Dynamic analysis of the double crank mechanism with a 3D translational clearance joint employing a variable stiffness contact force model

Nonlinear Dynamics - Oblique collisions are more likely to happen in the realistic translational joint with clearance, compared to the full front impacts. It can be a quite demanding task to...     

Graphene quantum dots/β-cyclodextrin nanocomposites: A novel electrochemical chiral interface for tryptophan isomer recognition

Graphene quantum dots (GQDs) were prepared by pyrolysis of citric acid, and then incorporated into β-cyclodextrin (β-CD) via H-bonds between the oxygen-containing groups on GQDs and the hydroxyl groups on β-CD. The nanocomposites of GQDs and β-CD (GQDs/β-CD) were negatively charged due to the ionization of carboxyl groups of GQDs, and therefore they could be effectively electrodeposited onto a glassy carbon electrode (GCE). The electrodeposited GQDs/β-CD were optically active due to the introduction of β-CDs with well-defined hydrophobic central cavities, and it was employed as an electrochemical chiral interface for enantiorecognition of tryptophan (Trp) isomers.     

Crystallization of Poly(ethylene adipate) within γ-Phase Poly(vinylidene fluoride) Matrix

The effects of PEA on the γ-phase PVDF crystal structure and the crystallization of PEA within the pre-existing γ-phase PVDF spherulites have been investigated by optical microscopy(OM), infrared spectroscopy(IR) and scanning electron microscopy(SEM). The results demonstrate that the γ-phase PVDF spherulites consist of the lamellae exhibiting a highly curved scroll-like morphology and develop preferentially in PEA-rich blend. With increasing PEA concentration, the scroll diameter increases and the scrolls are better separated from each other. PEA crystallizes first in the interspherulitic region and transcrystalline layer develops. Subsequently, the transcrystalline layer of PEA continues to grow within the γ-phase PVDF spherulites, e.g., in the region between the scrolls, until impinging on other PEA transcrystalline layers or spherulites. The crystallization kinetics results indicate that the growth rate of PEA crystals in the intraspherulitic region of γ-phase PVDF shows a positive correlation with content of PEA, but a negative one with the crystallization temperature of γ-phase PVDF.     

Link overlap,viability, and mutual percolation in multiplex networks

Many real-world complex systems are best modeled by multiplex networks. The multiplexity has proved to have broad impact on the system’s structure and function. Most theoretical studies on multiplex networks to date, however, have largely ignored the effect of the link overlap across layers despite strong empirical evidences for its significance. In this article, we investigate the effect of the link overlap in the viability of multiplex networks, both analytically and numerically. After a short recap of the original multiplex viability study, the distinctive role of overlapping links in viability and mutual connectivity is emphasized and exploited for setting up a proper analytic framework. A rich phase diagram for viability is obtained and greatly diversified patterns of hysteretic behavior in viability are observed in the presence of link overlap. Mutual percolation with link overlap is revisited as a limit of multiplex viability problem, and the controversy between existing results is clarified. The distinctive role of overlapping links is further demonstrated by the different responses of networks under random removals of overlapping and non-overlapping links, respectively, as well as under several link-removal strategies. Our results show that the link overlap facilitates the viability and mutual percolation; at the same time, the presence of link overlap poses a challenge in analytical approaches to the problem.     

Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets

Fault detection of rotating machinery by the complex and non-stationary vibration signals with noise is very difficult, especially at the early stages. Also, many failure mechanisms and various adverse operating conditions in rotating machinery involve significant nonlinear dynamical properties. As a novel method, phase space reconstruction is used to study the effect of faults on the chaotic behavior, for the first time. Strange attractors in reconstructed phase space proof the existence of chaotic behavior. To quantify the chaotic vibration for fault diagnosis, a set of new features are extracted. These features include the largest Lyapunov exponent; approximate entropy and correlation dimension which acquire more fault characteristic information. The variations of these features for different healthy/faulty conditions are very good for fault diagnosis and identification. For the first time, a new chaotic feature space is introduced for fault detection, which is made from chaotic behavior features. In this space, different conditions of rotating machinery are separated very well. To obtain more generalized results, the features are introduced into a neural network to identify different faults in rotating machinery. The effectiveness of the new features based on chaotic vibrations is demonstrated by the experimental data sets. The proposed approach can reliably recognize different fault types and have more accurate results. Also, the performance of the new procedure is robust to the variation of load values and shows good generalization capability for various load values.