A review of nanofluid stability properties and characterization in stationary conditions

A new engineering medium, called nanofluid attracted a wide range of researches on many cooling processes in engineering applications, which are prepared by dispersing nanoparticles or nanotubes in a host fluid. In this paper, the stability of nanofluids is discussed as it has a major role in heat transfer enhancement for further possible applications. It also represents general stabilization methods as well as various types of instruments for stability inspection. Characterization, analytical models and measurement techniques of nanofluids after preparation by a single step or two-step method are studied.     

Vibration analysis and identification of breathing cracks in beams subjected to single or multiple moving mass using online sequential extreme learning machine

In this paper, a novel approach was presented to vibration analysis and identification of breathing cracks in Timoshenko beam under single or multiple moving mass. Dynamic strain energies (DSEs) and translational accelerations in beam structures under moving mass were used as forward problem and application of an emergent learning algorithm called the online sequential extreme learning machine algorithm as inverse problem to predict crack depths and locations. To demonstrate the potential of the proposed vibration analysis over existing ones, two validation studies have been done. To evaluate the proposed method to identify breathing cracks, two examples, namely, clamped–clamped beam and two span continuous beams have been studied. Also, the effect of the discrepancy in stiffness between the finite-element model and the actual tested dynamic system has been investigated. Another examination has been performed in which moving mass with different speeds are utilized. Also, the effect of multi mass passing through the beam has been studied. The obtained results indicated that the proposed method could identify the breathing cracks existence and severity in the beam under moving mass using DSE and accelerations, which may be noisy or noise free.     

Optimal design of a modular axial-flux permanent-magnet synchronous generator for gearless wind turbine applications

Air-cored axial-flux permanent-magnet synchronous generators (AFPMSGs) are potential candidates for gearless direct-coupled wind turbines (DCWTs) owing to providing high efficiency and power density. The design of a DCWT generator is so complicated since the generator cost, dimension, and weight affected by gear elimination. Therefore, it is essential to find an optimal AFPMSG design at rated conditions. In this paper, an accurate procedure for the optimal design of an air-cored AFPMSG applicable for DCWTs is proposed. The genetic algorithm (GA) is used for multi-objective design optimization to reach the optimal configuration as well as system dimension in order to decrease the weight, increase the power density and enhance the effectiveness of the generator. To validate the efficiency of the suggested optimization proceducer, a 30 kW AFPMSG has been considered as a case study. The results of optimization have been investigated by finite element analysis (FEA). A prototype generator is also fabricated, and the test results are offered and compared with the numerical study. The outcomes show that there exists an acceptable agreement between FEA and experimental outcomes with the error percentage about of 1.35%.     

Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG

In this paper, a passive neuro-wavelet based islanding detection technique for grid-connected inverter-based distributed generation was developed. The weight parameters of the neural network were optimized by intelligent water drop (IWD) to improve the capability of the proposed technique in the proposed problem. The proposed method utilizes and combines wavelet analysis and artificial neural network (ANN) to detect islanding. Connecting distributed generator to the distribution network has many benefits such as increasing the capacity of the grid and enhancing the power quality. However, it gives rise to many problems. This is mainly due to the fact that distribution networks are designed without any generation units at that level. Hence, integrating distributed generators into the existing distribution network is not problem-free. Unintentional islanding is one of the encountered problems. Discrete wavelet transform (DWT) is capable of decomposing the signals into different frequency bands. It can be utilized in extracting discriminative features from the acquired voltage signals. In passive schemes with a large non-detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. The simulation results from Matlab/Simulink shows that the proposed method has a small non-detection zone, and is capable of detecting islanding accurately within the minimum standard time.     

Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization

This paper presents a novel modified interactive honey bee mating optimization (IHBMO) base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.     

Recovery of alcohols from water using polydimethylsiloxane–silica nanocomposite membranes: Characterization and pervaporation performance

Different polydimethylsiloxane (PDMS) nanocomposite membranes were synthesized by incorporating various contents of nanosized silica particles to improve the PDMS pervaporation (PV) performance. A uniform dispersion of silica nanoparticles in the PDMS membranes was obtained. The nanocomposite membranes were characterized morphologically by scanning electron microscopy (SEM) and atomic force microscopy (AFM). The results showed that surface roughness increases by incorporating silica, and this decreases absorption of penetrants on the membrane. Swelling studies showed that the presence of silica nanoparticles into the PDMS membranes decreases degree of swelling, which can be attributed to rigidification of the PDMS matrix. Additionally, the results revealed that helium permeability decreases through the nanocomposite membranes, due to the more polymer chains packing. Effects of silica on recovery of isopropanol (IPA) from water mixtures were also investigated. Based on the results, incorporating silica nanoparticles promotes significantly the PDMS membrane selectivity because the polymer chains are rigidified and also the polymer free volume decreases. However, permeation flux decreases as diffusion of the penetrants reduces in the presence of silica nanoparticles within the PDMS membranes. As PV performance depends on operating conditions, effects of feed composition, and temperature were also studied. Moreover, recoveries of IPA, ethanol, and methanol from water mixtures were compared using the PDMS‐silica nanocomposite membranes. The results demonstrated that polarity and solubility of alcohols affect permeation flux and selectivity resulting in the higher permeation flux and selectivity for IPA. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Crack detection of structures using modified extreme learning machine (MELM)

In this paper, crack detection and estimation method is presented in structures using modified extreme learning machine. For this purpose, extreme learning machine was modified using modified weights and biases. By using the first three frequencies and mode shapes as input, crack was detected as output. Performance of the proposed method was evaluated by using some numerical examples consisting of a simply supported beam, cantilever beam and fixed-simply supported beam. In addition, noise effect (3% noise level) on the measured frequencies and mode shapes have been investigated. In another work, a portal frame has been studied. The results indicated that the proposed method is effective and fast in crack detection and estimation of structures.     

A review on the buyer–supplier dyad relationships in sustainable procurement context: past,present and future

Sustainable development is currently being applied in most fields of research. Procurement, focused on the buyer–supplier dyad, is one such discipline where sustainability is being widely applied. This paper provides a review of these research studies, conducting a systematic content analysis in order to present the state of the art in this domain. The paper carries out a detailed review of articles in international scientific journals and well-known international conferences related to green and sustainable supplier selection published between 2008 and 2014 inclusive. Seven designed research questions are proposed and answered based on this bibliography. Interesting results are reported in each section and gaps in the current body of literature are identified. The purpose of this review is to provide important future directions and limitations in this research topic.     

Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks

Since the fiber diameter determines the mechanical, electrical, and optical properties of electrospun nanofiber mats, the effect of material and process parameters on electrospun polymethyl methacrylate (PMMA) fiber diameter were studied. Accordingly, the prediction and optimization of input factors were performed using the response surface methodology (RSM) with the design of experiments technique and artificial neural networks (ANNs). A central composite design of RSM was employed to develop a mathematical model as well as to define the optimum condition. A three-layered feed-forward ANN model was designed and used for the prediction of the response factor, namely the PMMA fiber diameter (in nm). The parameters studied were polymer concentration (13–28 wt%), feed rate (1–5 mL/h), and tip-to-collector distance (10–23 cm). From the analysis of variance, the most significant factor that caused a remarkable impact on the experimental design response was identified. The predicted responses using the RSM and ANNs were compared in figures and tables. In general, the ANNs outperformed the RSM in terms of accuracy and prediction of obtained results.     

The improtance of endogenous amino acid losses in diarrhea in infants

Protein depletion is introduced as a unitary concept in the progression of acute infantile diarrheas into a protracted or intractable stage. It has been shown that in acute infantile diarrhea significant amounts of endogenous nitrogen are lost through the stool. Accumulated loss beyond a week or so may interfere with regeneration of intestinal mucosa and the synthesis of digestive enzymes. The success of total parenteral alimentation in the management of intractable diarrhea supports this theory. As a relatively simple preventive measure, the administration of amino acid solution by peripheral vein, concomitantly with glucose, is suggested in the management of the early stages of infantile diarrhea.