Application of combined analytical/FEA coupled aero-structure simulation in design of wind turbine adaptive blades

This paper demonstrates the application of combined analytical/FEA coupled aero-structure simulation in design of bend-twist adaptive blades. A genetic algorithm based design tool, in which the power curve is predicted through a combined coupled aero-structure simulation, has been developed. A bend-twist adaptive blade has been designed to be used on the rotor of a constant speed stall regulated wind turbine. The bend-twist adaptive blade is assumed to be made out of anisotropic composite materials. The designed blade has the same aerofoil and chord distribution as the original blade used on the wind turbine, but with a different pre-twist distribution. The simulated results show a significant improvement in the average power of the studied stall regulated wind turbine when employing the designed adaptive blades.     

The ANFIS-PSO strategy as a novel method to predict interfacial tension of hydrocarbons and brine

The interfacial tension of hydrocarbons and brine is known as one of the important parameters which are measured in petroleum and petrochemical industries for example the interfacial tension has straight effect on trapping of oil in a reservoir. In the present work the Adaptive neuro-fuzzy inference system (ANFIS) algorithm was used as a novel approach for estimation of interfacial tension between hydrocarbons and brine as function of pressure, temperature, carbon number of hydrocarbon and ionic strength of brine then the particle swarm optimization (PSO) was used to optimize the predicting model parameters.in order to better evaluation of performance of predicting algorithm the coefficient of determination (R²), average absolute relative deviation (AARD) and root mean squared error (RMSE) were estimated for different steps. The outcomes of this investigation expressed that proposed model has high potential for prediction of interfacial tension between hydrocarbons and brine.     

Scale-up studies for stable,long-term indoor and outdoor production of hydrogen by immobilized Rhodobacter capsulatus

Photofermentative hydrogen production by immobilized Rhodobacter capsulatus YO3 was carried out in a novel photobioreactor in sequential batch mode under indoor and outdoor conditions. Long-term H₂ production was realized in a 1.4 L photobioreactor for 64 days using Rhodobacter capsulatus YO3 immobilized with 4% (w/v) agar on 5 mM sucrose and 4 mM glutamate. The highest hydrogen yield (19 mol H₂/mol sucrose) and hydrogen productivity (0.73 mmol H₂ L^?1 h^?1) were achieved indoors on 5 mM sucrose. The effect of initial sucrose concentration (5 mM, 10 mM, and 20 mM) on hydrogen production was also investigated. Sustained hydrogen production was carried out under natural, outdoor conditions as well. For the outdoor experiments, the highest hydrogen productivity and yield were obtained as 0.87 ± 0.06 mmol H₂ L^?1 h^?1 and 6.1 ± 0.2 mol H₂/mol sucrose, respectively on 10 mM sucrose. Furthermore, this system prevented sudden pH drops and fluctuations caused by the utilization of sucrose throughout the process. These results demonstrate that a proper immobilization setup can lead to long-term efficient and robust hydrogen production even under naturally varying conditions.     

Vapour treatments with methyl salicylate or methyl jasmonate alleviated chilling injury and enhanced antioxidant potential during postharvest storage of pomegranates

Pomegranates were treated after harvest with methyl jasmonate (MeJa) or methyl salicylate (MeSa) at two concentrations (0.01 and 0.1 mM), and then stored under chilling temperature for 84 days. Control fruits exhibited chilling injury (CI) symptoms manifested by pitting and browning, the severity being enhanced as storage time advanced, and accompanied by softening and electrolyte leakage (EL). The CI symptoms were significantly reduced by MeJa or MeSa treatments, without significant differences among treatments or applied dose. In addition, both treatments significantly increased total phenolics and anthocyanins with respect to controls. Hydrophilic (H-TAA) and lipophilic (L-TAA) total antioxidant activity decreased in control arils, but in both MeJa and MeSa treated fruits H-TAA increased while no significant changes occurred for L-TAA. Results would suggest that both MeJa and MeSa have potential postharvest applications in reducing CI, maintaining quality and improving the health benefits of pomegranate fruit consumption by increasing the antioxidant capacity.     

Micro‐modeling of porous composite anodes for solid oxide fuel cells

A new anode micromodel for solid oxide fuel cells to predict the electrochemical performance of hydrocarbon‐fuelled porous composite anodes with various microstructures is developed. In this model, the random packing sphere method is used to estimate the anode microstructural properties, and the complex interdependency among the multicomponent mass transport, electron and ion transports, and electrochemical and chemical reactions is taken into account. As a case study, a porous Ni–YSZ composite anode operated with biogas fuel is simulated numerically and distributions of the current density, polarization, and mole fraction and rate of flux of the fuel components along the thickness of the anode are determined. The effect of the anode microstructural variables including the porosity, thickness, particle‐size ratio, and particle size and volume fraction of Ni particles on the anode electrochemical performance is also studied. © 2011 American Institute of Chemical Engineers AIChE J, 58: 1893–1906, 2012     

Automated Quality Characterization for Composites Using Hybrid Ultrasonic Imaging Techniques

An enhanced technique using image processing has been developed for automated ultrasonic inspection of composite materials, such as glass/carbon-fibre-reinforced polymer (GFRP or CFRP), to ascertain their structural healthiness. The proposed technique is capable of identifying the abnormality features buried in the composite by image filtering and segmentation applied to ultrasonic C-Scan images. This work presents results performed on two composite samples with simulated delamination defects. A local gating scheme is applied to raw A-Scan data for improved contrast between defective and healthy regions in the produced C-Scan image. In this test campaign, different filtering and thresholding algorithms are evaluated and compared in terms of their effectiveness on defect identification. The accuracies of less than 3 mm and 1.11 mm were attained for the defect size and depth, respectively. The results demonstrates the applicability of the proposed technique for accurate defect localization and characterization of composite materials.     

Size-dependent nonlinear vibration of an electrostatic nanobeam actuator considering surface effects and inter-molecular interactions

In this paper, the size-dependent nonlinear vibration of an electrostatic nanobeam actuator is investigated based on the nonlocal strain gradient theory, incorporating surface effects. A comprehensive model regarding the von Karman geometrical nonlinearity, inter-molecular forces and both components of the electrostatic excitation (AC and DC) is proposed to explore the system behavior near the primary resonance. Utilizing Hamilton’s principle, the nonlinear equation of motion of the system is derived. The natural frequency and dynamic response of the system, comprising frequency and force response diagrams, are obtained analytically via multiple scales technique in conjunction with the differential quadrature method and validated through a numerical approach. The roles of the nonlocal and strain gradient parameters, surface elasticity, inter-molecular forces and quality factor on the system oscillations are examined. The acquired results unveiled that the size-dependent parameters can significantly displace the multi-valued portions and instability thresholds of the dynamical response. Furthermore, it is deduced that the surface effects induce the stiffness hardening of the nanobeam, whereas the inter-molecular forces impose the stiffness softening effect.

     

Symmetric deformable image registration via optimization of information theoretic measures

The use of information theoretic measures (ITMs) has been steadily growing in image processing, bioinformatics, and pattern classification. Although the ITMs have been extensively used in rigid and affine registration of multi-modal images, their computation and accuracy are critical issues in deformable image registration. Three important aspects of using ITMs in multi-modal deformable image registration are considered in this paper: computation, inverse consistency, and accuracy; a symmetric formulation of the deformable image registration problem through the computation of derivatives and resampling on both source and target images, and sufficient criteria for inverse consistency are presented for the purpose of achieving more accurate registration. The techniques of estimating ITMs are examined and analytical derivatives are derived for carrying out the optimization in a computationally efficient manner. ITMs based on Shannon’s and Renyi’s definitions are considered and compared. The obtained evaluation results via registration functions, and controlled deformable registration of multi-modal digital brain phantom and in vivo magnetic resonance brain images show the improved accuracy and efficiency of the developed formulation. The results also indicate that despite the recent favorable studies towards the use of ITMs based on Renyi’s definitions, these measures are seen not to provide improvements in this type of deformable registration as compared to ITMs based on Shannon’s definitions.     

A new feature selection from lidar data and digital aerial images acquired for an urban/c environment using an ANFIS-based classification and a fuzzy rough set method

In this article, a new fuzzy rough set (FRS) method was proposed for extracting rules from an adaptive neuro-fuzzy inference system (ANFIS)-based classification procedure in order to select the optimum features. The proposed methodology was used to classify lidar data and digital aerial images acquired for an urban environment to detect four classes, including trees, buildings, roads, and natural grounds. In this regard, 16 potentially primary features were produced for classification using the lidar data and the digital aerial images. The training and checking inputs of the proposed ANFIS were collected from the generated features for further training and evaluation processes. Also, the fuzzy c-mean clustering algorithm was used to initialize the fuzzy inference system of the proposed ANFIS-based classification method. By considering all states of fuzzy rules for each training input, the fuzzy rule with the maximum firing value was selected. Accordingly, these fuzzy rules were used as the inputs of the Rough Set Theory. Accordingly, the optimum features were acquired by the basic minimal covering algorithm as the rule induction method. To validate our proposed methodology, the procedure of classification was repeated by the achieved optimum features. The results showed that the classification using the optimum features has reached better overall accuracy than those achieved by using the 16 potentially primary features. Also, comparing the results of our proposed methodology with the other well-known genetic-algorithm-based feature selection methods indicated the significance of the proposed FRS method to select optimum features with high accuracy in a short running time.