and Bayesian update construction. We show that the first approach is more suitable for the general case where the loading program is not known and the best one could do is to quantify the randomness of the general failure criteria, whereas the second approach is more suitable for the case where the loading program is prescribed and one can quantify the corresponding deviations. More importantly, we also show that models of this kind can provide a more realistic prediction of localized failure phenomena including the probability based interpretation of the size effect, with failure states placed anywhere in-between the two classical bounds defined by continuum damage mechanics and linear fracture mechanics.


Keywords: multiscale analysis, inelastic behavior, uncertainty quantification, fracture, size-effect.