A simple non-linear global-local finite element methodology for composite structures

A simple non-linear global-local finite element methodology is presented. A global coarse model, using 2-D shell elements, is solved non-linearly and the displacements and rotations around a region of interest are applied, as displacement boundary conditions, to a refined local 3-D model using Kirchhoff plate assumptions. The global elements' shape functions are used to interpolate between nodes. The local model is then solved non-linearly with an incremental scheme independent of that used for the global model.

Quantum and classical properties of the fields generated by two interlinked second-order non-linear interactions

We consider two interlinked non-linear interactions occurring simultaneously in a single chi((2)) crystal. Classical and quantum working regimes are considered and their peculiar properties analysed. In particular, we describe an experiment, realized in the classical regime, that verifies the holographic nature of the process, and predict, for the quantum regime, the generation of a fully inseparable tripartite Gaussian state of light that can be used to support a general 1--> 2 continuous variable telecloning protocol.

Determination of Non-linear Damping Coefficients of bottom-hinged Oscillating Wave Surge Converters Using Numerical Free Decay Tests

Linear wave theory models are commonly applied to predict the performance of bottom-hinged oscillating wave surge converters (OWSC) in operational sea states. To account for non-linear effects, the additional input of coefficients not included in the model itself becomes necessary. In ocean engineering it is
common practice to obtain damping coefficients of floating structures from free decay tests. This paper presents results obtained from experimental tank tests and numerical computational fluid dynamics simulations of OWSC’s. Agreement between numerical and experimental methods is found to be very good, with CFD providing more data points at small amplitude rotations.
Analysis of the obtained data reveals that linear quadratic-damping, as commonly used in time domain models, is not able to accurately model the occurring damping over the whole regime of rotation amplitudes. The authors
conclude that a hyperbolic function is most suitable to express the instantaneous damping ratio over the rotation amplitude and would be the best choice to be used in coefficient based time domain models.

Can Leakage Models Be More Efficient? Non-Linear Models in Side Channel Attacks

In the last decade, many side channel attacks have been published in academic literature detailing how to efficiently extract secret keys by mounting various attacks, such as differential or correlation power analysis, on cryptosystems. Among the most efficient and widely utilized leakage models involved in these attacks are the Hamming weight and distance models which give a simple, yet effective, approximation of the power consumption for many real-world systems. These leakage models reflect the number of bits switching, which is assumed proportional to the power consumption. However, the actual power consumption changing in the circuits is unlikely to be directly of that form. We, therefore, propose a non-linear leakage model by mapping the existing leakage model via a transform function, by which the changing power consumption is depicted more precisely, hence the attack efficiency can be improved considerably. This has the advantage of utilising a non-linear power model while retaining the simplicity of the Hamming weight or distance models. A modified attack architecture is then suggested to yield the correct key efficiently in practice. Finally, an empirical comparison of the attack results is presented.

Blind Characterisation of Sensors with Second-Order Dynamic Response

Compensation for the dynamic response of a temperature sensor usually involves the estimation of its input on the basis of the measured output and model parameters. In the case of temperature measurement, the sensor dynamic response is strongly dependent on the measurement environment and fluid velocity. Estimation of time-varying sensor model parameters therefore requires continuous textit{in situ} identification. This can be achieved by employing two sensors with different dynamic properties, and exploiting structural redundancy to deduce the sensor models from the resulting data streams. Most existing approaches to this problem assume first-order sensor dynamics. In practice, however second-order models are more reflective of the dynamics of real temperature sensors, particularly when they are encased in a protective sheath. As such, this paper presents a novel difference equation approach to solving the blind identification problem for sensors with second-order models. The approach is based on estimating an auxiliary ARX model whose parameters are related to the desired sensor model parameters through a set of coupled non-linear algebraic equations. The ARX model can be estimated using conventional system identification techniques and the non-linear equations can be solved analytically to yield estimates of the sensor models. Simulation results are presented to demonstrate the efficiency of the proposed approach under various input and parameter conditions.

Integration of shading and texture cues: testing the linear model

One of the first attempts to develop a formal model of depth cue integration is to be found in Maloney and Landy's (1989) "human depth combination rule". They advocate that the combination of depth cues by the visual sysetem is best described by a weighted linear model. The present experiments tested whether the linear combination rule applies to the integration of texture and shading. As would be predicted by a linear combination rule, the weight assigned to the shading cue did vary as a function of its curvature value. However, the weight assigned to the texture cue varied systematically as a function of the curvature value of both cues. Here we descrive a non-linear model which provides a better fit to the data. Redescribing the stimuli in terms of depth rather than curvature reduced the goodness of fit for all models tested. These results support the hypothesis that the locus of cue integration is a curvature map, rather than a depth map. We conclude that the linear comination rule does not generalize to the integration of shading and texture, and that for these cues it is likely that integration occurs after the recovery of surface curvature.

Adsorption isotherm models for basic dye adsorption by peat in single and binary component systems

Coloured effluents from textile industries are a problem in many rivers and waterways. Prediction of adsorption capacities of dyes by adsorbents is important in design considerations. The sorption of three basic dyes, namely Basic Blue 3, Basic Yellow 21 and Basic Red 22, onto peat is reported. Equilibrium sorption isotherms have been measured for the three single component systems. Equilibrium was achieved after twenty-one days. The experimental isotherm data were analysed using Langmuir, Freundlich, Redlich-Peterson, Temkin and Toth isotherm equations. A detailed error analysis has been undertaken to investigate the effect of using different error criteria for the determination of the single component isotherm parameters and hence obtain the best isotherm and isotherm parameters which describe the adsorption process. The linear transform model provided the highest R2 regression coefficient with the Redlich-Peterson model. The Redlich-Peterson model also yielded the best fit to experimental data for all three dyes using the non-linear error functions. An extended Langmuir model has been used to predict the isotherm data for the binary systems using the single component data. The correlation between theoretical and experimental data had only limited success due to competitive and interactive effects between the dyes and the dye-surface interactions.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.

Parameter estimation and equation formulation in Business Dynamics

System Dynamics enables modelling and simulation of highly non-linear feedback systems to predict future system behaviour. Parameter estimation and equation formulation are techniques in System Dynamics, used to retrieve the values of parameters or the equations for ?ows and/or variables. These techniques are crucial for the annotations and thereafter the simulation. This paper critically examines existing and well established approaches in parameter estimation and equation formulation along with their limitations, identifying performance gaps as well as providing directions for potential future research.

Implementation of a non-orthogonal model for the finite element simulation of textile composite draping

In the pursuit of producing high quality, low-cost composite aircraft structures, out-of-autoclave manufacturing processes for textile reinforcements are being simulated with increasing accuracy. This paper focuses on the continuum-based, finite element modelling of textile composites as they deform during the draping process. A non-orthogonal constitutive model tracks yarn orientations within a material subroutine developed for Abaqus/Explicit, resulting in the realistic determination of fabric shearing and material draw-in. Supplementary material characterisation was experimentally performed in order to define the tensile and non-linear shear behaviour accurately. The validity of the finite element model has been studied through comparison with similar research in the field and the experimental lay-up of carbon fibre textile reinforcement over a tool with double curvature geometry, showing good agreement.

Dynamic identification of building structures: new strategies:Meccanica dei materiali e delle strutture

In this paper the evolution of a time domain dynamic identification technique based on a statistical moment approach is presented. This technique can be used in the case of structures under base random excitations in the linear state and in the non linear one. By applying Itoˆ stochastic calculus, special algebraic equations can be obtained depending on the statistical moments of the response of the system to be identified. Such equations can be used for the dynamic identification of the mechanical parameters and of the input. The above equations, differently from many techniques in the literature, show the possibility of obtaining the identification of the dissipation characteristics independently from the input. Through the paper the first formulation of this technique, applicable to non linear systems, based on the use of a restricted class of the potential models, is presented. Further a second formulation of the technique in object, applicable to each kind of linear systems and based on the use of a class of linear models, characterized by a mass proportional damping matrix, is described.