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Background Nanofluids are an innovative attractive challenging field suitable for many heat transfer applications. The helical coil is preferred due to compactness and additional curvature flow disturbance. Method Heat transfer and pressure drop characteristics of Ag, ZnO and TiO2 water based nanofluids through helical coil tubes under isothermal boundary conditions are experimentally investigated with 0.25% nanoparticles volume concentration Ag/water, ZnO/water and TiO2/water nanofluids. Five coils are constructed with different coil pitches of 2, 4, 6, 8 and 10 cm. The flow rate of the nanofluid varies between 1 to 3.5 L/min with inlet temperature from 30 °C to 60 °C. Findings The results showed that increasing the coil pitch reduces Nusselt number and pressure drop. The nanofluids have a positive impact on the enhancement of heat transfer but with a pressure drop penalty. Average Nusselt number enhancement up to 11.8 %, 17.8 % and 28.7 % was achieved at Ag/water, ZnO/water and TiO2/water with average pressure drop increasing up to 25.4%, 23.5% and 28.5% respectively. The results showed that an increase in coil pitch decreases hydrothermal performance index (HTPI) and utilizing nanofluids increases HTPI for all types of nanofluids Ag/water, ZnO/water and TiO2/water. Novel correlations were developed to predict Nusselt number and pressure drop.

Automatic crack detection is needed to reduce cost and to improve quality of surface inspection that is needed for maintenance of infrastructures. In this research, a novel system was developed to detect steel cracks and to estimate their depth from 2D images. The objective is to develop an affordable and user-friendly inspection system in replacement of expensive 3D measurement devices. A learning strategy was adopted and several learning structures were exploited to decide on the suitable structure. The average intensities of 2D steel crack profiles was fed to neural network together with the maximum depth of steel cracks measured by laser microscope to train a learning structure. Feed forward back propagation Neural Network was found to produce an overall average error of 18.81% in testing which is 10% less than the previous error using another learning strategy (updated 3D Make toolbox) for depth recovery. The system performance is comparable to the state of the art and provides an applicable and affordable inspection device. Keywords: Crack detection, Crack depth estimation, Artificial neural networks, Computer vision, Maintenance and safety

The influences of Nano particles incorporation, fiber type and matrix types on the mechanical properties of fiber-reinforced composites have been widely investigated by researchers. However, the effects of targeted fiber arrangements on the micro-hardness properties of bulk polymeric-based composites have not yet been studied. This study aims at assessing the influence of fiber arrangement as well as the incorporation of Nano particles, on Vickers micro-hardness properties of glass fiber-reinforced polymers (GFRP). Five types of samples were prepared: unreinforced (UR) polyester, bulk reinforced arranged (BRA) composite, surface reinforced arranged (SRA) composite, Nano Polyester (NP) and Nano Glass-fiber reinforced composite (NGFRP). Cellulose nanocrystals were uniformly dispersed using an ultrasonic processor. Characterization has been performed using micro-hardness tester. The experimental results demonstrate that a significant increase was observed in micro-hardness values of SRA and BRA specimens subjected to indentation forces with respect to UR samples. The optimum incorporation percentage of cellulose nanocrystals was found to be 4% by weight, which leads to the highest values of microhardness. The fiber arrangement and optimum incorporation percentage of Nano particles have shown promising micro-hardness results, aiming for quality improvement and cost reduction. 2018 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

Crack detection is needed to maintain safety and its automation is a must when human-based inspection cannot be made due to space limitations and/or hazards. In this study, an automatic crack depth measurement method using image processing and laser methods is developed. Measurement of maximum actual depths is done using Keyence (VK-X100) laser microscope. Microscope capture crack image segments using 1/3 in. (8.5 mm) sensor color charge-coupled device (CCD) camera with high resolution and 10x constant magnification. Depths are also calculated using the updated Make3D toolbox. Measured and calculated depths are compared for 11 cracked specimens with 105 segments. The comparison showed that the minimum and overall average error between measured and calculated depths are about 6.13% and 28.22% respectively. Keywords: Steel crack inspection, Image processing, Laser depth measurements, Construction safety

The goal of this chapter is to argue the tribological properties of fiber-reinforced polymer composites. Primarily, diverse types of polymeric resins and fibers are introduced to provide an outline of the basics of polymeric composite materials. Afterward, the effect of the length of the fiber on the tribological behavior of composites is mentioned. Moreover, the impacts of fiber orientation and content are deliberated. The tribological performance of fibrous composites considering different surface treatments of fibers is argued. Finally, the chapter addresses the impact of water and oil lubrication conditions on the tribology of polymer composites.

Automatic crack inspection techniques that limit the necessity of human have the potential to lower the cost and time of the process. In this study, a maximum crack width estimation approach is presented. Seventy nine segments of cracks are used for training the neural networks and twenty six segments are used for examination. The maximum width for each segment is measured using laser scanning microscope and segment image is captured and magnified using the microscope camera in order to obtain the extracted crack profile number of pixels. Feed and cascade forward back propagation artificial neural networks are designed and constructed. The input and output for the networks are the crack width in terms of number of pixels and the maximum estimated crack width respectively. It is shown that, the artificial neural networks technique can effectively be used to estimate the crack width. The feedforward back propagation structure which is designed with two layers and training function TRAINLM gives the best results in examination.

The development of studying nanocomposites has grown up rapidly in the last decade. The objective of the current research is to study the influence of incorporating cellulose nanocrystals on the mechanical properties of polyester resins, as well as to develop continuous filament e-glass fiber-reinforced polyester nanocomposites, which combine traditional composites with the added advantages of nanocomposites. Cellulose nanocrystals were uniformly dispersed into the polyester resin by an ultrasonic processor. The incorporation and dispersion of cellulose nanocrystals were a state-of-the-art method aimed at overcoming poor dispersion problems at low weight fractions of nanoparticles. Three weight percentages of cellulose nanocrystals were prepared, which were 2%, 4% and 6%. Fatigue and tensile specimens were manufactured by resin transfer molding process. Cellulose nanocrystals were fully characterized by using X-ray diffraction, transmission electron microscopy, Fourier-transform infrared spectroscopy and zeta-sizer analysis. The optimum incorporation percentage of cellulose nanocrystals was used to prepare glass fiber-reinforced polyester specimens containing cellulose nanocrystals. Tensile and fatigue behaviors of glass fiber-reinforced polyester composites were evaluated by means of universal testing machine and rotating bending fatigue machine. A series of testing specimens for each property was examined in accordance with the corresponding ASTM and JIS standards. The experimental results showed that the addition of 4% cellulose nanocrystals to polyester matrix lead to the optimum tensile and fatigue properties. Mechanical properties were improved through the enhanced material design and proper selection of compatible nanoparticles, and adding cellulose nanocrystals in a weight fraction that does not affect the mechanical properties of glass fiber-reinforced polyester nanocomposites negatively. The presented design of material and geometry have shown promising results for wide range of applications, particularly in biomedical industry, energy and electronics.

In this paper, heat transfer performance was experimentally tested in a helically coiled pipe using three types of nanofluids, TiO2, ZnO and Ag water-based nanofluids. TiO2, ZnO and Ag nanoparticles were characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscope (TEM). The XRD results showed that the purity of ZnO and TiO2 were obtained. TEM and SEM results showed that the mean particles size of TiO2, ZnO and Ag are 14, 20 and 16 nm respectively. The three types of nanofluids, TiO2/water, ZnO/water and Ag/water were prepared with a volume concentration of 0.25%. To ensure a good mixture and measure particles distribution in each nanofluid, Zetsizer test was performed and showed a good distribution of nanoparticles in all tested nanofluids. The three different nanofluids have been thermally tested in helical coil with a flow rate from 1 to 3.5 L/s with different inlet temperatures in the range of 30 °C and 60 °C. The experimental results showed an enhancement in Nusselt number up to 27.31%, 16.03%, and 10.38% with augmentation in the heat transfer coefficient up to 32%, 21% and 16% utilizing TiO2, ZnO and Ag nanoparticles respectively.

As the study of fatigue failure of composite materials needs a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new technique to predict the fatigue life of Woven Roving Glass Fiber Reinforced Epoxy (GFRE) subjected to combined completely reversed bending moments and internal hydrostatic pressure, with different pressure ratios (Pr) between the applied pressure and burst pressure equal to (0, 0.25, 0.5 and 0.75). Two fiber orientations (θ), [0o,90o]3s and [±45o]3s are considered. Two neural network structures, feed-forward (FFNN) and generalized regression (GRNN), are designed, trained and tested. The groups of data considered are the maximum stress and the Pressure ratio with the different fiber orientation. On the other hand, more accurate prediction method is obtained by using a useful expert system which is designed to aid the designer to decide whether his suggested data for the composite structure is suitable or not. In this expert system a neural network is designed to consider the design data as input and to get yes or no as output. The results show the feed-forward neural network is better results than that given by the generalized regression neural network. The designed expert system helped the designer with reliable conclusions about his decision of the combination of the proposed data.