Synthesis and thermomechanical characterization of polyurethane elastomers extended with α,ω‐alkane diols

A series of polyurethane (PU) elastomers was prepared by the reaction of poly(?‐caprolactone) and 4,4′‐diphenylmethane diisocyanate, which was extended with a series of chain extenders (CEs) having 2–10 methylene units in their structure. The completion of the reaction was confirmed by Fourier transform infrared spectroscopy. The chemical structures of the synthesized PU samples were characterized with Fourier transform infrared, ¹H‐NMR, and ¹³C‐NMR spectroscopy, and the thermal properties were determined by thermogravimetric analysis, DSC, and dynamic mechanical thermal analysis techniques. The mechanical properties were also studied and are discussed. The thermogravimetric analysis and DSC analysis showed that CE length had a considerable effect on the thermal properties of the prepared samples. The dynamic mechanical thermal analysis and damping peaks were also affected by the number of methylene units in the CE length. The elastomer extended with 1,2‐ethane diol exhibited optimum thermal properties, whereas the elastomer based on 1,10‐decane diol displayed the worst thermal properties. Tensile strength and elongation at break decreased with increasing CE length, whereas hardness showed the opposite trend. The glass‐transition temperature moved toward lower temperatures with increasing CE length. The decrease in the glass‐transition temperature and tensile properties were interpreted in terms of decreasing hard segments and increasing chain flexibility. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Analytical modeling and experimental study of tensile strength of asphalt concrete composite at low temperatures

In this study, analytical modeling of the tensile strength of hot-mix asphalt (HMA) mixtures at low temperatures was developed. To do this, HMA mixtures were treated as a two-phase composite material with aggregates (coarse and fine) dispersed in an asphalt mastic matrix. A two-phase composite model, which was similar to Papanicolaou and Bakos's [J. Reinforced Plast. Compos. 11 (1992) 104] model with a particle embedded in an infinite matrix, was proposed. Unlike Papanicolaou and Bakos's model, an axial stress was introduced to the fiber end to consider the load transferred from the asphalt mastic the aggregate. Efforts were also made to consider the effect of aggregate gradation, asphalt mastic degradation, and interfacial damage between the aggregates and asphalt mastic matrix on the tensile strength of the HMA mixtures. Experimental investigations were conducted to validate the developed theoretical relations. A reasonable agreement was found between the predicted tensile strength and the experimental results at low temperatures. Parameters affecting the tensile strength of asphalt mixtures were discussed based on the calculated results.     

A study of neutron and gamma radiation effects on transmission of various types of glasses, optical coatings, cemented optics and fiber

A study of radiation effects on various types of glasses, dielectric optical coatings, cemented optics and fiber was undertaken with a view to select them for extreme radiation environments. Samples were exposed to different radiation doses in the Pakistan Research Reactor-I (PARR-I) for neutron and Cobalt 60 source for gamma irradiation. Transmissions were measured before and after irradiation. The dielectric coatings were subjected to additional tests (adhesion, abrasion and humidity, etc.) as per MIL-M-13508C and MIL-C-675C. All 15 glasses studied showed varying amounts of transmission loss as expected, with negligible degradation for three types. Recovery of transmissions with time/ageing was also studied, with more or less complete recovery with temperature annealing. A faster bleaching of darkened/brown glasses was achieved by using UV lamps or UV laser. The dielectric coatings (HR, AR) and one of the two commercial optical cements showed excellent resistance to neutrons and gamma radiations, and could be good candidates for the fabrication and utilization of optical components in extreme radiation environments. The data allowed several Chinese glasses to be studied for the first time.     

Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.     

Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach

Water Resources Management - Reference evapotranspiration (ET0) is a crucial element for deriving irrigation scheduling of major crops. Thus, precise projection of ET0 is essential for better...     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

Deep learning controller for nonlinear system based on Lyapunov stability criterion

Neural Computing and Applications - For the current paper, the technique of feed-forward neural network deep learning controller (FFNNDLC) for the nonlinear systems is proposed. The FFNNDLC...     

Direct adaptive fuzzy control with membership function tuning

In this paper, a direct self‐structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

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Superfluid-insulator transition in disordered boson systems

We review the localization problem in two dimensions for interacting bosons in a random potential. This problem is intimately related to the study of ⁴ He adsorbed in porous media, Josephson junction arrays, disordered superconducting films and vortex glasses. Using path integral Monte Carlo techniques, we find a superfluid, a localized or Bose glass insulator with gapless excitations, and (at commensurate densities) a Mott insulator with a finite gap to excitations.