New insight into prediction of phase behavior of natural gas hydrate by different cubic equations of state coupled with various mixing rules

Progress in hydrate thermodynamic study necessitates robust and fast models to be incorporated in reservoir simulation softwares. However, numerous models presented in the literature makes selection of the best,proper predictive model a cumbersome task. It is of industrial interest to make use of cubic equations of state(EOS) for modeling hydrate equilibria. In this regard, this study focuses on evaluation of three common EOSs including Peng–Robinson, Soave–Redlich–Kwong and Valderrama–Patel–Teja coupled with van der Waals and Platteeuw theory to predict hydrate P–T equilibrium of a real natural gas sample. Each EOS was accompanied with three mixing rules, including van der Waals(vd W),Avlonitis non-density dependent(ANDD) and general nonquadratic(GNQ). The prediction of cubic EOSs was in sufficient agreement with experimental data and with overall AARD% of less than unity. In addition, PR plus ANDD proved to be the most accurate model in this study for prediction of hydrate equilibria with AARD% of 0.166.It was observed that the accuracy of cubic EOSs studied in this paper depends on mixing rule coupled with them,especially at high-pressure conditions. Lastly, the present study does not include any adjustable parameter to be correlated with hydrate phase equilibrium data.     

Prediction of asphaltene precipitation during gas injection

Maintaining the flow of multiphase fluid from the reservoir to the surface has been an important issue with wide economic importance for the petroleum industry. Asphaltene precipitation due to change in temperature, pressure, and composition of oil can adversely affect the oil flow to the surface by reducing the available diameter of the tubing. In this study, the precipitation of asphaltene from an Iranian crude oil was investigated. To do our study, through information about asphaltene instability in the live oil during both natural depletion and gas injection conditions about oil sample from Iranian oil field was gathered. Then, the solid model and scaling model were utilized to predict the weight percent of precipitated asphaltene at a wide range of the pressure and temperature. Results of the work revealed that both models predict the increase in weight percent of precipitated asphaltene when lean gas injected to the live oil at the maximum point of asphaltene instability. In addition, the study showed that both models are capable of predicting the experimental data of asphaltene precipitation; while scaling modeling is more reliable when the gas is injected to the oil.     

Thermal and rheological properties of nanoparticle modified asphalt binder at low and intermediate temperature range

There is a paucity of knowledge regarding the effect of nanoparticles on the performance of asphalt binder against fatigue and low-temperature cracking. In this research, asphalt binders were modified using SiO₂, TiO₂, and CaCO₃ nanoparticles, and rheological and thermal properties of the modified binders were investigated. Differential scanning calorimetry was used to determine glass transition temperature, and rheological properties at low and intermediate temperatures were determined using bending beam and dynamic shear rheometers, respectively. The results suggested that the addition of these nanoparticles increases glass transition temperature and the low-temperature stiffness of asphalt binder. Furthermore, increase in complex shear modulus and decrease in phase angle values were observed at intermediate temperatures. It can be concluded that inferior performance at low and intermediate temperature is expected by the addition of nanoparticles to asphalt binder.     

Connectivity properties of large-scale sensor networks

In wireless sensor networks, both nodes and links are prone to failures. In this paper we study connectivity properties of large-scale wireless sensor networks and discuss their implicit effect on routing algorithms and network reliability. We assume a network model of n sensors which are distributed randomly over a field based on a given distribution function. The sensors may be unreliable with a probability distribution, which possibly depends on n and the location of sensors. Two active sensor nodes are connected with probability p _e (n) if they are within communication range of each other. We prove a general result relating unreliable sensor networks to reliable networks. We investigate different graph theoretic properties of sensor networks such as k-connectivity and the existence of the giant component. While connectivity (i.e. k = 1) insures that all nodes can communicate with each other, k-connectivity for k > 1 is required for multi-path routing. We analyze the average shortest path of the k paths from a node in the sensing field back to a base station. It is found that the lengths of these multiple paths in a k-connected network are all close to the shortest path. These results are shown through graph theoretical derivations and are also verified through simulations.     

CEDAR: Modeling impact of component error derating and read frequency on system-level vulnerability in high-performance processors

Reliability of the current microprocessor technology is seriously challenged by radiation-induced soft errors. Accurate Vulnerability Factor (VF) modeling of system components is crucial in designing cost-effective protection schemes in high-performance processors. Although Statistical Fault Injection (SFI) techniques can be used to provide relatively accurate VF estimations, they are often very time-consuming. Unlike SFI techniques, recently proposed analytical models can be used to compute VF in a timely fashion. However, VFs computed by such models are inaccurate as the system-level impact of soft errors is overlooked.     

RTDGPS Accuracy Improvement Using PSO-LSWSVM and Low-Cost GPS Receivers

Wireless Personal Communications - The position of mobile devices is determined by Real Time Differential Global Positioning System (RTDGPS). This system is composed of fixed and mobile station....     

Content‐Based Image Retrieval Based on Relevance Feedback and Reinforcement Learning for Medical Images

To enable a relevance feedback paradigm to evolve itself by users’ feedback, a reinforcement learning method is proposed. The feature space of the medical images is partitioned into positive and negative hypercubes by the system. Each hypercube constitutes an individual in a genetic algorithm infrastructure. The rules take recombination and mutation operators to make new rules for better exploring the feature space. The effectiveness of the rules is checked by a scoring method by which the ineffective rules will be omitted gradually and the effective ones survive. Our experiments on a set of 10,004 images from the IRMA database show that the proposed approach can better describe the semantic content of images for image retrieval with respect to other existing approaches in the literature.     

Gain improvement and microwave operation of 4H–SiC MESFET with a new recessed metal ring structure

A new multi-recessed 4H-SiC MESFET with recessed metal ring for RF embedded circuits is proposed (MR2-MESFET). The key idea in the proposed structure is based on the elimination of the spaces adjacent to gate and stopped the depletion region extending towards drain and source and the reduction of the channel thickness between gate and drain to increase breakdown voltage (V_BR); meanwhile the elimination of the gate depletion layer extension to source/drain to decrease gate-source capacitance (C_gs). The influence of multi-recessed drift region and recessed metal ring structures on the characteristics of the MR2-MESFET is studied by numerical simulation. The optimized results show that the V_BR of the MR2-MESFET is 119% larger than that of the conventional 4H–SiC MESFET (C-MESFET); meanwhile maintain 85% higher saturation drain current. Therefore, the maximum output power density of the MR2-MESFET is 23.1 W/mm compared to 5.5 W/mm of the C-MESFET. Also, the cut-off frequency (f_T) and the maximum oscillation frequency (f_max) of 24.9 and 91.7 GHz are obtained for the MR2-MESFET compared to 11 and 40 GHz of the C-MESFET structure, respectively. The proposed MR2-MESFET shows a maximum stable gain (MSG) exceeding 23.6 dB at 3.1 GHz which is the highest gain yet reported for SiC MESFETs, showing the potential of this device for high power RF applications.     

High-Performance Mixed-Mode Universal Min-Max Circuits for Nanotechnology

In this paper a low-power, high-speed and high-resolution voltage-mode Min-Max circuit, as well as a new efficient universal structure for determining the minimum and maximum values of the input digital signals, is proposed for nanotechnology. In addition, the proposed designs provide rail-to-rail input and output signals which enhance the performance and the robustness of the circuits. The advantage of the proposed Min-Max circuit is that it is extendable for any arbitrary n-digit and radix-r input numbers. Comprehensive simulation results at CMOS and CNFET technologies demonstrate the low-power and high-performance operation as well as insusceptibility to PVT variations of the proposed structure.