On the retrograde condensation of one or two liquids

This contribution discusses the phenomena of retrograde condensation of one or two liquids. It w1 be shown that both phenomena can be well understood. Also the relation of retrograde condensation of one liquid phase with the condensation behavior of natural gas will be discussed. Similarly that of two liquid phases with multiple phase behavior occurring in low temperature reservoir fluids will be pointed out.     

On the behaviour of rubberlike materials in compression and shear

Summary The mechanical behaviour of rubberlike materials is modelled in a phenomenological approach using a strain-energy formulation. Nonhomogeneous shear experiments on solid rubber specimens have been carried out as well as simple elongation tests on the same rubber compound. The elongation tests have been used to determine the model constants. By a comparison between experiment and numerical simulation of the nonhomogeneous shear test the predictive capabilities of the Mooney-Rivlin, the Ogden and the Besseling model have been assessed for compression-shear deformation paths. An analytical study explains the numerically observed behaviour.

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Über das Verhalten von Gummimaterialien unter Druck und Schubverformungen

Übersicht Das mechanische Verhalten von Gummimaterialien wird phänomenologisch modelliert unter Anwendung einer Formänderungsenergiefunktion. Es sind nicht-homogene Schubversuche auf dreidimensionale Versuchsproben aus Gummi so wie auch einfache Verlängerungsversuche auf Gummiproben der gleichen Zusammenstellung durchgeführt worden. Die Verlängerungsversuche sind durchgeführt worden um die Stoffkennwerte feststellen zu können. In einer Vergleichung zwischen dem Experiment und der numerischen Simulation von nicht-homogenen Schubversuchen ist die Genauigkeit des Mooney-Rivlinschen Modells, des Ogdenschen Modells und des Besselingschen Modells für kombinierte Druck-Schubverformungen festgestellt worden. In einem analytischen Studium wird das numerisch betrachtete Verhalten auseinandergesetzt.

     

Scaling probability, key to the equilibrium statistical volume distribution in foams

The equilibrium statistical volume distribution in foams, which has been previously demonstrated to be the so-called log-normal, is accounted on probability grounds by a scaling law on the bubble's volume time-evolution. This law states that the conditional probability for a given bubble to have the volume v(t + Δt), he assumption that it was v(t), to depend solely on the relationship v(t + Δt)/v(t), and in particular, to be independent of the time t. The theoretical results, however, allow to go further. Indeed, since the time dependece of its parameters (expectation and variance) are given functions of time, only two parameters are necessary to describe completely the foam.     

Thermal and chemical nanofractal relaxation

We studied shape relaxation of nano-fractal islands, during annealing, after their growth from antimony cluster deposition on graphite surface. Annealing at 180^°C shows evidence of an increase of the fractal branch width with time followed by branch fragmentation, without changing the fractal dimension. The time evolution of the width of the arm suggests the surface self-diffusion mechanism as the main relaxation process. With Monte Carlo simulations, we confirmed the observed behavior. Comparison is done with our previous results on fragmentation of nano-fractal silver islands when impurity added to the incident cluster promotes rapid fragmentation by surface self-diffusion enhancement [1].     

High-order harmonic generation with a strong laser field and an attosecond-pulse train: The Dirac-Delta comb and monochromatic limits

In recent publications, it has been shown that high-order harmonic generation can be manipulated by employing a time delayed attosecond-pulse train superposed to a strong, near-infrared laser field. It is an open question, however, which is the most adequate way to approximate the attosecond-pulse train in a semianalytic framework. Employing the strong-field approximation and saddle-point methods, we make a detailed assessment of the spectra obtained by modeling the attosecond-pulse train by either a monochromatic wave or a Dirac-Delta comb. These are the two extreme limits of a real train, which is composed by a finite set of harmonics. Specifically, in the monochromatic limit, we find the downhill and uphill sets of orbits reported in the literature, and analyze their influence on the high-harmonic spectra. We show that, in principle, the downhill trajectories lead to stronger harmonics, and pronounced enhancements in the low plateau region. These features are analyzed in terms of quantum interference effects between pairs of quantum orbits, and compared to those obtained in the Dirac-Delta limit.     

Barycentric systems and stretchability

Using a general resolution of barycentric systems we give a generalization of Tutte's theorem on convex drawing of planar graphs. We deduce a characterization of the edge coverings into pairwise non-crossing paths which are stretchable: such a system is stretchable if and only if each subsystem of at least two paths has at least three free vertices (vertices of the outer face of the induced subgraph which are internal to none of the paths of the subsystem). We also deduce that a contact system of pseudo-segments is stretchable if and only if it is extendible.     

A novel approach based on recurrent neural networks applied to nonlinear systems optimization

This paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network.