Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling

In the present study, the Group method of data handling (GMDH) network was utilized to predict the scour depth below pipelines. GMDH network was developed using back propagation. Input parameters that were considered as effective parameters on the scour depth included those of sediment size, geometry of pipeline, and approaching flow characteristics. Training and testing performances of the GMDH networks have been carried out using nondimensional data sets that were collected from the literature. These data sets are related to the two main situations of pipelines scour experiments namely clear-water and live-bed conditions. The testing results of performances were compared with the support vector machines (SVM) and existing empirical equations. The GMDH network indicated that using of back propagation produced lower error of scour depth prediction than those obtained using the SVM and empirical equations. Also, the effects of many input parameters on the scour depth have been investigated.     

Optimisation and modelling green bean's ultrasound blanching

Using of ultrasound in food processing is a novel and interesting technique, which is often complementary to classical methods. This study reports on the ultrasound blanching (USB) of green bean. Response surface methodology (RSM) was used to study the effect of process variables on the USB. Three independent variables including temperature (50–90 °C), time (45–225 s) and duty cycle (0.2–0.8 s) were examined. The optimal USB conditions were obtained with a temperature of 90 °C, USB time of 58.27 s and duty cycle of 0.79 s. At these conditions, the residual peroxidase activity (RPA) determined as 9.64% and the vitamin C loss as 8.92%. The experimental values under optimal condition were in good consistent with the predicted values. According to the results, the USB process is more efficient process and as well as less damage to the product compared to the conventional blanching method.     

Pipe Break Rate Assessment While Considering Physical and Operational Factors: A Methodology based on Global Positioning System and Data-Driven Techniques

An accurate prediction of pipes failure rate plays a substantial role in the management of Water Distribution Networks (WDNs). In this study, a field study was conducted to register pipes break and relevant causes in the WDN of Yazd City, Iran. In this way, 851 water pipes were incepted and localized by the Global Positioning System (GPS) apparatus. Then, 1033 failure cases were reported in the eight zones of understudy WDN during March-December 2014. Pipes break rate (BR_P) was calculated using the depth of pipe installation (h_P), number of failures (N_P), the pressure of water pipes in operation (P), and age of pipe (A_P). After completing a pipe break database, robust Artificial Intelligence models, namely Multivariate Adaptive Regression Spline (MARS), Gene-Expression Programming (GEP), and M5 Model Tree were employed to extract precise formulation for the pipes break rate estimation. Results of the proposed relationships demonstrated that the MARS model with Coefficient of Correlation (R) of 0.981 and Root Mean Square Error (RMSE) of 0.544 provided more satisfying efficiency than the M5 model (R?=?0.888 and RMSE?=?1.096). Furthermore, statistical results indicated that MARS and GEP models had comparatively at the same accuracy level. Explicit equations by Artificial Intelligence (AI) models were satisfactorily comparable with those obtained by literature review in terms of various conditions: physical, operational, and environmental factors and complexity of AI models. Through a probabilistic framework for the pipes break rate, the results of first-order reliability analysis indicated that the MARS technique had a highly satisfying performance when MARS-extracted-equation was assigned as a limit state function.

     

Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions

Water Resources Management - Longitudinal dispersion coefficient (LDC) is known as the most remarkable environmental variables which plays a key role in evaluation of pollution profiles in water...     

Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques

There is growing demand on datacenters to serve more clients with reasonable response times, demanding more hardware resources, and higher energy consumption. Energy-aware datacenters have thus been amongst the forerunners to deploy virtualization technology to multiplex their physical machines (PMs) to as many virtual machines (VMs) as possible in order to utilize their hardware resources more effectively and save power. The achievement of this objective strongly depends on how smart VMs are consolidated. In this paper, we show that blind consolidation of VMs not only does not reduce the power consumption of datacenters but it can lead to energy wastage. We present four models, namely the target system model, the application model, the energy model, and the migration model, to identify the performance interferences between processor and disk utilizations and the costs of migrating VMs. We also present a consolidation fitness metric to evaluate the merit of consolidating a number of known VMs on a PM based on the processing and storage workloads of VMs. We then propose an energy-aware scheduling algorithm using a set of objective functions in terms of this consolidation fitness metric and presented power and migration models. The proposed scheduling algorithm assigns a set of VMs to a set of PMs in a way to minimize the total power consumption of PMs in the whole datacenter. Empirical results show nearly 24.9% power savings and nearly 1.2% performance degradation when the proposed scheduling algorithm is used compared to when other scheduling algorithms are used.     

Healing of Double-Oxide Film Defects in Commercial Purity Aluminum Melt

The possibility of the formation of bonding between the two layers of a double-oxide film defect when held in a commercial purity liquid Al alloy was investigated. The defect was modeled experimentally by maintaining two aluminum oxide layers in contact with one another in a commercial purity Al melt at 1023 K (750 °C) for times ranging from 7 minutes to 48 hours. Any changes in the composition and morphology of these layers were studied by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). The results showed that the oxide layers started to bond to one another after approximately 5 hours, and the extent of the bonding increased gradually by the holding time. The bonding is suggested to form because of the transformation of γ- to α-Al₂O₃. A complete bonding formed between the layers only when the oxygen and nitrogen trapped between the two layers were consumed, after approximately 13 hours. The results also confirmed that the nitrogen within the atmosphere of an oxide film defect reacts with the surrounding Al melt to form AlN at the interface of the defect and the melt.     

The behaviour of double oxide film defects in Al–4.5 wt% Mg melt

The possibility of bonding of the two layers of a double oxide film defect when held in a liquid Al–4.5 wt% Mg alloy was investigated. The defect was modelled experimentally by maintaining two aluminium oxide layers in contact with each other in an Al–4.5 wt% Mg liquid alloy at 750 °C from 2 min to 16 h. Any changes in the composition and morphology of these layers were studied by SEM, EDX and XRD. The results showed that in contrast to previous studies reported in the literature on Al–0.3 wt% Mg in which the two layers bonded to each other after a holding time of 5 h, no bonding took place between the two oxide layers even after a holding time of 16 h. Based on the comparison between the two studies, it was concluded that a transformation involving rearrangement of atoms at the interface between the two oxide layers is essential for the bonding to take place between the two oxide layers. This criterion could be used to predict the bonding behaviour of oxide film defects when held in different liquid aluminium alloys, or when subjected to a HIPping process.     

Group method of data handling to predict scour depth around bridge piers

In this study, group method of data handling network with quadratic polynomial was used to predict scour depth around bridge piers. Effective parameters on scour phenomena include sediment size, geometry of bridge pier, and upstream flow conditions. Different shapes of piers have been utilized to develop the GMDH network. Back propagation algorithm was performed to train the GHMD network which updated weighting coefficients of quadratic polynomial in each iteration of the training stage. The GMDH performed with the lowest errors of training and testing stages for cylindrical pier. Also, Richardson and Davis, Johnson’s equations produced relatively good performances for different types of piers. Finally, the results indicated that GMDH could be provided more accurate prediction than those obtained using traditional equations.     

Reduce energy consumption in sensors using a smartphone,smartwatch, and the use of SFLA algorithms (REC-SSS)

The Journal of Supercomputing - Wireless body area networks are a technology for remote medical care. Because of the limited energy of the sensors, one of the problems of long-distance medical care...     

Experimental and theoretical analysis of air-inflated circular woven fabric deformation

In this study, a bulge tester is designed and constructed in order to simulate air-inflated fabric deformations. The shadow moiré method is used to analyze and reconstruct the 3-D profile of the deformed fabric. To verify the results of the shadow moiré method, front view photography is performed. Theoretical modeling of fabric deformation is carried out through uniform pressure and energy methods. The experimental results showed that the fabric deformation decreases as the angular distance from warp and weft directions increases. The fabric deformation increases nonlinearly with the air pressure in which the fabric with a higher density exhibits a slightly lower deformation. Theoretical results showed good agreement with the experimental results.