Relating Sediment Yield Estimations to the Wet Front Term Using Rainfall Simulator Field Experiments

Water Resources Management - Depth to wet front is generally considered as the amount of water that penetrates into soil and wets the internal soil layer. This is an important variable especially...     

Optimization of the Inspection of Large Composite Materials Using Robotized Line Scan Thermography

The emergence of composite materials has started a revolution in the aerospace industry. When using composite materials, it is possible to design larger and lighter components. However, due to their anisotropy, composite materials are usually difficult to inspect and detecting internal defects is a challenge. Line scan thermography (LST) is a dynamic thermography technique, which is used to inspect large components of metallic surfaces and composites, commonly used in the aerospace industry. In this paper, the robotized LST technique has been investigated on a large composite component which contains different types of internal defects located at a variety of depths. For theoretical analysis, the LST inspection was simulated using a mathematical formulation based on the 3D heat conduction equation in the transient regime in order to determine the optimum parameters. The solution of the model was performed using the finite element method. The LST parameters were adjusted to detect the deepest defects in the specimen. In order to validate the numerical results with experimental data, a robotized system in which the infrared camera and the heating source move in tandem, has been employed. From the experimental tests, it was noted that there are three sources of noise (non-uniform heating, unsynchronized frame rate with scanning speed and robot arm vibration) which affect the performance of the test. In this work, image processing techniques that were initially developed to be applied on pulse thermography have been successfully implemented. Finally, the performance of each technique was evaluated using the probability of detection approach.     

Thermodynamic Equilibrium Analysis of Propane Dehydrogenation with Carbon Dioxide and Side Reactions

A thermodynamic analysis of propane dehydrogenation with carbon dioxide was performed using constrained Gibbs free energy minimization method. Different reaction networks corresponding to different catalytic systems, including non-redox and redox oxide catalysts, were simulated. The influences of CO₂/C₃H₈ molar ratio (1–10), temperature (700–1000 K), and pressure (0.5–5 bar) on equilibrium conversion and product composition were studied. In the presence of CO₂ with a molar ratio of CO₂/C₃H₈ = 1, the temperature of dehydrogenation can be 30 K lower than that of dehydrogenation in the presence of steam (H₂O/C₃H₈ = 1) and about 50 K lower than that of simple dehydrogenation without dilution to achieve 60% propane conversion. It was found that the occurrence of dry reforming of propane and coke-forming side reactions could strongly impact the equilibrium product composition of the multireaction system and, therefore, these reactions should be kinetically controlled. Comparison of the simulated reactant conversions with those reported in the literatures revealed that the experimental conversion levels of propane are far below the corresponding equilibrium values due to rapid catalyst deactivation by coke, implying that research efforts should be directed toward formulation of more active and selective catalysts.     

Parameter Optimization of Robotize Line Scan Thermography for CFRP Composite Inspection

Demands for Composite materials is increasing more and more because of their specific mechanical properties, especially in aerospace industry. Due to the porous structure of composite materials, there is the negligible probability of breaking up and defects in the internal structure. Detection of deep defects is a challenging subject in the field of Non-destructive testing. Due to the large size of composite components in the aerospace industry, line scanning thermography (LST) coupled with a robot arm is used to inspect large composite materials. In this paper, an innovative optimization procedure has been employed using analytical model, 3-D simulation using COMSOL Multiphysics, experimental setup and signal processing algorithms. The goal is to maximize the detection depth and signal to noise value as the criteria to evaluate the inspection quality and performance. the proposed algorithm starts searching to find the optimization variables of robotized LST such as scanning speed, source power and distance considering all technical and mechanical constraints. The optimal values are dependent on the material structure, thermal specifications of the composite, defect shape and infrared camera resolution. Using the proposed optimization algorithm, the detection depth was increased to 3.5 mm in the carbon fiber reinforced polymer and the signal to noise ratio was enhanced to 95%.     

Antibacterial activities of novel nanocomposite biofilms of chitosan/phosphoramide/Ag NPs

Novel nanocomposite films of chitosan/phosphoramide/Ag NPs were prepared containing 1–5% of silver nanoparticles. The Ag NPs were synthesized according to the citrate reduction method. The XRD and SEM analysis of Ag NPs, chitosan (CS), phosphoramide (Ph), CS/Ph, CS/Ag NPs films and the nanocomposite films 1–5 containing CS/Ph/1–5% Ag NPs were investigated. The in vitro antibacterial activities were evaluated against four bacteria including two Gram‐positive Staphylococcus aureus (S. aureus), Bacillus cereus (B. cereus) and two Gram‐negative Escherchia coli (E. coli), Pseudomonas aeruginosa (P. aeruginosa) bacteria. Results revealed greater antibacterial effects of the films against Gram‐positive bacteria. Also, nanocomposite films containing higher percent of Ag NPs showed more antibacterial activities. POLYM. COMPOS. 36:454–466, 2015. © 2014 Society of Plastics Engineers     

An Approach for Estimating Monthly Curve Number Based on Remotely-Sensed MODIS Leaf Area Index Products

Water Resources Management - Curve number (CN) is a principal factor which is widely used in hydrology, specifically in the rainfall-runoff modelling. Its value varies based on soil moisture...     

Towards novel deep neuroevolution models: chaotic levy grasshopper optimization for short-term wind speed forecasting

High accurate wind speed forecasting plays an important role in ensuring the sustainability of wind power utilization. Although deep neural networks (DNNs) have been recently applied to wind time-series datasets, their maximum performance largely leans on their designed architecture. By the current state-of-the-art DNNs, their architectures are mainly configured in manual way, which is a time-consuming task. Thus, it is difficult and frustrating for regular users who do not have comprehensive experience in DNNs to design their optimal architectures to forecast problems of interest. This paper proposes a novel framework to optimize the hyperparameters and architecture of DNNs used for wind speed forecasting. Thus, we introduce a novel enhanced version of the grasshopper optimization algorithm called EGOA to optimize the deep long short-term memory (LSTM) neural network architecture, which optimally evolves four of its key hyperparameters. For designing the enhanced version of GOA, the chaotic theory and levy flight strategies are applied to make an efficient balance between the exploitation and exploration phases of the GOA. Moreover, the mutual information (MI) feature selection algorithm is utilized to select more correlated and effective historical wind speed time series features. The proposed model’s performance is comprehensively evaluated on two datasets gathered from the wind stations located in the United States (US) for two forecasting horizons of the next 30-min and 1-h ahead. The experimental results reveal that the proposed model achieves the best forecasting performance compared to seven prominent classical and state-of-the-art forecasting algorithms.

     

Energy-aware cloud computing

Cloud computing, as a trending model for the information technology, provides unique features and opportunities including scalability, broad accessibility and dynamic provision of computing resources with limited capital investments. This paper presents the criteria, assets, and models for energy-aware cloud computing practices and envisions a market structure that addresses the impact of the quality and price of energy supply on the quality and cost of cloud computing services. Energy management practices for cloud providers at the macro and micro levels to improve the cost and reliability of cloud services are presented.     

Three-dimensional compressible-incompressible turbulent flow simulation using a pressure-based algorithm

In this work, we extend a finite-volume pressure-based incompressible algorithm to solve three-dimensional compressible and incompressible turbulent flow regimes. To achieve a hybrid algorithm capable of solving either compressible or incompressible flows, the mass flux components instead of the primitive velocity components are chosen as the primary dependent variables in a SIMPLE-based algorithm. This choice warrants to reduce the nonlinearities arose in treating the system of conservative equations. The use of a new Favre-averaging like technique plays a key role to render this benefit. The developed formulations indicate that there is less demand to interpolate the fluxes at the cell faces, which is definitely a merit. To impose the hyperbolic behavior in compressible flow regimes, we introduce an artificial hyperbolicity in pressure correction equation. We choose k-ω turbulence model and incorporate the compressibility effect as a correction. It is shown that the above considerations grant to achieve a robust algorithm with great capabilities in solving both flow regimes with a reasonable range of Mach number applications. To evaluate the ability of the new pressure-based algorithm, three test cases are targeted. They are incompressible backward-facing step problem, compressible flow over a wide range of open to closed cavities, and compressible turbulent flow in a square duct. The current results indicate that there are reliable agreements with those of experiments and other numerical solutions in the entire range of investigation.     

Effect of tectonics and earthquakes on geothermal activity near plate boundaries: A case study from South Iceland

We studied fracture-controlled geothermal fields in the Hreppar Rift-Jump Block (HRJB), a micro-plate bounded by two NNE rifts and the E–W transform zone of the South Iceland Seismic Zone (SISZ). Distinguishing whether the extensional rift swarm or the transform zone shear fractures host the geothermal activity is challenging. GPS mapping of 208 springs and tectonic analysis indicate that six Riedel shear fracture sets of an older transform zone in the HRJB are permeable. Northerly dextral strike-slip faults are the principal permeable faults, although the highest discharge and temperature are found at their intersections with other fracture sets. Two northerly faults from the HRJB connect to the source faults of the major 1784 and 1896 earthquakes within the active SISZ. The 1784 earthquake caused pressure changes as far north as the studied springs, indicating that earthquakes keep faults permeable over hundreds of years.