Analytical layerwise solution of nonlinear thermal instability of SMA hybrid composite beam under nonuniform temperature condition

Abstract

Thermal buckling and post-buckling behavior of composite beam reinforced with shape memory alloy (SMA) wires under nonuniform temperature distribution is explored. Thermo-mechanical behavior of SMA wires is formulated by using the one-dimensional Brinson SMA model. Considering von Karman strain–displacement relation, corresponding nonlinear governing equations are obtained and solved analytically. Heat conduction equation is employed and through-the-thickness temperature distribution is obtained by discretization scheme of layerwise method. Influence of SMA-wire positioning across the thickness, temperature distribution, SMA wire pre-straining level and volume fraction of SMAs upon the thermal buckling and post buckling of reinforced beam are examined and discussed in detail.     

Co<Subscript>2</Subscript>SiO<Subscript>4</Subscript> nanostructures/nanocomposites: synthesis and investigations of optical,magnetic, photocatalytic,thermal stability and flame retardant properties

Cobalt orthosilicate (Co₂SiO₄) nanostructures and nanocomposites were successfully synthesized via a sol–gel method, by controlling different conditions. The gels were prepared starting from cobalt (II) acetatete tetrahydrate (Co(CH₃COO)₂·4H₂O), tetraethyl orthosilicate, NH₃ and carbohydrate at calcination temperature 500–700 °C for 5 h. We choose 700 °C as optimum calcination temperature base on XRD results. SEM images showed that NH₃ and glucose are optimum catalysis and capping agent, respectively, in our experimental conditions. For the first time, glucose, fructose, sucrose, maltose and lactose were applied as capping agents to green synthesis of cobalt orthosilicates. The optical and magnetic properties of Co₂SiO₄ nanostructures were investigated by UV–Vis and VSM, respectively. Also, for the first time photocatalytic behavior of these nanostructures was evaluated using UV–Vis and degradation of methyl orange, methylene blue, erythrosine and eosine. DSC and TG curves of the nanocomposites showed both thermal stability and flame retardant property for Co₂SiO₄ nanocomposites prepared in the presence of the PS and PSU.     

Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method

Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R~2) of lower than 0.2%, 1.05 × 10~(-7) and 0.9994, respectively.     

Comparison and validation of artificial intelligent techniques to estimate intestinal broiler microflora

This study was carried out to investigate the ability of major mathematical methods to estimate intestinal broiler microflora population. Artificial neural network (ANN), coactive neuro-fuzzy inference system (CANFIC), and artificial neural network genetic algorithm (ANNGA) were used in this respect. The lactic acid bacteria and Enterobacteriaceae were applied as models of microflora. Input and output variables were considered as time and microflora population, respectively. The best model of ANN, CANFIC, and ANNGA was determined based on the coefficient of determination and root mean square error criteria. The results of the current study have shown that ANN, ANNGA, and CANFIS are accurate methods to estimate lactic acid bacteria and Enterobacteriaceae. The highest accuracy of microflora estimation was related to 7 days of age. The efficiency of intelligent models to lactic acid bacteria and Enterobacteriaceae has shown that ANNGA had better prediction between mentioned models. The models estimated Enterobacteriaceae population better than that for lactic acid bacteria.     

Miniaturized low‐profile antenna based on uniplanar quasi‐composite right/left‐handed metamaterial

In this article, a metamaterial‐based broadband low‐profile antenna is presented. The proposed antenna employed an array of uniplanar quasi‐composite right/left‐hand (CRLH) metamaterial cells. This structure contributes to exciting the operating modes in lower frequencies. The antenna has an overall electrical size of 0.75 × 0.60 × 0.07 λ₀³ (λ₀ is the center operating wavelength in free space) and provides a 25% measured bandwidth with the center frequency of 5.1 GHz and maximum gain of 6.6 dB. The proposed antenna is an appropriate candidate for WLAN, WiMAX, and other wireless communication applications.     

NERA: A new and efficient RSU based authentication scheme for VANETs

Wireless Networks - In vehicular ad-hoc networks (VANETs), the correctness of a message requires authentication of the origin vehicle. In this paper, we introduce a novel authentication scheme for...     

Dynamic mathematical modeling of a novel dual-type industrial ethylene oxide (EO) reactor

In this paper, the dynamic behavior of a novel dual-type industrial ethylene oxide reactor has been proposed with taking catalyst deactivation into account. The configuration of two catalyst beds instead of one single catalyst bed is developed for conversion of ethylene to ethylene oxide. In the first reactor which is an industrial fixed-bed water-cooled reactor, the feed gas is partly converted to ethylene oxide. This reactor functions at very high yield and at a higher than normal operating temperature. In the second converter, the reaction heat is used to preheat the feed gas to the first reactor and a milder temperature profile is observed. The potential possibilities of a two-stage catalyst bed system are analyzed using a 1D heterogeneous dynamic model to obtain necessary comparative estimates. A differential evolution (DE) algorithm is applied as an effective and robust method to optimize the reactors length ratio. The results obtained from the simulation demonstrate that there is a desirable catalyst temperature profile along the dual-type reactor (DR) compared with the conventional single-type reactor (SR). In this way, the catalysts are exposed to less extreme temperatures and thus, diminishing the catalyst deactivation via sintering. Results from this study provided beneficial information about the effects of reactors configuration on catalyst lifetime and ethylene oxide production rate simultaneously.     

Electromagnetic interference shielding effectiveness of hybrid multifunctional Fe3O4/carbon nanofiber composite

Electromagnetic interference shielding effectiveness (EMI SE) of multifunctional Fe₃O₄/carbon nanofiber composites in the X-band region (8.2–12.4 GHz) is studied. Here, we examine the contributing effects of various parameters such as Fe₃O₄ content, carbonization temperature and thickness on total shielding efficiency (SE_total) of different samples. The maximum EMI SE of 67.9 dB is obtained for composite of 5 wt.% Fe₃O₄ (0.7 mm thick) with the dominant shielding by absorption (SE_A) of electromagnetic radiation. The enhanced electromagnetic shielding performance of Fe₃O₄/carbon nanofiber composites is attributed to the increment of both magnetic and dielectric losses due to the incorporation of magnetite nanofiller (Fe₃O₄) in electrically conducting carbon nanofiber matrix as well as the specific nanofibrous structure of carbon nanofiber mats, which forms a higher aspect ratio structure with randomly aligned nanofibers. Furthermore, we prove that the addition of elastomeric polydimethylsiloxane (PDMS) as a coating for carbon nanofiber composite strengthens the composite structure without interfering with its electromagnetic shielding efficiency.     

A Naïve Bayes prediction model on location-based recommendation by integrating multi-dimensional contextual information

Multimedia Tools and Applications - In recent years, researchers have been trying to create recommender systems. There are many different recommender systems. Point of Interest (POI) is a new type...