Bearing capacity of strip footings on sand slopes reinforced with geogrid and grid-anchor

This paper presents the effect of a new type of geogrid inclusion on the bearing capacity of a rigid strip footing constructed on a sand slope. A broad series of conditions, including unreinforced cases, was tested by varying parameters such as geogrid type, number of geogrid layers, vertical spacing and depth to topmost layer of geogrid. The results were then analyzed to find both qualitative and quantitative relationships between the bearing capacity and the geogrid parameters. A series of finite element analyses was additionally carried out on a prototype slope and the results were compared with the findings from the laboratory model tests and to complete the results of the model tests. The results show that the bearing capacity of rigid strip footings on sloping ground can be intensively increased by the inclusion of grid-anchor layers in the ground, and that the magnitude of bearing capacity increase depends greatly on the geogrid distribution. It is also shown that the load-settlement behavior and bearing capacity of the rigid footing can be considerably improved by the inclusion of a reinforcing layer at the appropriate location in the fill slope. The agreement between observed and computed results is found to be reasonably good in terms of load-settlement behavior and optimum parameters.     

Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoirs

Dew point pressure is one of the most critical quantities for characterizing a gas condensate reservoir. So, accurate determination of this property has been the main challenge in reservoir development and management. The experimental determination of dew point pressure in PVT cell is often difficult especially in case of lean retrograde gas condensate. Empirical correlations and some equations of state can be used to calculate reservoir fluid properties. Empirical correlations do not have ability to reliable duplicate the temperature behavior of constant composition fluids. Equations of state have convergence problem and need to be tuned against some experimental data. Complexity, non-linearity and vagueness are some reservoir parameter characteristic which can be propagated simply by intelligent system. With the advantage of fuzzy sets in knowledge representation and the high capacity of neural nets (NNs) in learning knowledge expressed in data, in this paper a neural fuzzy system(NFS) is proposed to predict dew point pressure of gas condensate reservoir. The model was developed using 110 measurements of dew point pressure. The performance of the model is compared against performance of some of the most accurate and general correlations for dew point pressure calculation. From the results of this study, it can be pointed out that this novel method is more accurate and reliable with the mean square error of 0.058%, 0.074% and 0.044% for training, validation and test processes, respectively.     

Photocatalytic Reforming of Glycerol over Gold and Palladium as an Alternative Fuel Source

The photocatalytic reforming of glycerol to hydrogen over palladium and gold modified TiO₂ catalysts is reported. The rate of hydrogen production exceeds that measured for the photoreforming of methanol over both catalysts with the palladium catalyst performing approximately four times better than the gold. The reaction occurs under ambient conditions and neither system suffered from poisoning after extensive testing. This therefore offers a potentially attractive route by which one of the major waste products from the manufacture of biodiesel can be converted into a useful product.     

Martensitic Transformation Behavior of B‐Bearing Steel During Isothermal Deformation

The purpose of the present research is to study the martensitic transformation in 22MnB5 steel under thermomechanical conditions by means of dilatation data. To reach this aim, the effects of deformation temperature and strain rate on the martensitic dilatation as well as martensite start temperature (M_s) were investigated. Thermomechanical treatments were performed in a deformation dilatometer including the isothermal deformation of samples in the temperature range of 550–900°C up to the final strain of 0.5 in three strain rates of 0.1, 1, and 10 s^?1. Finally, deformation temperatures were divided into two regimes of lower and higher than 800°C. In the former, strain‐induced phase transformations, while in the latter, occurrence of dynamic recovery against mechanical stabilization of austenite influenced martensitic transformation.     

Free vibration analysis of functionally graded coupled circular plate with piezoelectric layers

Based on classical plate theory (CLPT), free vibration analysis of a circular plate composed of functionally graded material (FGM) with its upper and lower surfaces bounded by two piezoelectric layers was performed. Assuming that the material properties vary in a power law manner within the thickness of the plate the governing differential equations are derived. The distribution of electric potential along the thickness direction in piezoelectric layers is considered to vary quadratically such that the Maxwell static electricity equation is satisfied. Then these equations are solved analytically for two different boundary conditions, namely clamped and simply supported edges. The validity of our analytical solution was checked by comparing the obtained resonant frequencies with those of an isotropic host plate. Furthermore, for both FGM plate and FGM plate with piezoelectric layers, natural frequencies were obtained by finite element method. Very good agreement was observed between the results of finite element method and the method presented in this paper. Then for the two aforementioned types of boundary conditions, the values of power index were changed and its effect on the resonant frequencies was studied. Also, the effect of piezoelectric thickness layers on the natural frequencies of FGM piezoelectric plate was investigated. This paper was recommended for publication in revised form by Associate Editor Seockhyun Kim Saeed Jafari Mehrabadi received his B.S. in mechanical Engineering from Azad University, Arak, Iran, in 1992. He then received his M.S. from Azad University, Tehran, Iran in 1995. Now he is a faculty member of the department of mechanical engineering in Azad university of Arak, Iran and PhD student of Azad University, Science and Research Campus, Pounak, Tehran, Iran. His interests include computational methods and solid mechanics such as vibration, buckling.     

Enhancement of Thermoelectric Behavior of La0.5Co4Sb12−x Te x Skutterudite Materials

Metallurgical and Materials Transactions A - In this work, the effects of Te doping on the microstructure and thermoelectric properties of the partially filled skutterudite La0.5Co4Sb12 compounds...     

Recovery prediction of copper oxide ore column leaching by hybrid neural genetic algorithm

The artificial neural network (ANN) and hybrid of artificial neural network and genetic algorithm (GANN) were applied to predict the optimized conditions of column leaching of copper oxide ore with relations of input and output data. The leaching experiments were performed in three columns with the heights of 2, 4 and 6 m and in particle size of <25.4 and <50.8 mm. The effects of different operating parameters such as column height, particle size, acid flow rate and leaching time were studied to optimize the conditions to achieve the maximum recovery of copper using column leaching in pilot scale. It was found that the recovery increased with increasing the acid flow rate and leaching time and decreasing particle size and column height. The efficiency of GANN and ANN algorithms was compared with each other. The results showed that GANN is more efficient than ANN in predicting copper recovery. The proposed model can be used to predict the Cu recovery with a reasonable error.