Polarizability,Susceptibility, and Dielectric Constant of Nanometer‐Scale Molecular Films: A Microscopic View

The size‐dependence of the polarizability, susceptibility, and dielectric constant of nanometer‐scale molecular layers is explored theoretically. First‐principles calculations based on density functional theory are compared to phenomenological modeling based on polarizable dipolar arrays for a model system of organized monolayers composed of oligophenyl chains. Size trends for all three quantities are primarily governed by a competition between out‐of‐plane polarization enhancement and in‐plane polarization suppression. Molecular packing density is the single most important factor controlling this competition and it strongly affects the bulk limit of the dielectric constant as well as the rate at which it is approached. Finally, the polarization does not reach its “bulk” limit, as determined from the Clausius–Mossotti model, but the susceptibility and dielectric constant do converge to the correct bulk limit. However, whereas the Clausius–Mossotti model describes the dielectric constant well at low lateral densities, finite size effects of the monomer units cause it to be increasingly inaccurate at high lateral densities.     

Sharing spray and wait routing algorithm in opportunistic networks

Due to the instability and intermittent connectivity of links among the nodes and the lack of connectivity in opportunistic network, it is not feasible to use common routing for delivering messages. The only practical method for routing and delivering messages is to use the store-carry-forward routing method. As a case in point, spray and wait is considered to be one of the most appropriate routing methods. The efficiency of this method depends directly on the proper selection of the next hop and the number of copies when it encounters a node. In this paper, a method was proposed that constantly selects the next node and considers the number of copies a node can deliver. In the proposed method, the selection of the next node and the number of message copies to be transmitted by the next hop are based on message carrying time and the probability of message delivery. The network model, based on Markov chain, is extended for analysis. Simulation and analysis results showed that significant enhancement is obtained with the proposed method when measuring metrics such as delay, delivery ratio and copy do comparisons with similar methods.     

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.     

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.     

A quantitative approach to study solid state phase coarsening in solder alloys using combined phase-field modeling and experimental observation

In the present work, a quantitative phase-field approach is introduced to study the phase-coarsening phenomena in solder alloys, Pb-Sn alloy here. The most important part of this work is to introduce a simple and versatile approach to quantify the experimental and simulation data, without putting into difficulties corresponding to the stochastic nature of phenomenon, in order to compute unknown physical data required to perform numerical simulation. For this purpose, at first, the evolution of microstructure vs. time is studied experimentally by the conventional optical microscopy. Then, unknown physical data, the interface mobility here, is computed by fitting the time evolution of the total interface perimeter of the simulation results to that of the experimental data. In fact, by this approach, the physical data is computed such that it will be applied to predict reality in the subsequent simulations, i.e., the presented method can be accounted as the calibration of the corresponding mathematical model and numerical method. The validity of the presented approach is supported by comparing simulation data to experimental ones.     

TQM consultant selection in SMEs with TOPSIS under fuzzy environment

Although much has been written about TQM in SMEs, little attention has been paid to the role of external consultant in the successful implementation of a TQM program in this kind of firms. Sometimes there are some candidates and companies have to select the best one. In general, many factors affect this problem which adheres to uncertain and imprecise data, and usually several people from different functional areas of the company are involved in this process. This study aims to improve the quality of decision in this area. In this paper a systematic decision process for selecting external consultant is proposed. The proposed method is based on TOPSIS method in fuzzy environment. Decision criteria are obtained from the nominal group technique (NGT). Additionally, a real case study is presented to illustrate the application of the proposed method.