High-order compact boundary value method for the solution of unsteady convection–diffusion problems

In this paper, we propose a new class of high-order accurate methods for solving the two-dimensional unsteady convection–diffusion equation. These techniques are based on the method of lines approach. We apply a compact finite difference approximation of fourth order for discretizing spatial derivatives and a boundary value method of fourth order for the time integration of the resulted linear system of ordinary differential equations. The proposed method has fourth-order accuracy in both space and time variables. Also this method is unconditionally stable due to the favorable stability property of boundary value methods. Numerical results obtained from solving several problems include problems encounter in many transport phenomena, problems with Gaussian pulse initial condition and problems with sharp discontinuity near the boundary, show that the compact finite difference approximation of fourth order and a boundary value method of fourth order give an efficient algorithm for solving such problems.     

A novel spin-bonding process for manufacturing multilayered clad tubes

Utilizing the so-called tube spinning, a cold-bonding process entitled as “spin-bonding” is developed to produce seamless thin-walled clad tubes and cylinders. By this method, two layers of AA 1050 tubes were successfully bonded together to form a clad tube at room temperature. Based on mechanical aspects of the tube spinning process, the mechanism of spin-bonding is explained in two stages: surface preparation before occurrence of a stable bond and bond strengthening thereafter. The effects of process temperature, thickness reduction, feed rate and roller attack angle as the parameters of tube spinning on the bond strength are studied. It is shown that the bond strength increases by increasing the thickness reduction and process temperature. It is also observed that the bond strength is not considerably affected by the feed rate and roller attack angle, especially at high thickness reductions. The SEM micrographs of the peel surfaces illustrate that at high temperature, both of area fraction and number of bond spots increase and their inter-distances decrease.     

Energy absorption capacity of ferroelectrets based on porous polypropylene

Ferroelectret materials are special group of piezoelectric materials obtained from the cellular structure of nonpolar polymers. They are under investigation for a variety of applications such as actuators, vibration control, speakers, microphones, sensors, as well as healthcare monitoring systems. In this work, the effect of morphological and mechanical properties of ferroelectret films on their capacitance and stored energy capacity was studied. Different eye‐like cellular structure of porous polypropylene films with different cell aspect ratio (AR) and mechanical properties were used. The optimized sample was obtained with AR = 6.6 using nitrogen (N₂) as the ionizing gas to give a quasi‐static piezoelectric coefficient (d₃₃) of about 800 pC/N, an electrode charge density of about 2.10 mC/m², a maximum capacitance of about 465 pF, as well as a maximum stored energy capacity of about 1,824 pJ. It was observed that replacing air with N₂ as the ionizing gas led to an increase in charge/voltage output. Moreover, by replacing air with N₂ as the ionizing gas, the capacitance and stored energy of the samples were improved by about 20% and 80%, respectively. Hence, it can be concluded that the capacitance was mostly related to the cellular morphology of the samples while the stored energy was also function of the ionizing gas used. POLYM. ENG. SCI., 58:300–309, 2018. © 2017 Society of Plastics Engineers     

Predicting pressure drop in venturi scrubbers with artificial neural networks

In this study a new approach based on artificial neural networks (ANNs) has been used to predict pressure drop in venturi scrubbers. The main parameters affecting the pressure drop are mainly the gas velocity in the throat of venturi scrubber (V(g)(th)), liquid to gas flow rate ratio (L/G), and axial distance of the venturi scrubber (z). Three sets of experimental data from five different venturi scrubbers have been applied to design three independent ANNs. Comparing the results of these ANNs and the calculated results from available models shows that the results of ANNs have a better agreement with experimental data.     

Axial compressive behavior of concrete actively confined by metal strips; part A: experimental study

This paper presents the results of an experimental study on the application of strapping technique for retrofit of concrete compressive specimens. In this technique, standard strapping devices, which are used in the packaging industry, are applied to post-tension high strength metal strips around the concrete columns. Experimental program included axial compressive tests on 72 cylindrical and prismatic compressive specimens, which were actively confined by pre-stressed metal strips. The effects of various parameters on strength and ductility of confined concrete were studied including compressive strength of concrete, mechanical volumetric ratio of confining strips, post-tensioning force in the strip, number of strip layers wrapped around the specimens and details of strip joint. The effects of strength and ductility of confining strips on the behavior of confined specimens were also studied. Longitudinal and lateral strains of concrete and strain of the strips were monitored. Test results showed significant increase in the strength and ductility of specimens due to active confinement by metal strips. It was observed that ductility of confining material plays the most important role in enhancement of concrete ductility. The gain in strength is strongly dependent to the effective mechanical volumetric ratio of confining strips. It was also observed that the active confinement of concrete by post-tensioning the confining element results in stiffer pre-peak response of concrete specimens than the usual passive confinement.     

The Effect of Methylcellulose,Temperature, and Microwave Pretreatment on Kinetic of Mass Transfer During Deep Fat Frying of Chicken Nuggets

The aim of this study was to determine the effect of microwave pretreatment, usage of methylcellulose, oil temperature, and frying time on mass transfer during deep fat frying of chicken nuggets. Methylcellulose was used in batter and as a coating on product. Microwave with two power densities namely 3.7 and 7.4 W/g was used for reduction of initial moisture content of samples before frying. Frying was performed at three temperatures (150 °C, 170 °C, and 190 °C) and five intervals (0.5, 1, 2, 3, and 4 min) in the sunflower oil. The least oil content was observed when MC was used as a coating layer on non-precooked samples fried at 190 °C. Oil absorption of samples with MC in batter was partially higher compared to control samples. This could be attributed to the rheology of batters. The first-order kinetic model was fitted to moisture and oil content. For determining the correlation between temperature and moisture diffusivity, Arrhenius equation was used. The constant rate for moisture and oil transfer was in the range of 2.2–5 and 0.023–2.67 s^?1, respectively. Effective moisture diffusivity values were between 1.43?×?10^?8 and 3.24?×?10^?8 m²/s. Activation energy ranged between 0.71 and 1.71 kJ/mol.     

Application of Fractal Theory for Prediction of Shrinkage of Dried Kiwifruit Using Artificial Neural Network and Genetic Algorithm

In current research, fractal theory has been applied for estimation of shrinkage of osmotically dehydrated and air-dried kiwifruit using a combination of neural network and genetic algorithm. Kiwifruits were dehydrated at different conditions and digital images of final dried products were taken. Kiwifruit-background interface lines were detected using a threshold combined with an edge detection approach and their corresponding fractal dimensions were calculated based on a box counting method. A neural network was constructed using fractal dimension and moisture content as inputs to predict shrinkage of dried kiwifruit and a genetic algorithm was applied for optimization of the neural network's parameters. The results indicated good accuracy of optimal model (correlation coefficient of 0.95) and high potential application of fractal theory and described intelligent model for shrinkage estimation of dried kiwifruit.