Effects of bed-load movement on flow resistance over bed forms

The effect of bed-load transport on flow resistance of alluvial channels with undulated bed was experimentally investigated. The experiments were carried out in a tilting flume 250mm wide and 12.5m long with glass-sides of rectangular cross-section and artificial dune shaped floor that was made from Plexi-glass. Steady flow of clear as against sediment-laden water with different flow depths and velocities were studied in the experiments with a fine sand (d ₅₀ = 0.5mm). The results indicate that the transport of fine particles (d ₅₀ = 0.5mm) can decrease the friction factor by 22% and 24% respectively for smooth and rough beds. Increasing the bed-load size (d ₅₀ = 2.84 mm) can decrease the friction factor by 32% and 39% respectively for smooth and rough beds. The decrease in flow resistance is due to filling up of the troughs of dunes. This separation zone is responsible for increasing the flow resistance. On the upstream side of dunes condition is similar to plane bed. Presence of bed-load causes to increase the shear velocity and hence increasing flow resistance. But decreasing in flow resistance is more and it causes to decrease the total flow resistance. Grains saturated the troughs in the bed topography, effectively helping in smoothening of bed irregularities.     

The effects of post annealing on the mechanical properties, microstructure and texture evolutions of pure copper deformed by twist extrusion process

X-ray diffraction peak broadening analysis showed that performing a proper heat treatment between the twist extrusion passes of commercially pure copper decreased the coherent domain size and increased the microstrain. Moreover, SEM micrographs illustrated that annealed material contained new formed grains that could not grow due to lack of sufficient time. Under such circumstances, the ultimate strength was elevated about 45 MPa. The deformed material showed texture of simple shear deformation, changing by applying the post annealing.     

Investigation of cohesive FE modeling to predict crack depth during deep-scratching on optical glasses

Optical glass scratching can induce various types of cracks, among which median cracks are extremely detrimental and penetrate deeply under the surface. Due to deep-scratching process complexity, it is challenging to devise a method to predict median crack depth. Indentation testing has been examined comprehensively in prior research works. It has been found that using the correlation between scratch and indentation testing can simplify predictive method development. In this research, a numerical method based on indentation testing is proposed to determine median crack depth during deep scratching. In the first step, an FE model is configured to simulate the indentation testing process and the Cohesive Zone Method is applied to describe median crack behavior. The cohesive parameters calibrated through experimental indentation testing are implemented in the FE scratch model, and the results are compared with the experimental scratch test results. According to the results, the FE scratch model was enhanced by mode II fracture energy and the modeled friction coefficient. The indentation and scratch experiments were conducted with BK7, F2, Fused silica, K5, Pyrex, Quartz, SF6, and SF19. The experimental results prove that the nonlinearity of the median crack depth curve correlates with K_Ic. A comparison of the experimental and numerical results demonstrates the model is virtually functional for materials with K_Ic below 1000?kPa?m^1/2. Comparisons between the current findings and other studies infer the model and experimental results are accurate and reliable.     

Nanoelectromechanical Chip (NELMEC) Combination of Nanoelectronics and Microfluidics to Diagnose Epithelial and Mesenchymal Circulating Tumor Cells from Leukocytes

An integrated nano‐electromechanical chip (NELMEC) has been developed for the label‐free distinguishing of both epithelial and mesenchymal circulating tumor cells (ECTCs and MCTCs, respectively) from white blood cells (WBCs). This nanoelectronic microfluidic chip fabricated by silicon micromachining can trap large single cells (>12 µm) at the opening of the analysis microchannel arrays. The nature of the captured cells is detected using silicon nanograss (SiNG) electrodes patterned at the entrance of the channels. There is an observable difference between the membrane capacitance of the ECTCs and MCTCs and that of WBCs (measured using SiNG electrodes), which is the key indication for our diagnosis. The NELMEC chip not only solves the problem of the size overlap between CTCs and WBCs but also detects MCTCs without the need for any markers or tagging processes, which has been an important problem in previously reported CTC detection systems. The great conductivity of the gold‐coated SiNG nanocontacts as well as their safe penetration into the membrane of captured cells, facilitate a precise and direct signal extraction to distinguish the type of captured cell. The results achieved from epithelial (MCF‐7) and mesenchymal (MDA‐MB231) breast cancer cells circulated in unprocessed blood suggest the significant applications for these diagnostic abilities of NELMEC.     

A NEW DISTRIBUTED JOB SCHEDULING ALGORITHM FOR GRID SYSTEMS

Job scheduling is one of the key issues in the design of grid environments. The performance of the grid system severely degrades if a method does not exist to efficiently schedule the user jobs. In this article, a fully distributed, learning automata–based job scheduling algorithm is proposed for grid environments. The proposed method is composed of two types of procedures: in the first, a procedure is run at the grid nodes and in the second, the procedure is run at the schedulers. The proposed algorithm synchronizes the performance of the schedulers by the learning automata that select their actions using the pseudo-random number generators with the same seed. In this method, the grid computational capacity that is allocated to each scheduler is proportional to its workload. To show the efficiency of the proposed method, several simulation experiments were conducted under different grid scenarios. The obtained results show that the proposed algorithm outperforms several well-known methods in terms of makespan, flow time, and load balancing.     

Fault detection and diagnosis in synchronous motors using hidden Markov model-based semi-nonparametric approach

Early detection and diagnosis of faults in industrial machines would reduce the maintenance cost and also increase the overall equipment effectiveness by increasing the availability of the machinery systems. In this paper, a semi-nonparametric approach based on hidden Markov model is introduced for fault detection and diagnosis in synchronous motors. In this approach, after training the hidden Markov model classifiers (parametric stage), two matrices named probabilistic transition frequency profile and average probabilistic emission are computed based on the hidden Markov models for each signature (nonparametric stage) using probabilistic inference. These matrices are later used in forming a similarity scoring function, which is the basis of the classification in this approach. Moreover, a preprocessing method, named squeezing and stretching is proposed which rectifies the difficulty of dealing with various operating speeds in the classification process. Finally, the experimental results are provided and compared. Further investigations are carried out, providing sensitivity analysis on the length of signatures, the number of hidden state values, as well as statistical performance evaluation and comparison with conventional hidden Markov model-based fault diagnosis approach. Results indicate that implementation of the proposed preprocessing, which unifies the signatures from various operating speeds, increases the classification accuracy by nearly 21% and moreover utilization of the proposed semi-nonparametric approach improves the accuracy further by nearly 6%.     

Color image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditions

This study was undertaken to develop machine vision-based raisin detection technology for various lighting conditions. Supervised color image segmentation using a permutation-coded genetic algorithm (GA) identifying regions in hue–saturation–intensity (HSI) color space (GAHSI) for desired and undesired raisin detection in various conditions was successfully implemented. Images from two extreme intensity lighting and dense conditions: under weak lighting and high-density product and under suitable lighting and low-density product, were mosaicked to explore the possibility of using GAHSI to locate desired raisin and undesired raisin regions in color space when these two extremes were presented simultaneously. The GAHSI results provided evidence for the existence and separability of such regions. In the experiment, GAHSI performance was measured by comparing the GAHSI-segmented image with a corresponding hand-segmented reference image. When compared with cluster analysis-based segmentation results, the GAHSI method showed no significant difference.     

Two-dimensional modeling of selective CO oxidation in a hydrogen-rich stream

In this study, carbon monoxide removal by preferential oxidation in a hydrogen-rich stream is simulated between two parallel infinite plates of 150 μm distance. A three-step kinetic is considered that includes carbon monoxide oxidation, hydrogen oxidation and water–gas shift reaction. The walls temperature is in the range of 80–120 °C. The function of this microreactor is to reduce carbon monoxide content from about 2% to below 10 ppm, suitable for use in a PEM fuel cell. Based on the problem conditions, the flow is in the continuum regime and application of the Navier–Stokes equations is admissible. In order to simulate the reacting flow, continuity, conservations of x- & y-momentum, conservation of energy, conservation of species, state equation and reaction rates are simultaneously solved through SIMPLE algorithm by utilizing power-law scheme. Effects of important parameters including walls temperature, steam content, CO content and O₂/CO are assessed. It is observed that increasing walls temperature or oxygen content will increase both CO selectivity and conversion. It is also found that by steam addition, CO conversion is improved without significant change of CO selectivity. These results are in good agreement with previous published data.